**Model Simplification on Energy and Comfort Simulation Analysis for Residential Building Design in Hot and Arid Climate**

#### **Sara Elhadad 1,2,3,\* , Chro Hama Radha 4, István Kistelegdi 1,5, Bálint Baranyai 1,5 and János Gyergyák <sup>6</sup>**


Received: 14 March 2020; Accepted: 10 April 2020; Published: 12 April 2020

**Abstract:** Accurate building physics performance analysis requires time-consuming, detailed modeling, and calculation time requirement. This paper evaluates the impact of model simplifications on thermal and visual comfort as well as energy performance. In the framework of dynamic zonal thermal simulation, a case study of a residential building in hot climate is investigated. A detailed model is created and simplified through four scenarios, by incrementally reducing the number of thermal zones from modeling every space as a separate zone to modeling the building as a single zone. The differences of total energy and comfort performance in the detailed and simplified models are analyzed to evaluate the grade of the simplifications' accuracy. The results indicate that all simplification scenarios present a marginal average deviation in total energy demand and thermal comfort by less than 20%. Combining rooms with similar thermal features into a zone presents the optimal scenario, while the worst scenario is the single-zone model. Results showed that thermal zone merging as a simulation simplification method has its limitations as well, whereas a too intensive simplification can lead to undesired error rates. The method is well applicable in further early-stage design and development tasks, specifically in large-scale projects.

**Keywords:** Model simplifications; Thermal and visual comfort; Energy performance; IDA ICE; Residential building

#### **1. Introduction**

High consumption of energy is unavoidable at a global scale [1–6]. It measures the economic success of a given country. The operation of residential and commercial buildings attributes one third of the world's energy consumption [7]. Thus, there is great potential for decreasing global energy consumption through improving the building design [8]. All advanced countries concerned on building-energy problem in various ways to preserve the energy sources and to use energy in a rational way [9]. Based on the U.S. Department of Energy report, buildings are attributed to the

majority of total annual energy consumptions and greenhouse gas emissions by the range of 40% to 50% [10,11], and similar results are shown in Europe [12]. Thus, different supranational and national initiatives, regulations, and different programs of private sectors such as CASBEE, LEED, BEEAM, DGNB, and others identify the parameters and standards to assess buildings' sustainability level and to minimize energy use. The role of appliances and residents' behaviors of users should be taken into account in sustainable building design as this role is strictly connected to energy savings and indoor comfort [13,14]. Becherini et al. [15] suggested and modeled several scenarios through which occupant behavior and thermal coating can contribute to the thermal performance of the building. Proper implementation of the framework, materials, knowledge, and system from design stage to construction and operation stages is required to obtain efficient buildings. The "Integrated Building Design" approach [16] is one of the possible solutions to integrate all these elements in the building sector.

Building-energy simulation is an essential support tool to design and commission green buildings. Many available, validated building-energy simulation tools, as Energy Plus, IDA ICE, TRNSYS, BLAST, ESP-r, Radiance, DOE-2 and eQUEST promise high accuracy level and effectivity for comprehensive simulation of building designs [17,18], but they require detailed input for model analysis, composing of zero thickness partitions or walls between thermal zones [19]. The operation and input of building-energy simulation parameters are quite complex [20], including geometric modeling, division of thermal zones, software selection, and selection of meteorological data. Geometric modeling represents the first stage of simulation and often consumes about half of the time of the simulation procedure [21]. Thus, simplification of geometric modeling is considered to be one of the most crucial way to enhance the simulation process. Converting a detailed model back to the spatial model is a complex task for the user and represents some of unfortunate challenges [19]. Despite the proliferation of several building-energy analysis tools in recent years, architects still face difficulties to use the basic tools of energy analysis [22]. The outputs confirmed that the majority of energy simulation tools are not appropriate for the working needs and methods of architects [23–25]. Usually, simplifications occur during translating real building geometry into an energy simulation model due to the lack of modeler software, or model simplifications serve the reduction of computational effort and calculation time. Though some previous studies such as Liu and Henze [26], Westphal and Lamberts [27] and Capozzoli et al. [28] investigated the effects of simplifications on the energy analysis of buildings, it is often underestimated or neglected. Therefore, it is essential to develop a simplification methodology of building physics modeling tools to reduce time and costs of thermal and lighting building simulations, without adverse impact on the quality of results. Complex building geometries are often simplified to perform energy performance simulation [29]. Zhao et al [20] identified three common types of geometric model simplifications as follows:


Several studies have examined the effect of model simplification on the result accuracy. Amitrano et al. [30] investigated the effect of the level of detail on the accuracy of the energy simulation in office buildings. Their study concluded that more detailed geometry can enhance the reliability of simulation by 5 to 15%. Picco and Marengo [16] assessed the effect of different simplifications in building construction types, thermal zoning, and building obstructions, for instance. The findings showed that strong simplifications on the building geometries do not make significant change on the outputs, compared to the detailed model. Bosscha [31] applied a sensitivity analysis by varying the material properties, geometry, and heating, ventilations and air conditioning (HVAC) settings to compare the accuracy of the calculations with the detailed model. The results concluded that the increase in accuracy obtained by more detailed zoning and geometry is highly relying on the HVAC simulation type. Korolija and Zhang [32] compared the predicted annual energy use of the detailed model in which every room was modeled as a separate zone with a simplified model, in which each floor is was modeled as a single

zone. The output results showed that thermal zoning simplifications decreased the simulation time by 30% and the mean absolute error of annual heating demand was 10.6%. Klimczak et al. [33] explored the effect of model simplifications on the quality of energy simulation results of a residential building case. The simplifications consisted of the reduction of thermal zones and internal walls, removal of shading elements, and calculations were carried out in different iterations. The findings showed that the exclusion of the shading devices on the south façade had a considerable effect, thus, in future studies this simplification should not be applied. Heo [34] estimated the impacts of internal load, scheduling, and thermal zoning simplifications for domestic buildings in the United Kingdom. They concluded that the differences in annual heating demand are 26% and 17% in the simplification with one single zone for the entire dwelling and one thermal zone per floor, respectively. Dipasquale et al. [35] studied the impact of defining the physical and geometric characteristics of buildings, such as the presence of internal walls, thermal capacity, thermal bridges, the gross or net surfaces, and the number of zones during the modeling stage for heat load assessment. The findings of these results concluded that the reduction of the number of zones has the highest effect on the loads, almost 22% in the cooling demand and 12.5% in heating demand. Chatzivasileiadi et al. [19] explored the impact of simplifying the complex geometries through a systematic analysis of different test cases on the accuracy of energy performance simulation results. The results concluded that orthogonal prisms as simplified surrogates for buildings should be avoided where it is possible, as it showed the worst-case scenario. Akkurt et al. [36] concluded that the simplification of geometry is often unavoidable for use in building-energy performance simulation, but inaccuracies resulted from oversimplification in some geometrical characteristics must be avoided. Zhao et al. [21] investigated the appropriate level of geometric modeling simplification through thermal zone, typical floor and fenestration in energy analysis for office buildings and they found that the more accurate case is modeling the exterior wall in regarding to internal edge. Samuelson et al [37] assessed the accuracy of 18 design-phase building-energy models to enhance the simulation predictions compared to measured energy data.

Despite the valuable results of the aforementioned studies, they just evaluate the impact of model simplification on energy simulation in residential buildings or in office types. The impacts of modeling simplification on the thermal comfort analysis are usually not investigated properly. A study of Korolija and Zehan [32] analyzed the effect of modeling simplification on thermal comfort analysis, but with a different method and focus as they considered one simplification scenario of treating each floor by a single zone and they assessed the thermal comfort performance through annual operation of carbon emission and overheating risk. Consequently, it can be stated that there is no study about the effect of model simplifications on the thermal and visual comfort published yet.

Accurate energy and thermal comfort analysis of buildings requires a lot of time, especially in complex cases it may require up to several weeks. Minimizing the required time of analysis is necessary to be compatible with design duration. Therefore, the main aim of this paper is to assess the impact of model simplifications through different scenarios considering the simulation time, modeling time, and accuracy level of the derived results in both energy demand and thermal comfort in residential houses. The paper evaluates the impact of simplifications by comparing the simulation outputs of the detailed reference model and the simplified models with incremental reduction in the number of thermal zones, until the whole house is modeled as a single zone. Moreover, the investigations explore what level of simplified thermal zoning is required to support energy and thermal comfort analysis of residential buildings. The study is carried out in the simulation framework of IDA ICE, and it also identifies the optimal scenario of the proposed simplification scenarios.

#### **2. Model Simplification Methodology**

This study examines the impact of reducing the number of thermal zones on the prediction accuracy of energy and comfort of residential buildings. A thermal zone represents the division of a dwelling for the convenient calculation of the energy and thermal comfort simulation of the building. The thermal properties and parameters are relatively consistent in the same thermal zone. Obviously,

to get more accurate results of energy and thermal comfort, the simulation model should be more accurate regarding the number of modeled thermal zones of the building, but at the same time it would need more calculation time and, as a result, modeling work expenses. Many countries have provided relevant regulations for the division of thermal zones of the buildings. American National Standards Institute / American Society of Heating, Refrigerating and Air-Conditioning Engineers (ANSI/ASHRAE) 90.1 [38] reported that multiple spaces can be represented as one thermal zone with the following requirements: the usage of the spaces, the air conditioning and heating systems applied in the spaces and the orientation of the exterior walls and windows should be the same. The Building Research Establishment Ltd. [39] stated that a thermal zone is an area that has the same set points for cooling and heating, identic operating times of the plant, the same ventilation provisions and set-back conditions. In addition, they should be served by the same primary plant and terminal device type. The Canadian standard EE4 [40] stipulates that a thermal zone must have the following features: (1) same air conditioning system and heating with similar operations and functions, and similar heating and cooling loads; (2) the surrounding and the internal space should be distributed into different thermal zones; (3) rooms for laundry, equipment, power distribution, corridors, cloakrooms, and stairs cannot be modeled as a single partition.

For the purpose of the model simplifications, a multifamily house as a reference is proposed, representing a generic, typical residential building type in the largest building sector of the world. This reference building model is derived form an existing, common residential house, built in 2005 in New Minia, Egypt at 30.73 E longitude, 28.08 N latitude (Figure 1). The building consists of nine apartments. The ground floor is represented by one apartment and consists of a lounge, dining room, bathroom, and kitchen, with the total floor area of 180 m2. Each floor of the repeated floors consists of two identical apartments, with 220 m2 net floor area. Every apartment includes reception, master bedroom, two children rooms, bathroom, and kitchen as shown in Figure 1 and occupied by a couple with two children based on the real evaluation from the field. The composition of building elements was used on the basis of the Egyptian standards, as shown in Table 1. IDA ICE has been used to simulate thermal and visual comfort as well as energy performance in a detailed model about the reference building and in several simplification scenarios, whereas the reference model is modified according to the simplification concepts. Table 1 presents an overview of the major parameters and input data.

**Figure 1.** Generic residential building as a reference for model simplification tests.


#### **Table 1.** Boundary conditions for the simulation.

In the following, four different simplification scenarios of the thermal zones are proposed as shown in Figure 2. Summary of the simulated scenarios is presented in Table 2. First, in the base scenario (BS) model, each space is modeled as a single independent zone (Figure 1). Then, scenario S1 combines spaces with similar characteristics (e.g., orientation, operation schedules, same use, etc.) into one thermal zone (Figure 2). Then, scenario S2 combines the same oriented spaces for all of the 4 floors

into one thermal zone (Figure 2). In scenario S3, all spaces on the same floor are merged into one single zone, and scenario S4 models the entire building as one single thermal zone (Figure 2).

**Figure 2.** Simplification scenarios—simulation models (plan, side, and 3D view).



#### **3. Results and Discussion**

#### *3.1. Building-Energy Assessment*

IDA ICE has been used to simulate energy consumption and indoor comfort performance of the studied building for the BS model and all the simplification scenarios. Figure 3 summarizes the energy results for the BS model and the simplification scenarios in comparison to BS model. The simplification scenarios have minor effect on the lighting, facility, equipment, tenant, and DHW results due to their similar input parameter and cumulated settings. On the other hand, electric cooling and heating show larger differences. In BS scenario, the cooling demand accounts to 67% of the total energy consumption, while the heating demand attributes to 18%, as the case study located in a hot and dry climate. Lighting, facility, equipment, tenant and DHW accounted to 15% of the total energy consumption. In S1, the cooling demand increased by 9.6% and the heating demand decreased by 3.1% with respect to BS (Table A1). S2 and S3 scenarios performed an increased cooling demand by 15.1% and 10.6% respectively, while the heating demand decreased by 3.5% and 0.3%, respectively, compared to BS (Table A1). In scenario S4, the heating demand decreased by 23.6% in respect to BS model, while the cooling demand increased by 12.2% compared to BS (Table A1). Similar reports of the simplification on the energy performance are available in the literature, e.g., Heo et al. [34] and Ren et al. [41] have reported that merging rooms with similar characteristics into one zone (scenario S1) and modeling a single zone for the entire building (scenario S4) underestimated the annual heating demand by 7% and 24%, respectively, in comparison to modeling every room as a separate zone (detail model) for domestic buildings in UK. Picco et al [17] have also reported that cooling and heating loads was underestimated by 9.29% and 8.12%, respectively for scenario S3 (Every floor was represented by one individual zone) compared to the detailed model in an office building built, located in Bolzano, Italy. Picco and Marengo [16] have reported similar finding of simplification on cooling and heating demands. They reported that when the number of thermal zones are reduced to one thermal zone per floor (scenario S3), the annual heating and cooling demand are underestimated by 0.86% and 6.25%, respectively. Consistent with the present result, Dipasquale et al. [35] have also reported that reducing the whole floor to one thermal zone underestimated the annual heating and cooling demand by 12.5% and 22%, respectively with respect to the detailed model. Korolija, and Zhang [32] have also reported that treating each floor of a house as a single thermal zone underestimated the annual heating demand by 10.6%. The change in the total energy consumption evolved in the first, second, third, and the fourth scenarios as follows, +5.8%, +9.5%, +7.1%, +4.0% in respect to the BS model (Table A1). Although the fourth scenario represented the worst scenario considering only the cooling and heating demand individually, it had the smallest change in total energy consumption compared to BS model, because the heating and cooling deviations equaled each other out, resulting in the least difference in total. The thermal envelope is the same in all of the models, hence the fundamental differences can be derived from the complexity level of the actual modeled thermal mass, (walls, slabs) that affect mostly the cooling and heating demand, although the geometrically "missing" thermal mass was added to the model variations as individual mass elements respectively. Case S4's lowest heating demand is caused by the least floor space to be heated.

**Figure 3.** Delivered energy for the detailed and simplified models.

#### *3.2. Simulation Time and Modeling Time*

The total modeling time of the BS model was 215 min, while it decreased to 45, 35, 22, and 11 minutes in the simplification scenarios, as shown in Table 3. The most decisive difference in modeling expenditure of time takes place in modeling of one story of a building, since multifamily houses possess a great diversity of apartment sizes, room arrangements and room geometries. After completion of a floor, the typically identic domestic levels can be copied above each other to complete the building model; therefore, this modeling work duration is insignificant. As the number of thermal zones are reduced in a story, the simulation time decreases decisively. Considering the geometry and structure creation as well as the editing and parametrization working time, the required modeling time is approx. proportional—with a rate of 1:1—to the number of zones. At the same time, the total simulation time of the BS model was 86 minutes, and it decreased to 32, 14, 23, and 5 minutes in the scenarios. With a decreasing number of thermal zones, the simulation time decreases significantly. The scenarios saved 79 to 95% of the modeling time and 63 to 94% calculation duration compared to BS, demonstrating a huge potential in model simplification and workflow conservation.


**Table 3.** Modeling and calculation duration of the detailed and simplified models and respective differences.

#### *3.3. Assessment of Building Thermal Comfort*

#### 3.3.1. Evaluation of Predicted Mean Vote (PMV)

In this study, PMV was evaluated as one of the main indices to assess the thermal comfort in an occupied zone [42,43]. PMV refers to thermal 7-stage sensation scale [44] through seven points range from −3 to +3 as follow −3 = cold, −2 = cool, −1 = slightly cool, 0 = neutral, 1 = slightly warm, 2 = warm, and 3 = hot [45]. Three categories A, B, and C were proposed in ISO 7730, PMV is ranged in the interval of [−0.2, +0.2]; for Category A, in the interval [−0.5, +0.5] for Category B and, in the interval [−0.7, +0.7] for Category C [46]. Category B represents the normal level of applicability based on ISO 7730. Figure 4 shows the average number of annual hours of PMV, category B in the detailed

and simplified models' separated as well as merged thermal zones. In the simplified models, the average annual hours of PMV, category B is calculated by an area weighted averaging of the annual hours of PMV, category B for each thermal zone, as presented in Equation (1)

$$N\_{PMV} = \frac{\sum\_{i=1}^{i=n} N\_i \cdot A\_i}{\sum\_{i=1}^{i=n} A\_i} \tag{1}$$

where *NPMV*. means the average annual hours of PMV, category B for the whole model, *Ni* represents the number of annual hours of PMV, category B for thermal zone *i*, *Ai* the total area of each thermal zone [m2], "*n*" is the total number of thermal zones of the model. For the complete building in BS, the annual hours of PMV, category B were 7781 h, while 6642 were accounted for S1. The annual hours of PMV, category B increased by 6 hours for S2 and, while the annual hours decreased by 875 and 64 hours for S3 models and one-zone model (S4), respectively compared to the BS model, as shown in Table A2. In S2, the difference in the annual hours of PMV, category B increased by 3.2% in the south side and decreased by 29.3% in the north side, related to the BS model (Table A2). Reason for that: in the south oriented zone, solar gains enabled higher level of PMV, while in the north zone, the contrary effect evolved, because the high thermal zones (3-storey high) are more difficult to heat. In S3, the PMV decreased by 4.7% and 1.7% on the 2rd floor and the 3nd floor respectively, with respect to BS. Reason for that: in the 3rd floor the highest zone is the warmest in summer because of thermal gradient and less thermal mass. However, this greatest deviation is more still at a marginal scale, hence, in general, a consistent calculated thermal comfort sensation was observed in each model.

**Figure 4.** Average number of annual hours of PMV, Category B for whole and some parts of the building in the detailed.

#### 3.3.2. Carbon Dioxide Level Assessment

Concentration of Carbon Dioxide (CO2) was applied as an indicator of indoor air quality [47]. The connection between indoor air quality and indoor CO2 concentration originates from the fact that at the same time people are generating odor-causing bio effluents and producing CO2 [47]. In European Standard CEN-EN 13779:2007 [48], CO2 concentration is also applied to classify indoor air quality, and the maximum value of CO2 concentration level is 1500 ppm, while they recommend keeping CO2 concentration level below 1000 ppm. In this particular study, the number of annual hours is estimated,

when the CO2 concentration level is above 1000 ppm in the models. The results are compared at three scales (i.e., whole building, 2nd and 3rd floors, south and north sides of the building in all floors). Figure 5 presents the annual hours with CO2 concentration level above 1000 ppm in the detailed and simplification models. Additionally, an area weighting such as Equation (1) was used to calculate average annual hours of CO2 concentration level. Regarding to the complete building, the number of annual hours of CO2 level above 1000 ppm in BS scenario was 2248 h, while S1 was accounted to 2130 h. In the scenarios S2, S3, and S4 this value decreased by 7.2%, 8.4%, and 5.9%, respectively, compared to the BS scenario Table A3. Consistent with the present result, Korolija and Zhang [32] have also reported that treating each floor of a house as a single thermal zone (scenario S3) underestimated the carbon emission by 8%. In scenarios S1 and S3, the differences in the air quality were 0.1% and 21.7% respectively in the second floor, while 48.9% and 8.7%, differences occurred in third floor. In the south side of the whole building (S2 – building high thermal zone), the air hygiene decreased by 17.4% at the north side of the building with respect to the BS scenario while and the south side accounted to the same hours of BS scenario (Table A3). The merged, simplified zones have more space to be window-ventilated, since they include the corridors and secondary spaces (elevator/stairs) as well. That is why they perform higher CO2 level. Generally, the distribution of CO2 concentration shows great inhomogeneity in the different sized thermal zones.

**Figure 5.** Average number of annual hours with CO2 concentration > 1000 ppm for the whole and some parts of the building in the detailed and simplified models.

#### 3.3.3. Daylight Factor Assessment

Daylighting as visual comfort is an effective parameter in sustainable and energy efficient building design [49] and it is becoming an essential part of the environmentally friendly building design [50]. Adequate level of daylight is not only important to illuminate all year long and secondarily to heat in wintertime the interior, but it is also an essential source of the occupant's emotional and physiological well-being. Besides ensuring low level of odor and noise, daylight provision is an essential parameter in indoor environment investigations for maintaining the enjoyment of a property. Daylighting performance strongly relies on the illuminance under direct, respectively diffuse sky conditions.

Since the daylight provision under direct illuminance (clear sky conditions) in Minia region possesses high level of daylight autonomy in interior spaces, in this study, the visual comfort assessment focused on the Daylight Factor (DF), representing the illuminance performance of the spaces under mixed sky circumstances, as a kind of 'worst-case scenario'. Satisfying the minimum required DF limit means a whole year long secured daylighting quality. The DF value is a ratio that represents the amount of illuminance available indoors relative to the illuminance level present outdoors at the same time, under overcast sky [51]. DF at a point of the room is the ratio of the indoor illuminance Ei to the outdoor horizontal illuminance, Eo, [52], expressed as percentage in the following Equation (2):

$$\text{DF} = \frac{E\_i}{E\_o} \times 100 \,\text{[\%]} \tag{2}$$

Calculating Equation (2), the required value of DF for Minia city is 2.1, by applying the required Ei as 300 lx and Eo (median external diffuse illuminance) as 14012 lx according to EN17037 Daylight in Buildings and ASHRAE database. The DF was assessed in all models. Illuminances were computed using meteorological data taken from Meteonorm 7 database [53]. Figure 6 presents the ratio of floor area performing a DF above (corresponding to adequate daylight space partition) and below (equals to inadequate daylight space partition) the DF (2.1) threshold value. In case of BS, 21.3% the floor area is adequately daylight. In S1 and S3 the appropriately daylight floor area increased by 6.1% and 21.6% with respect to BS, while in S2 and S4 delivered significant, 19.8% and 60.3% differences compared to the reference. In S1 the abandonment of all internal walls caused the weaker DF performance and in S3 the additionally merged, deep spaces of the whole story thermal zones indicated the lower level of DF. The reason of the anomalies in S2 and S4 were the different height of the zones in the S2 and S4 models.

**Figure 6.** Floor area ratio with daylight factor above (red color) and below (blue color) the minimum DF (2.1%) value.

#### **4. Optimal Scenario of the Proposed Model Simplifications**

To determine the optimal scenario of the proposed simplifications, two crucial criteria should be taken into account: the required simulation time and the accuracy of the energy and comfort results. In respect to calculation duration, obviously the single-zone model (S4) represents the fastest model as shown in Table 3, followed by S2 model, S3 model, and S1 model. For the accuracy criteria, Table 4 presents the absolute differences of energy demand (heating and cooling) and indoor comfort (PMV, CO2 level and DF) in respect to the BS model. More simplification leads to more inaccurate results, as in S4 model the high differences in energy demand and DF distribution demonstrate. In comparison to BS, (S1) presented the optimal accuracy case of the proposed simplification scenarios, resulting in 6.8% average difference of all parameters in energy demand and comfort performance. At the same time, S1 saves over 63% of simulation time. S1 is followed by scenarios 3, 2, and 4. Consequently, the model simplification can be accomplished until the anomalies appear due to the simplified geometry.


**Table 4.** Absolute % differences of heating and cooling demand, PMV, CO2 concentration, and DF between the simplification scenarios and BS.

#### **5. Conclusions**

Buildings are attributed to a tremendous amount of energy consumption due to their continuous operation and extensive lifetime. Performing Building-energy simulations is an essential part of a decision-making process as it helps designers to assess the energy and comfort effect of different building design options. Since the impacts of building physics simulation model simplifications on the accuracy of the results are not well studied and reported, the proposed simplification scenarios seek to overcome the obstacle of long calculation time and according design costs by providing a simpler and faster way to carry out building-energy and comfort simulations. The main aspect of the methodology is to achieve an adequate level of accuracy that can promote the simulation results of energy demand and thermal comfort analysis by simultaneously minimizing calculation time. The detailed reference building physics simulation model contained all separate rooms modeled as individual thermal zones. The model was then simplified in scenario S1, whereas all spaces with similar use and orientation were merged into one-zone floor by floor. The same oriented spaces for all of the 4 floors were combined into one thermal zone in scenario S2. Every floor was represented by one individual zone in scenario S3, and the whole building was treated as one single zone for scenario S4. Multiple effects of the model simplification methods on energy consumption, CO2 level, PMV, and DF performance were evaluated in a common residential building in New Minia, Egypt.

A model simplification method that merges all spaces with similar use and orientation into one-zone floor by floor (scenario S1), enables the shortening of the required modeling time of 79% and the acceleration of the required solver calculation duration by 63%. At the same time, the comfort performance values possess 21.4% deviations, while the energy performance results are underestimated by 12.7% in comparison to the detailed model. Combining the same oriented spaces with the same use for all of the 4 floors into one thermal zone (scenarios S2) reduces the simulation time by 84%, while the deviation in total energy demand and thermal comfort are 18.6% and 27.4%, respectively, compared to the detailed model. When the number of thermal zones is further reduced to one thermal zone per floor (scenarios S3), the simulation time is saved by 73%, while the energy and thermal comfort are underestimated by 10.9% and 24.2%. However, modeling the entire building by a single zone (scenarios S4) saves 95% and 94% of the required modeling time and the simulation time, respectively, the energy and thermal comfort are underestimated by 35.8% and 63.3%, respectively. The interdependency of result accuracy and calculation time proved that the optimal simplification method merges all spaces with similar use and orientation into one-zone floor by floor (scenario S1). It is obvious that besides the advantages the geometrical simplifications might carry some limitations as well. Results showed that thermal zone merging as a simulation simplification method has its limitations as well, whereas a too intensive simplification can lead to undesired error rates. Furthermore, the essentially geometry related daylight distribution interpretation can be affected due to the different depth of the merged zones. In addition, the orientation should be considered with consciousness, since the different oriented zones

should not be combined to avoid different solar heat load (summer) or heat gain (winter) effect to be mixed in one greater unified zone to confuse both energy and comfort behavior.

Important to mention that taking only the energy results into consideration during the simplification process is not sufficient to get a truly comparable model version to the original detailed building model, rather it is inevitable to consider all determinate indoor comfort indices as well. Analysis of both comfort and energy results is the only way to identify the optimum on model simplification level. The gained thermal zoning simplification method can imply a high design feedback acceleration effect, offering a great potential for building design optimization. An until now unreached quality level of design optimization evolves, since testing of significantly higher number of design cases in the same amount of available planning time is getting to be possible. The thermal zone geometry simplification's result inaccuracy level should be further reduced by compensation solutions for thermal mass and the central, deeper settled zone sections, which distort to a certain measure the simulation results. The described methodology can help to reduce the duration requirements for a dynamic simulation and it can be seen as a 1st step in a multi-level model simplification strategy, consisting of next stages in simplifications techniques for fenestration, shading, thermal mass, HVAC systems, as well as controlling automation strategies. It can be concluded that the analysis results will be useful for modelers to determine the optimal level of model simplification in the modeling process depending on the achievable accuracy level of energy performance and thermal comfort. The method provided promising results for further applications and it is intended to be further tested in next multifamily projects and office buildings to prove its reliability in building industry standard practice.

**Author Contributions:** Conceptualization, S.E.; Data curation, S.E., B.B. and C.H.R.; Formal analysis, S.E.; Methodology, S.E. and B.B.; Project administration, I.K. and B.B.; Software, S.E. and B.B.; Supervision, I.K., B.B. and J.G.; Validation, S.E. and C.H.R.; Visualization, I.K., B.B. and C.H.R.; Writing – original draft, S.E.; Writing – review & editing, I.K., B.B., J.G. and C.H.R. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding

**Acknowledgments:** The present scientific contribution is dedicated to the 650th anniversary of the foundation of the University of Pécs, Hungary. The first author would like to thank the Egyptian Ministry of Higher Education (MoHE) and Tempus Public Foundation for providing him the Stipendium Hungaricum Scholarship. Further we appreciate the support of the János Szentágoghai Research Centre, University of Pécs, Energia Design Research Group, led by Prof Dr István Kistelegdi. The research project is conducted at the University of Pécs, Hungary, within the framework of the Biomedical Engineering Project of the Thematic Excellence Programme 2019 (TUDFO/51757-1/2019-ITM). The research project is conducted at the University of Pécs, Hungary, within the framework of the Biomedical Engineering Project of the Thematic Excellence Programme 2019 (TUDFO/51757-1/2019-ITM).

**Conflicts of Interest:** "The authors declare no conflict of interest."

#### **Appendix A**

**Table A1.** Delivered energy for detailed and simplified models and their differences with respect to detailed model.



**Table A2.** Average number of annual hours of PMV, Category B for whole and some parts of the building in the detailed and simplified models and differences of simplified models with respect to detailed model.


**Table A3.** Average number of annual hours with CO2 concentration > 1000 ppm for whole and some parts of the building in the detailed and simplified models and differences of simplified models with respect to detailed model.



#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*

## **Trombe Wall Thermal Behavior and Energy E**ffi**ciency of a Light Steel Frame Compartment: Experimental and Numerical Assessments**

#### **Victor Lohmann and Paulo Santos \***

ISISE, Department of Civil Engineering, University of Coimbra, Pólo II, Rua Luís Reis Santos, 3030-788 Coimbra, Portugal; valohmann@gmail.com **\*** Correspondence: pfsantos@dec.uc.pt; Tel.: +351-239-797-199

Received: 29 April 2020; Accepted: 27 May 2020; Published: 30 May 2020

**Abstract:** Buildings are seeking renewable energy sources (e.g., solar) and passive devices, such as Trombe walls. However, the thermal performance of Trombe walls depends on many factors. In this work, the thermal behavior and energy efficiency of a Trombe wall in a lightweight steel frame compartment were evaluated, making use of in situ measurements and numerical simulations. Measurements were performed inside two real scale experimental identical cubic modules, exposed to natural exterior weather conditions. Simulations were made using validated advanced dynamic models. The winter Trombe wall benefits were evaluated regarding indoor air temperature increase and heating energy reduction. Moreover, a thermal behavior parametric study was performed. Several comparisons were made: (1) Sunny and cloudy winter week thermal behavior; (2) Office and residential space use heating energy; (3) Two heating set-points (20 ◦C and 18 ◦C); (4) Thickness of the Trombe wall air cavity; (5) Thickness of the thermal storage wall; (6) Dimensions of the interior upper/lower vents; (7) Material of the thermal storage wall. It was found that a Trombe wall device could significantly improve the thermal behavior and reduce heating energy consumption. However, if not well designed and controlled (e.g., to mitigate nocturnal heat losses), the Trombe wall thermal and energy benefits could be insignificant and even disadvantageous.

**Keywords:** passive solar; Trombe wall; light steel frame; thermal behavior; energy efficiency; Mediterranean climate; office use; residential use; heating set-points

#### **1. Introduction**

Energy is one of the main concerns when addressing sustainable development, especially since the world's energy matrix is still very dependent on fossil fuels, as oil and coal. The building's sector plays an important role, as buildings consume approximately 40% of the total energy in Europe, being also responsible for about 36% of the CO2 emissions [1]. Aiming to improve the energy efficiency of buildings, the European Union (EU) has established the energy performance of buildings directive (EPBD) [2], in which two key concepts are defined: (1) the cost-optimal energy, regarding cost-efficiency of strategies [3], and (2) the nearly zero-energy buildings (nZEB)—buildings with very high energy efficiency—that cover their energy needs with energy produced by renewable sources, on-site or nearby [4]. To meet the EPBD requirements, the optimization of construction systems and the development of strategies to decrease energy consumption by buildings are key [5].

A sustainable strategy to improve the thermal and energy performance of buildings is exploiting solar energy, which also meets the EPDB establishments. A Trombe wall (TW) is a passive solar device that can be present in a building's façade to accumulate solar heat, heating, and even cooling indoor spaces, fostering natural ventilation [6]. This passive solar device was patented in 1881 by the

American engineer Edward Morse and popularized in the 1960s by the French engineer Felix Trombe and architect Jacque Michel, as mentioned by Saadatian et al. [7]. The classical configuration of Trombe walls is an outer glazed area to allow solar radiation to reach a massive storage wall, promoting the greenhouse effect. The storage wall usually has two interior vents (ventilated TW), connecting the indoor space to an air cavity between the wall and the glass panel—one at a lower height and other at an upper height [8]. To reduce heat losses through the TW device during cold winter nights, it is often used as an external night shutter [6]. Additionally, in warmer climates, exterior shading devices or overhangs are often used to mitigate overheating risk, as well as external upper and lower vents, promoting natural air-ventilation cooling effect during the summer season [9].

The operation of a Trombe wall is based on heat transfer principles. It absorbs solar heat in its high thermal mass storage wall during daytime and transfers part of this heat to the interior space of the building through conduction, radiation, and convection. The wall stores heat during the day and releases it during evening and night times, when the occupants require it and outdoor temperature decreases. The TW system, when exposed to direct solar radiation, exploits the greenhouse effect that occurs in the glazed air cavity, absorbing and storing heat in a massive wall. When the air cavity is warmed up by the heated storage wall, the air will flow upward due to buoyancy or thermosiphon effect. This heated air goes to the interior of the adjacent compartment through an upper vent, while colder air comes from the same room through a lower vent, re-entering to the TW air cavity [6].

Trombe walls have attracted attention over the last years, with different types studied, incorporating modern materials and construction methods, such as the incorporation of phase change materials [10] and photovoltaic cells on the glazed area [11].

Recently, Zhou et al. [12] studied the thermal performance of a composite Trombe wall under steady-state conditions. They compared three types of Trombe walls: traditional (TTW), water (WTW), and glass-water (GWTW). They optimized the thermal performance of the composite Trombe walls by defining two operating modes: (1) heat-collecting mode during the daytime, and (2) heat-preservation mode during night-time. The WTW exhibited the best efficiency during daytime (3.3% higher than the TTW) and also during night-time, allowing a heat loss reduction of 31% compared to TTW.

Besides space heating, researchers are also trying to develop new application advantages for Trombe walls. Hu et al. [13] made some experimental and numerical studies of a novel water blind-Trombe wall system. This new TW system, besides space heating and natural ventilation, could also provide domestic hot water since it made use of orientated steel blinds filled with flowing water and a hot water tank. They performed a comparison with conventional (i.e., without a glazing panel) and traditional TWs. A significant annual overall thermal load reduction was found compared to conventional (−42.6%) and traditional (−13%) Trombe walls. They also concluded that the new water blind-Trombe wall system, besides achieving a favorable insulation performance during winter, was also able to take advantage of the undesired solar radiation during the summer season to heat the water for domestic uses.

As mentioned before, Trombe walls could be very useful during the winter season to reduce space heating energy, but during the cooling season, this may have a negative impact due to limited control capability. Hong et al. [14] analyzed the thermal performance of a Trombe wall with an integrated Venetian blind during the cooling season. They evaluated the TW cooling mode operational control to regulate shading (from orientable Venetian blind slats within the TW air cavity) and natural ventilation (outside and cross). Several building occupation schedules were compared, i.e., service, office, and domestic buildings. It was found that the studied Venetian blind integrated TW could effectively prevent overheating through shading and ventilation. Moreover, they also concluded that the outside circulation mode was a more effective ventilation strategy to reduce cooling energy (5.0% to 5.8%) in comparison with the cross ventilation mode (2.5% to 4.6%).

Obviously, the thermal behavior and energy efficiency of buildings also depend on the buildings' envelope and construction system. In Tunisia, Abbassi et al. [15] performed numerical simulations, for a small single zone building (4 m × 4 m), to evaluate the heating energy savings provided by a

Trombe wall for different heavyweight building envelope façade walls (e.g., brick and stone), having different thermal transmittances (*U*-values), ranging from 2.035 W/(m2·K) down to 0.388 W/(m2·K) for a higher insulated exterior wall. For a smaller TW (3 m<sup>2</sup> area), they predicted heating energy savings, ranging from 28% up to 69%, for lower and higher thermal insulation levels, respectively. For a larger TW (6 m<sup>2</sup> area), the analogous heating energy savings ranged from 66% up to 98%.

An interesting alternative to traditional reinforced concrete and ceramic masonry construction is the lightweight steel frame (LSF) system, which has been attracting attention worldwide, given its functional, economic, and environmental advantages [16,17]. This lightweight innovative system presents construction flexibility and adaptability due to its modularity [18], safety at work, and construction economy due to the industrialized nature of the components, which also facilitates series production, prefabrication, and transportation [19]. In fact, several previous research studies have addressed the LSF system-related benefits, including sustainability [20], life cycle energy balance [21], and operational energy [22]. Nevertheless, an effort has been made to mitigate eventual drawbacks related to the thermal behavior of LSF construction, aiming to mitigate thermal bridges originated by the high thermal conductivity of the steel elements [23,24] and to increase the thermal inertia of this type of construction [25].

As mentioned before, the thermal behavior and energy efficiency of a Trombe wall depend on many factors, such as geometric (e.g., area, height, thickness, and orientation of the TW; existence and dimension of overhangs), materials' properties (e.g., storage wall thermal properties; glazed pane optical and thermal properties; shutter thermal properties; thermal insulation), fluid dynamics (e.g., dimensions and control of inner/outer and upper/lower vents; thickness of the air channel; natural or forced airflow), location (e.g., latitude; north or south hemisphere), and weather (e.g., solar radiation level and incidence angle; nocturnal cloudy or clear sky; temperature; wind speed, and direction) [6]. Thus, it is not an easy task to adequately design and control a TW device to take full thermal, energy, and economic advantages [26,27].

As stated before, despite the LSF system advantages, there are also possible drawbacks, such as the reduced thermal inertia, due to its natural weightlessness, compared to traditional concrete structures [28]. Thus, it would be interesting to evaluate the effect of a solar passive Trombe wall device, which is characterized by having a massive storage wall, on an LSF construction system, having low thermal inertia and reduced mass. However, this kind of research has not been found in the literature. Moreover, research works on water Trombe walls are very scarce. Therefore, in this work, the influence of a passive solar water Trombe wall (TW) device on the thermal behavior and energy efficiency of a lightweight steel frame (LSF) compartment, located in Coimbra (Portugal), was studied, being this evaluation based in numerical simulations and in situ measurements. Measurements of indoor air temperature were performed inside two real scale experimental identical cubic modules, exposed to natural exterior weather conditions, while simulations were performed using advanced dynamic models, validated experimentally.

First, the experimental approach has been described, regarding the LSF experimental modules, the TW prototype, the weather stations, and temperature/humidity data-logger sensors. After, the numerical approach has been detailed, including the 2D thermal computations to obtain the *U*-values of the LSF components and the advanced numerical simulations. Next, the calibration and model validation has been reported for both reference and TW LSF models, and some computational fluid dynamics (CFD) results have also been reported. Afterward, the obtained results have been discussed and grouped in TW benefits and parametric study. The winter TW benefits were evaluated regarding indoor air temperature increase and heating energy reduction. The thermal behavior parametric study was performed for several TW key-factors, such as the thicknesses of the air cavity and storage wall and dimensions of the internal vents and the storage wall materials. Finally, some concluding remarks about this research work have been highlighted.

#### **2. Materials and Methods**

The materials and methods used in this research have been described in detail in this section, starting with the experimental and numerical approaches, followed by the calibration and validation of the advanced dynamic thermal simulation models of the LSF modules and water Trombe wall.

#### *2.1. Experimental Approach*

#### 2.1.1. LSF Experimental Modules

The experimental measurements were performed on two similar lightweight steel frame (LSF) modules constructed near the Department of Civil Engineering (DEC) of the University of Coimbra (Portugal), as illustrated in Figure 1, having a GPS coordinates: 40.1855◦ N, 8.4167◦ W. Those experimental modules were two identical cubic compartments constructed in LSF, with inner dimensions: (L) 2.75 m × (W) 2.75 m × (H) 2.80 m. Module 1 was used as a reference (for results comparison), while module 2 had a water Trombe wall prototype on its south facade.

**Figure 1.** LSF (lightweight steel frame) experimental modules constructed at the University of Coimbra, Engineering Campus (GPS: 40.1855◦ N, 8.4167◦ W).

The external dimensions of the experimental modules, as well as the material specifications of the LSF construction elements, such as the number of layers, materials, and thicknesses, are schematically illustrated in Figure 2, while Table 1 displays the thermal conductivities of the materials. In these experimental modules, the LSF system B(A)a was adopted and manufactured by Urbimagem company [29], making use of steel profiles C100 × 45 × 1.5 mm. The structural sheathing was provided by 12 mm oriented strand board (OSB) panels [30] on both sides of the walls' steel frame. The ceiling was also inferiorly lined with OSB panels, as well as the upper side of the roof steel frame beams. To allow access to the interior, both modules had a similar wooden door (2.00 m high by 0.78 m wide), which was thermally insulated with the same expanded polystyrene (EPS) external thermal insulation composite system (ETICS) system of the walls. There were no windows in the experimental TW modules. This was justified by the intention to isolate the TW effect in the evaluated compartments. A glazed window (e.g., south orientated) would provide additional solar heat gains, which would be overlapped and more difficult to distinguish from the heat gains provided by the TW device.

Notice that, as illustrated in Figure 2, the experimental modules were designed to have gypsum plasterboard (GPB) as an inner sheathing layer of walls and ceiling, but later it was decided not to apply these GPB panels. The batt insulation was provided by 100 mm mineral wool (MW) [31], fulfilling the air-cavity between the steel frame. The exterior thermal insulation composite system (ETICS) was made with EPS thermal insulation [32] (50 mm thick) and finished by a reinforced plaster layer (5 mm). The exterior thermal insulation of the roof was made of extruded polystyrene (XPS) [33] with the same

thickness. To avoid moisture direct contact from the ground, the floor was 300 mm elevated, creating a small crawl space below, as illustrated in Figure 2, having an 18 mm OSB panel [30] below and another above the continuous XPS [34] thermal insulation layer (60 mm thick). The inclined flat roof was waterproofed by a polyvinyl chloride (PVC) membrane [35] (1.5 mm thick), forming a plenum above the ceiling with variable thickness.


**Table 1.** Thermal conductivity (λ ) of the materials used in the lightweight steel frame (LSF) modules.

<sup>1</sup> ETICS, external thermal insulation composite system; <sup>2</sup> EPS, expanded polystyrene; <sup>3</sup> OSB, oriented strand board; <sup>4</sup> XPS, extruded polystyrene; <sup>5</sup> PVC, polyvinyl chloride.

**Figure 2.** Schematic details of the LSF modules construction elements (adapted from [36]).

Table 2 displays, for each LSF element, the materials and thicknesses of the layers, as well as the computed thermal transmittance (*U*-value). Notice that two types of layers were assessed in these LSF elements: (1) homogeneous, where the steel frame was not included in the thermal computations, given its location outside the insulation and sheathing materials, and (2) inhomogeneous, where the steel frame crossed through the insulation materials (e.g., mineral wool). The *U*-value for the elements with homogeneous layers (floor, roof, and door) was computed following the analytical calculation procedures prescribed by standard ISO 6946 [41]. The *U*-values of the LSF elements

containing inhomogeneous layers (walls and ceiling) were computed, making use of bi-dimensional (2D) finite element method (FEM) models built in the THERM software [42], as has been detailed next in Section 2.2.1. The obtained *<sup>U</sup>*-values (Table 2) ranged from 0.326 W/(m2·K) in the walls up to 0.670 W/(m2·K) in the ceiling.


<sup>1</sup> ETICS, external thermal insulation composite system; <sup>2</sup> EPS, expanded polystyrene; <sup>3</sup> OSB, oriented strand board; <sup>4</sup> XPS, extruded polystyrene; <sup>5</sup> PVC, polyvinyl chloride.

#### 2.1.2. Trombe Wall Prototype

The Trombe wall prototype (2.80 m high and 0.55 m wide) was placed on the south-oriented wall of module 2 (Figure 1). Figure 3a schematically illustrates the geometry of this Trombe wall prototype, which was developed and executed during a Ph.D. research work [36]. Notice that the dimensions of this modular TW prototype were defined, taking into account the ceiling height (2.80 m) and the usual vertical steel stud spacing in LSF construction (0.60 m). The thermal storage wall was made with a black-painted steel sheet tank fulfilled with water, having 50 mm of thickness. On the outer side, there was an aluminum frame glazing system with double glass (4 mm + 16 mm of argon + planistar 6 mm), having an effective solar absorption area of 1.1 m2. The glazing panel had a solar heat gain coefficient (SHGC) equal to 0.743, while the direct solar transmission was 0.667, and the thermal transmittance was 2.552 W/(m2·K), as displayed in Figure 3b.

**Figure 3.** Trombe wall prototype: (**a**) Schematic geometry details (adapted from [36]); (**b**) Glazing optical and thermal properties.

This glazed aluminum frame had a top and lower exterior openings for exterior ventilation, which were not used during these experiments, being all the time closed. Between the storage wall and the outer glazing, there was a 100 mm thick air cavity. On the inner surface of the storage wall, there was a layer of 0.10 m of mineral wool, covered by an OSB panel (12 mm). To allow air circulation between the outer air cavity and the indoor environment, there were two rectangular air vents on the Trombe wall: (1) an upper air vent, 0.50 m wide by 0.10 m high, and (2) a bottom air vent with the same width but a smaller height (0.05 m).

#### 2.1.3. Monitoring Equipment

To reproduce the thermal behavior of the experimental modules exposed to exterior weather conditions, it was needed to have access to hourly weather data recorded nearby. With this purpose, two weather stations were used: (1) Department of Mechanical Engineering (DEM) [43], also located in the Engineering campus of the University of Coimbra (GPS: 40.1849◦ N, 8.4132◦ W), and (2) CoolHaven company [44], located in Coimbra iParque, Antanhol (GPS: 40.1792◦ N, 8.4654◦ W).

The nearest weather data station (DEM) was used for most of the data needed to perform advanced dynamic simulations, including air temperature, dew-point temperature, relative humidity, wind direction, wind speed, atmospheric pressure, and precipitation. However, this weather station did not provide some additional relevant weather data, such as the parameters related to solar radiation, i.e., global horizontal radiation, diffuse horizontal radiation, and direct normal radiation. This essential detailed solar radiation information was obtained in the CoolHaven weather station, located about 7 km from the experimental modules.

Regarding the hardware, the DEM weather station is a wireless Davis Vantage Pro2 Plus [45], while the CoolHaven is constituted of several sensors, with the pyranometer being a sunshine sensor Delta-T BF5 [46].

Notice that according to the Köppen–Geiger climate classification [47], the city of Coimbra (Portugal) is located in a Csb climate region, which is characterized by a temperate climate with rainy winter and dry summer slightly hot, being a very frequent climate within the Mediterranean region [16].

The indoor air temperature and humidity were measured simultaneously, inside both LSF modules, to monitor their thermal behavior and verify the influence of the solar Trombe wall. With this purpose, one Tinytag Ultra 2—TGU-4500 [48] air temperature and humidity sensor was installed inside each module, being suspended in the middle ceiling, at mid-height. These sensors were factory calibrated, having a precision of ±0.45 ◦C for temperature and ±3% for relative humidity. The measured data was averaged and recorded every 10 minutes, having a sampling interval of 10 seconds. The in situ measurements took place from the 26th of July 2019 until the 19th of January 2020.

#### *2.2. Numerical Approach*

#### 2.2.1. 2D FEM Thermal Computations

As mentioned before (see Section 2.1.1), the *U*-values of the inhomogeneous LSF elements (walls and ceiling) were computed, making use of bi-dimensional (2D) models implemented in a finite element method (FEM) software: THERM [42]. The FEM mesh was refined to have a maximum error of 2%.

#### LSF Ceiling Element

For the ceiling element, as the steel profiles are placed only in one direction (see the yellow region in Figure 4a), the *U*-value was directly obtained from the 2D FEM model, as illustrated in Figure 4b. The model had a width of 600 mm, i.e., equal to the distance between the steel studs within the ceiling. The steel C stud was positioned in the middle of the model, as shown in Figure 4b, and this is a representative part of the LSF ceiling slab. Moreover, the ceiling mineral wool (MW) insulation was considered only between steel sections since, in practice, it was not possible to put MW inside the corresponding steel lattice beam, where it was considered an air gap. Figure 4c displays the temperature distribution predicted in the ceiling cross-section, where the thermal bridged effect was clear due to the MW thermal insulation discontinuity. The global *U*-value computed from the THERM model was 0.670 W/(m2·K). Notice that assuming homogeneous layers, i.e., considering continuous MW insulation and neglecting the steel studs, the *<sup>U</sup>*-value obtained was 0.334 W/(m2·K), being 50% smaller.

#### LSF Wall Element

Since the LSF walls had steel studs in vertical, horizontal, and diagonal planes (see Figure 5a), the bi-dimensional *U*-value computation procedure was different from the ceiling element, where the *U*-value was directly obtained from the THERM model. It is well known that an insulated LSF element has two distinct thermal zones [49,50]: (1) an increased heat transfer zone (lower thermal resistance) in the vicinity of the steel studs, given the high thermal conductivity of steel, and (2) a more reduced heat transfer zone (higher thermal resistance) in the insulated cavity between the steel studs. Thus, the global thermal transmittance (*Uglobal*) of LSF elements with complex steel frame could be estimated, making an area-weighted summation of the *U*-values for each thermal zone mentioned before ("stud" and "cav"), as given in the following equation:

$$\mathcal{U}\_{\text{global}} = \frac{\mathcal{U}\_{\text{stud}} \mathcal{A}\_{\text{stud}} + \mathcal{U}\_{\text{cav}} \mathcal{A}\_{\text{cav}}}{\mathcal{A}\_{\text{global}}} \tag{1}$$

where *Aglobal* is the total area of the LSF element (internal dimensions), *Astud* is the total area of influence of the steel stud on the LSF element, and *Acav* is the remaining cavity area of the LSF wall. For this specific LSF wall, the areas considered in the computations are displayed in Figure 5a.

Both *U*-values (*U*stud and *U*cav) were obtained, making use of a THERM model, as illustrated in Figure 5b. This simplified LSF wall model had a length equal to the spacing between the vertical steel studs, i.e., 600 mm. To obtain the two representative *U*-values, two "measurement" zones were simulated in the LSF wall model: one right under the steel stud and another one in the edge of the wall cavity. These "measurement" zones were modeled having the same width as the steel stud flange, i.e., 45 mm, and is delimited in Figure 5 by two dashed white lines.

Figure 5c displays the obtained temperature (◦C) color distribution along the cross-section of the LSF wall model and is well visible in the thermal bridge originated by the central steel stud and its correspondent temperature disturbance. Figure 5d shows the computed heat flux (W/m2) distribution within the cross-section of the LSF wall, as well as the two *U*-values computed in the steel stud vicinity and in the edge of the wall cavity. As expected, the *<sup>U</sup>*stud (0.797 W/m2·K) was considerably higher (+260%) than the *<sup>U</sup>*cav (0.221 W/m2·K), confirming the huge relevance of the steel stud (only 1.5 mm thick) in the thermal performance of the LSF wall.

**Figure 4.** LSF ceiling element: (**a**) Plan view of the ceiling steel frame; (**b**) THERM model; (**c**) Temperature color distribution and obtained *U*-value.

**Figure 5.** *Cont*.

**Figure 5.** LSF wall element: (**a**) Frontal view of the wall steel frame; (**b**) THERM model; (**c**) Temperature color distribution; (**d**) Heat flux distribution and local *U*-values.

Finally, knowing the three areas (Figure 5a) and the two *U*-values (Figure 5d) and making use of Equation (1), a global *<sup>U</sup>*-value equal to 0.326 W/(m2·K) was obtained. Notice that when the steel studs were neglected and homogenous layers were assumed, the *<sup>U</sup>*-value reduced to 0.225 W/(m2·K) (31% smaller).

It is important to highlight that there are several strategies to mitigate the thermal bridges originated by steel studs within an LSF component, reducing their *U*-value, such as the use of thermal break (TB) strips within steel studs flange [51]. These TB strips could be made of different materials, such as recycled tire rubber [52]. Shortly, it was intended to use this type of TB strips to improve the thermal performance of these experimental LSF modules.

#### 2.2.2. Advanced Dynamic Simulations

The advanced dynamic thermal simulations were performed in the software DesignBuilder version 5.5.0.012 (DesignBuilder Software Ltd, Stroud, Gloucester, UK) [37]. The computations were performed, making use of hourly interval data. A replica of the two LSF experimental modules photographed in Figure 1 was modeled, taking into account the location/climate, the geometry/dimensions, the construction elements composition (e.g., walls, floor, ceiling, roof, door, and Trombe wall), the material properties, the airtightness, the activity, and occupation parameters. Figure 6 exhibits a print-screen view of the two models: (1) module 1, used as reference (Figure 6a), and (2) module 2, containing the Trombe wall (Figure 6b).

The airtightness of these experimental modules was measured in-situ [36], and the obtained value (0.05 air changes per hour) was implemented in the DesignBuilder model as a constant value and without any natural ventilation since, during the measurements, the openings (back door and Trombe wall exterior vents) were always closed. Moreover, the modules were kept empty, i.e., without anyone inside. Thus, the occupancy was set as "null", and the activity tab as "none". Notice that the color of the materials was also reproduced, in particular, the black color of the Trombe wall (Figure 6b).

#### *2.3. Calibration and Model Validation*

To ensure good reliability of the DesignBuilder [37] advanced dynamic models (Figure 6) thermal behavior predictions, the obtained simulation results were compared with the air temperature in-situ measurements (see Section 2.1.3), performed inside the LSF modules (Figure 1), subjected to natural outdoor weather conditions (recorded nearby, as previously explained in Section 2.1.3), allowing to validate these models, as shown next.

**Figure 6.** DesignBuilder models southeast views: (**a**) Module 1 (Reference); (**b**) Module 2 (Trombe wall, TW).

#### 2.3.1. Reference LSF Model

Figure 7 presents a graph with a comparison among predicted and measured indoor air temperatures in the reference LSF module (module 1) during one week (2–8 September 2019). A good agreement between the DesignBuilder model predictions and the in-situ indoor air temperatures was observed. In fact, both average temperatures were very similar: 26.4 ◦C (recorded) and 26.3 ◦C (predicted). Moreover, the root mean square error (RMSE) was only 0.3 ◦C, allowing to conclude that this DesignBuilder advanced dynamic simulation reference LSF model was calibrated and experimentally validated.

**Figure 7.** Predicted and measured indoor temperatures in module 1 (reference).

#### 2.3.2. Trombe Wall LSF Model

The accuracy of the Trombe wall LSF model was also verified by comparison among predicted and measured indoor air temperatures. Figure 8 displays the obtained results plot, in which a good agreement between both curves was observed. The RMSE for this model was 0.5 ◦C, confirming also a good accuracy performance of this second model.

**Figure 8.** Predicted and measured indoor temperatures in module 2 (with a Trombe wall).

#### 2.3.3. Trombe Wall CFD Assessment

To verify if the modeled Trombe wall is operating coherently, a computational fluid dynamics (CFD) analysis was also conducted on DesignBuilder, which has a built-in CFD tool. Figure 9 displays the results of the CFD analysis, carried for the 16:00 hours of the 4th of September, with both air velocity and temperature in a color scale being displayed, as well as velocity vectors.

**Figure 9.** CFD (computational fluid dynamics) analysis (air velocity and temperature) of module 2 (with a Trombe wall): (**a**) Horizontal planes at vent levels; (**b**) Vertical plane in front of the Trombe wall.

Looking at the results of the horizontal plane plotted in Figure 9a was well visible the colder air entrance to the Trombe wall air cavity through its lower vent, as well as the warmer air flowing out of the upper vent near the ceiling. Moreover, in Figure 9b (the vertical plane in front of the Trombe wall), the air stratification in height and also the air being heated near the Trombe wall were again visible, which was exposed to direct solar radiation (4 pm) and, consequently, was flowing up to the ceiling. Therefore, these CFD simulation results made sense and were coherent with the expected ones for a

compartment with a Trombe wall exposed to direct solar radiation, which ensured the reliability of the implemented models.

#### **3. Results and Discussion**

In this section, the obtained results have been presented and discussed, starting with the Trombe wall benefits, regarding the thermal behavior and heating energy savings. Thereafter, the results of the sensibility analysis, for several Trombe wall parameters, have been described and discussed.

#### *3.1. Trombe Wall Benefits*

In this section, the water Trombe wall benefits were assessed, making use of in situ indoor air temperature measurements (Section 3.1.1.) and advanced dynamic numerical simulations for the heating energy reduction predictions (Section 3.1.2.). These assessments were performed by comparison between module 1 (the reference one) and module 2 (the one with a Trombe wall) located in the city of Coimbra (Portugal), during winter.

#### 3.1.1. Indoor Temperature Increase

The indoor air temperature comparisons were made using the data from measurements taken simultaneously with the temperature and humidity sensors [48], on both modules (with and without the Trombe wall) and are plotted in Figure 10, as well as the exterior environment air temperature. Two distinct winter weeks were chosen to demonstrate the behavior of the modules under different weather conditions. In Figure 10a, the records for a sunny week (from 28th of December to 3rd of January) are displayed, while in Figure 10b, the measurements for a cloudy week (from 16th to 22nd of December) are shown.

In the sunny winter week (Figure 10a), the indoor air temperature increase in module 2 due to the Trombe wall was well visible, having an average temperature of 16.2 ◦C, i.e., a temperature increase of 3.3 ◦C relative to module 1. Notice that even with a Trombe wall, the indoor comfort air temperature (e.g., 18 ◦C) was not reached. Another interesting feature was that the daily indoor air temperature amplitude (or fluctuation) was also greater in the experimental module with the Trombe wall (module 2), having a higher temperature increase rate during the day (due to the solar heat gains) and also a higher temperature decrease rate during the night (due to the higher heat losses through the Trombe wall, which did have any night shutter device).

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**Figure 10.** *Cont*.

**Figure 10.** Recorded indoor air temperatures with and without a Trombe wall: (**a**) Winter sunny week; (**b**) Winter cloudy week.

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When the sky was cloudy (Figure 10b), as expected, the daily temperature variation was very smothered, and the air temperature difference inside the modules became very reduced, which was only 1 ◦C higher for this week inside module 2. Comparing both weeks (sunny and cloudy), the average environment exterior air temperature was lower during the sunny week (Figure 10a) (12.2 ◦C) in comparison with the cloudy week (13.4 ◦C), which was 1.2 ◦C higher. This was due to the night cooling effect, which was much higher in a winter clear sky in comparison with a cloudy sky. Thus, this feature also demonstrated how important it was to control the night heat losses, mainly when the sky was clear, in order to optimize the thermal performance of the Trombe wall during the heating season.

#### 3.1.2. Heating Energy Decrease

In this section, the heating energy decrease due to the existence of a Trombe Wall was predicted, making use of advanced numerical dynamic simulation models, as previously detailed in Section 2.2.2 and validated in Section 2.3. The hourly weather data was obtained from the EnergyPlus IWEC database [53] for Coimbra city (Portugal), and the computations were performed for all winter season (from 22nd of December until the 20th of March). The modeled air-conditioning heating system was a "split" type with no fresh air, having a coefficient of performance (COP) for heating mode equal to 2.35, with the adopted energy source the electricity from the grid.

To compare its relevance in the heating energy demand, two heating set-points were simulated, namely, 20 ◦C and 18 ◦C, respectively; the former and current thermal comfort temperatures considered for calculating residential heating energy needs in Portugal [54].

Moreover, two occupation schedules and use types were considered, namely, (1) an office space occupied from 08:00 to 18:00 during weekdays (Monday to Friday), and (2) a residential space occupied from 19:00 to 07:00 during all days. The predicted energy demand for heating (electricity) was displayed and analyzed as a total value (kWh) and as normalized values (kWh/m2).

#### Residential Space Use (Heating during the Night)

The heating energy demand predicted for residential space use (night occupation) is displayed in Figure 11 for both LSF modules and two heating set-points. As expected, reducing the heating set-point (18 ◦C instead of 20 ◦C) allowed reducing also the heating energy consumption. This energy reduction was significant (Figure 11b), ranging from −33%, in the reference LSF module 1, to −40% in the Trombe wall LSF module 2.

**Figure 11.** Predicted heating energy consumption (electricity) during the winter season in Coimbra (Portugal), assuming residential space use, for two different set-points: (**a**) Total values; (**b**) Normalized values.

The heating energy consumption in module 2 was 5% lower than in module 1 for an 18 ◦C heating set-point, confirming the energy efficiency advantage of the Trombe wall (TW) in the second LSF module. However, when the heating set-point was higher (20 ◦C), the computed results showed a 5% increase in the heating energy for the TW module 2 (24.79 kWh/m2) in comparison with the reference module 1 (23.60 kWh/m2). This surprising feature was related to the increased heat losses during the night due to the existence of the TW in module 2, which were not enough to balance the solar heat gains during the daytime, and this assumption has been explained in detail in the following paragraphs.

The space heating energy demand, besides the efficiency of the air-conditioning system (assumed to be 2.35 for the heating mode in this work), depended on the heat balance (gains versus losses) for each module. When this heat balance was positive (e.g., during a sunny day due to significant solar heat gains), the indoor temperature arose. When this heat balance was negative (e.g., during the night due to the exterior temperature drop and absence of solar radiation), the indoor temperature decreased.

As measured and previously plotted in Figure 10a, the indoor temperature increase rate during the day was bigger in module 2 (red line) due to the higher solar heat gains provided by the Trombe wall. However, as also displayed in the same figure, during the night, the indoor temperature decrease rate was also bigger in the TW module 2, compared to the reference module 1 (black line), due to higher heat losses through the Trombe wall.

In fact, the thermal transmittance (*U*-value) of the TW device, due to air circulation between the glazed air-cavity and the interior of the module, was increased to the *U*-value of the glazing panel (2.552 W/(m2·K), see Figure 3b). Comparing this *<sup>U</sup>*-value with the one provided by the LSF wall (0.326 W/(m2·K), see Table 2), for the same area and temperature difference, the heat losses through the glazing panel of the TW were almost 7 times higher (+683%).

Obviously, when the indoor air temperature set-point was elevated from 18 ◦C up to 20 ◦C, the temperature difference between indoor and outdoor conditions also increased, leading also to an increase in the heat losses, which originated a higher space heating energy consumption to maintain the defined set-point indoor temperature. Once again, this feature reinforced the importance of mitigating heat losses through the TW, mainly during winter season night-time, for example, making use of a controllable night shutter device.

#### Office Space Use (Heating during the Day)

The heating energy demand simulation results, assuming an office space use, i.e., during the daytime, in both LSF modules, are displayed in Figure 12. Now, the energy efficiency benefits of the TW use were significantly higher in comparison with the residential daytime use (Figure 11). The heating

energy reduction ranged from −14%, for a 20 ◦C heating set-point, to −27% for an 18 ◦C set-point. This improved energy efficiency was because the heating schedule of the air-conditioning system matched the higher TW solar heating gains during the daytime. Consequently, the indoor temperature increased, and the heating energy use decreased for both heating set-points.

**Figure 12.** Predicted heating energy consumption (electricity) during the winter season in Coimbra (Portugal), assuming office space use, for two different heating set-points: (**a**) Total values; (**b**) Normalized values.

Comparing the energy demand for both heating set-points, the energy reduction in percentages was similar to the previous ones, i.e., residential space use (Figure 11b), ranging from −32% up to −42% (Figure 12b), for reference LSF module 1 and TW module 2, respectively. However, in absolute values, this energy consumption reduction was smaller, i.e., <sup>−</sup>5.41 kWh/m<sup>2</sup> (office daytime use) instead of <sup>−</sup>7.80 kWh/m<sup>2</sup> (residential night-time use) for module 1, while for module 2, it was <sup>−</sup>6.09 kWh/m2 instead of <sup>−</sup>9.84 kWh/m2, for office and residential space use, respectively.

Jaber and Ajib [55] also performed hourly energy computer simulations to analyze the energy performance of a Trombe wall system for a typical Jordanian residential building (Mediterranean region). The studied house had a rectangular shape, having a floor area of about 154 m2. The heavyweight façade walls had a very reduced thermal transmittance value, 0.133 W/(m2·K), which corresponded to 41% of the LSF walls' *<sup>U</sup>*-value in the experimental modules, i.e., 0.326 W/(m2·K) (see Table 2).

Their simulations were performed for a 20 ◦C heating set-point [55]. The predicted normalized heating energy consumption for the Jordanian building, without a Trombe wall, was 15.27 kWh/m2, which was reduced to 12.09 kWh/m2 (−21%), simulating a TW filling 18% of the south-oriented façade area (two bedrooms). They performed several simulations for different TW area ratios, ranging from 0% up to 50%, and based on the obtained results, they adjusted a polynomial curve (2nd order regression) to estimate the percentage of energy saving.

Making use of the previously mentioned estimation curve and applying the area ratio for the modular water TW evaluated in this paper, which was about 20%, the predicted energy saving would be around 22%. Not surprisingly, due to our reduced exterior walls insulation level, this energy-saving prediction was considerably higher than the ones obtained here for the 20 ◦C indoor set-point temperature.

#### *3.2. Parametric Study*

After analyzing the Trombe wall (TW) benefits in terms of indoor air temperature increase and heating energy decrease, in this section, a parametric study was conducted to assess the impact of the changes of some TW-related parameters on its thermal behavior. In this sensibility analysis, all the simulations were performed for the TW LSF module 2, having as reference for comparison the DesignBuilder model, previously validated in Section 2.3.2, i.e., an unoccupied module. Notice that only one parameter was changed for each evaluated scenario, as displayed in Table 3. Four different parameters were evaluated: (1) Air cavity thickness; (2) Air vents dimensions; (3) Storage thickness; (4) Thermal storage material. For each parameter, two additional scenarios were assessed, besides the reference model scenario. Again, the hourly weather data for Coimbra (Portugal) was used [53], and a sunny winter week was chosen (23rd–29th January) for these simulations.


**Table 3.** Overview of evaluated parameters, models' identifications, and used values.

#### 3.2.1. Air Cavity Thickness

The first TW parameter analyzed was the air cavity thickness between the storage wall and the glazed exterior frame. Three different air cavity thicknesses were evaluated: 10 cm (reference), 20 cm (scenario 1), and 30 cm (scenario 2), as illustrated in Figure 13. The increase in the air cavity thickness originated an indoor air temperature decrease. While the reference model had an average temperature of 18.2 ◦C, when the air cavity thickness was doubled (20 cm) and tripled (30 cm), the indoor temperature decreased to 0.9 ◦C and 1.2 ◦C, respectively. These results allowed to conclude that, for this TW configuration, the better thermal performance was achieved for the smaller air cavity (10 cm), which could be related to the lower air volume to be heated inside the air cavity and the higher buoyancy effect, promoting an increased upwards air convection and consequent higher heat flow through the upper vent to the interior of the module.

**Figure 13.** Influence of different air cavity thicknesses.

Hong et al. [56] performed a three-dimensional CFD thermal simulation of a Trombe wall with Venetian blind structure located in Hefei (China), assuming adiabatic surfaces for the air vents and internal wall. They compared several air cavity thicknesses, ranging from 8 cm up to 18 cm, with an increment of 2 cm. No significant thermal performance improvement was found for a thickness of the air cavity higher than 14 cm. Thus, they suggested a thickness equal to 14 cm.

#### 3.2.2. Air Vents Dimensions

The second parameter analyzed was the dimension of the interior vents present on the storage wall to allow vertical air convection and airflow to/from the LSF module. The reference model had an upper vent with dimensions of 50 × 10 cm and a lower vent with 50 × 5 cm. Two additional scenarios were evaluated by modeling increased vents dimensions: 50 × 13 cm (upper) and 50 × 8 cm (lower) in scenario 3, and; 50 × 16 cm (upper) and 50 × 11 cm (lower) in scenario 4.

Figure 14 displays the obtained results, where a slightly indoor air temperature increase was visible with an increase in the dimensions of the air vents (+0.4 ◦C for scenario 3 and +0.5 ◦C for scenario 4). As expected, this indoor temperature increase was greater during the daytime, near noon, when the solar radiation was also higher. This better thermal performance could be justified by the increased natural air convection and airflow exchange between the TW air cavity and the interior of the module. Moreover, it could be deduced that forced air convection, making use of small fans, might improve, even more, the TW thermal performance.

**Figure 14.** Influence of different dimensions of the air vents.

Hong et al. [56] also evaluated the influence of the inlet/outlet vent dimensions in the Trombe wall (2.00 m high × 1.00 m width) thermal performance. They assumed equal sized upper and lower vents and fixed their height to 10 cm. The vents width ranged from 20 cm up to 70 cm, with an increment of 10 cm. They found a slight decrease in the TW thermal performance for 70 cm width vents and suggested the use of vents with the following dimensions: 60 cm width × 10 cm height.

#### 3.2.3. Storage Wall Thickness

The third parameter analyzed was the thickness of the water storage wall of the Trombe wall. The reference model had a 5 cm water storage wall composed of black painted steel, filled with water. Two additional scenarios with increased storage wall thickness were evaluated: 10 cm for scenario 5 and 15 cm for scenario 6.

Figure 15 exhibits the obtained results, where a decrease in indoor air temperature was visible in scenarios 5 (−0.7 ◦C) and 6 (−1.0 ◦C). This worst TW thermal performance could be justified by the larger volumes of water to be heated, inside the storage walls, by the same solar radiation and the consequent lower temperatures achieved.

**Figure 15.** Influence of different storage wall thicknesses.

Briga-sá et al. [9] also evaluated the influence of the storage wall thickness (15 cm up to 40 cm), made of concrete, on ventilated and non-ventilated Trombe walls for the climate of Vila Real, a city located in the north of Portugal. Making use of a simplified calculation methodology prescribed by standard ISO13790:2008, they found that the heat gains were reduced when increasing the thickness for non-ventilated TWs, while for ventilated TWs, the heat gains increased.

#### 3.2.4. Thermal Storage Material

The fourth and last parameter studied was the thermal storage material of the Trombe wall. As stated before, the reference TW thermal storage material was water. Two additional scenarios were simulated, making use of two other materials: concrete in scenario 7 and basalt stone in scenario 8. The thermal properties (thermal conductivity, specific heat, and density) of these three materials are displayed in Table 4. Regarding the optical properties, all these materials were modeled as being black painted, i.e., having solar and visible absorptances equal to 0.9.



<sup>1</sup> For 40 ◦C temperature.

Figure 16 exhibits the obtained results, showing a slight decrease in the average indoor air temperature inside module 2 for the newly evaluated thermal storage materials: −0.4 ◦C for concrete (scenario 7) and −0.8 ◦C for basalt stone (scenario 8). Concrete storage material exhibited a higher temperature increase rate but also the higher temperature decrease rate during the cooling afternoon and night time, perhaps due to the significant lower specific heat (about four times smaller) and higher thermal conductivity (almost two times greater). The basalt stone temperature curve (scenario 8) exhibited a similar trend to the water temperature curve (Ref.), but with slightly lower indoor air temperature values (−0.8 ◦C).

**Figure 16.** Influence of different thermal storage materials.

As stated by Saadatian et al. [7], "Because the specific heat of water (*c*) is higher than that of other types of building material, such as concrete, bricks, adobe, and stone, water stores more heat than the other materials. Similarly, because water convects, the transfer of heat to the interior space occurs faster than with classic Trombe walls.". Hu et al. pointed out another advantage of water as a thermal storage material: "Because the specific heat of water is higher than that of the building materials, the water's surface temperature does not rise as high as that of the masonry. Therefore, less heat is reflected back through the glazing." Nevertheless, Saadatian et al. [7], regarding water TWs, also stated that: "in harsh colder climates the glass layer should be insulated. Otherwise, the loss of heat from the warm wall to the outside would be significant.".

#### **4. Conclusions**

In this work, the influence of a passive modular water Trombe wall (TW) in the thermal behavior and energy efficiency of a lightweight steel frame (LSF) compartment was evaluated. Two real scale experimental identical LSF cubic modules, located in Coimbra (Portugal), exposed to natural exterior weather conditions, were used for in situ measurements. Module 1 was used as a reference, while the other one (module 2) was used to measure the influence of the TW, positioned in the south façade, on their thermal behavior by making a direct comparison between both modules. Additionally, these measurements allowed to calibrate and validate two numerical models (without and with a TW), with very good accuracy, i.e., having a root mean square error (RMSE) equal to 0.3 ◦C, for the reference model, and 0.5 ◦C for the TW model. These two validated models were used to perform advanced dynamic thermal simulations, making use of DesignBuilder software. Finally, these validated models allowed to predict the TW benefits in the heating energy consumption, as well as to perform a parametric study to evaluate the influence of four TW-related parameters on its thermal performance.

The first conclusion remark was that in this work, it was possible to evaluate the thermal behavior influence of a TW by in situ direct measurements and also performing advanced thermal dynamic simulations. The assessment was performed by quantifying the TW benefits (thermal and heating energy) and carrying out a thermal behavior parametric study. Several comparisons were performed, regarding (1) Sunny and cloudy winter week thermal behavior; (2) Office and residential space use heating energy; (3) Two heating set-points (20 ◦C and 18 ◦C); (4) Thickness of the TW air cavity; (5) Thickness of the thermal storage wall; (6) Dimensions of the interior upper/lower vents, and (7) Material of the thermal storage wall.

Regarding the obtained results for the TW benefits evaluation, the following main conclusions could be pointed out:

• In both sunny and cloudy winter weeks, the measured temperature was higher in module 2 (with a TW passive device). However, the warmer effect of the TW was much more effective during the sunny week, increasing the average indoor air temperature significantly, i.e.,+3.3 ◦C and+4.0 ◦C relative to the interior of module 1 (reference) and exterior environment temperatures, respectively.


For residential use, the TW energy benefits were very reduced (only 5% decrease for 18 ◦C set-point), and there was even a heating energy consumption increase (+5%) when the set-point was 20 ◦C, due to nocturnal heat losses through the TW device.

Regarding the TW device parametric study, the main conclusions could be summarized as follows:


In short, a TW device could, in fact, significantly improve the thermal behavior of an LSF compartment and reduce heating energy consumption during winter in a Csb Köppen–Geiger [47] Mediterranean climate. However, there were many factors that could influence the TW thermal performance, with adequate design and control to mitigate nocturnal heat losses very important. Otherwise, their thermal performance and energy efficiency improvement could be very insignificant and even decreased.

As most of the research studies, this work also had some limitations, including the assessment of only one climate/location, only one TW orientation (south exposed), only one isolated small compartment (not an entire building) without any window, only one construction system (LSF), only the heating mode during the winter season was evaluated (not an entire year), etc. Thus, in real buildings, thermal behavior and energy performance are much more complex, depending on many more factors. Nevertheless, the obtained results and conclusions could be very useful to identify the main benefits and possible drawbacks of a solar passive TW device in an LSF compartment, as well as to enhance the importance of the indoor set-point temperature and the occupation schedule of the compartment.

**Author Contributions:** All the authors participated equally in this work. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research work was supported by ISISE (Institute for Sustainability and Innovation in Structural Engineering) and funded by FEDER funds through the Competitivity Factors Operational Programme—COMPETE and by national funds through FCT—Foundation for Science and Technology within the scope of the project POCI-01-0145-FEDER-032061.

**Acknowledgments:** The Trombe wall prototype was manufactured by CoolHaven company, and the experimental modules were built with the support of the following companies: Urbimagem; Fachaimper; CoolHaven; Forbo flooring systems; Weber (Saint-Gobain); Termolan; Bifase; Sociveda; Falper, and FibroPlac.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Acronyms**


#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Article*

### **Design and Construction of a New Metering Hot Box for the In Situ Hygrothermal Measurement in Dynamic Conditions of Historic Masonries**

**Mirco Andreotti 1, Marta Calzolari 2, Pietromaria Davoli 3, Luisa Dias Pereira 3,\* , Elena Lucchi <sup>4</sup> and Roberto Malaguti <sup>1</sup>**


Received: 29 May 2020; Accepted: 6 June 2020; Published: 9 June 2020

**Abstract:** The main purpose of the HeLLo project is to contribute to data available on the literature on the real hygrothermal behavior of historic walls and the suitability of insulation technologies. Furthermore, it also aims at minimizing the energy simulation errors at the design phase and at improving their conservation features. In this framework, one of the preliminary activities of the study is the creation of a real in situ hot box to measure and analyze different insulation technologies applied to a real historic wall, to quantify the hygrothermal performance of a masonry building. Inside this box, 'traditional' experiments can be carried out: recording heat flux, surface temperature, and air temperatures, as well as relative humidity values through the use of a new sensing system (composed of thermocouples and temperature/relative humidity combined sensors). Within this paper, the process of development, construction, and validation of this new metering box is exhibited. The new hot box, specifically studied for historic case studies, when compared to other boxes, presents other advantages compared to previous examples, widely exemplified.

**Keywords:** metering hot box; in situ; hygrothermal measurement; dynamic conditions; historic masonries; HeLLo

#### **1. Introduction**

Energy refurbishment of existing buildings is one of the priorities of the European policies to reduce fuel consumption, starting from the recognition of the 'exemplary role of public bodies' buildings (art.5 2012/27/UE) [1] to activate effective strategies in the private building stock. Existing buildings in the European Union are, indeed, responsible for 40% of final energy consumption [2] and for 36% of carbon dioxide (CO2) emissions [3]. Approximately 35% of the buildings are more than 50 years old [3]. Considering the low rate of new buildings construction, 3% in Europe [4], and 2% in the USA [5], energy efficiency in existing, historical, and historic buildings is one of the greatest opportunities towards a sustainable future.

Besides the social and cultural value of all historic buildings, the specific value of heritage assets in Italy strongly justifies the origin of the current research: according to the Italian Ministry for Cultural Heritage and Activities, there are more than 20,000 historic centers of different ages. In light of such numbers, it is evident that many Italian cities are largely made up of historic buildings, which almost often require a greater commitment in the design of conservative and improving interventions than those devoted to the process of new construction. Nonetheless, this is also verified in many other European heritage city centers. Two examples could be pointed out: Edinburgh (Scotland) and Antwerp (Belgium). In 1995, UNESCO added the Old and New Towns of Edinburgh as a World Heritage Site [6]. In this site, 75% of the 4500 individual buildings are listed for their special architectural or historic interest. This city's latest management plan concerning the heritage site (2017–2022) has 6 main objectives and 39 actions, of which stands out" strengthening care and maintenance of buildings and streets' and the 'sustainable re-use of underused and unused buildings" [6]. In Antwerp, instead, there are several listed buildings, such as the Vleeshuis Museum located in the historic center. This significant building has been the object of study [7] in various domains (e.g., evaluation of brick masonry or the assessment of hygrothermal parameters and conservation of important housed collection).

The interest in historic and historical buildings has been gaining cultural and social strategic roles. One important way of preserving built heritage for the future is to keep it in use and to accommodate new uses, avoiding its transformation into a 'museum' and preserving its cultural memory. In order to make this operation successful, it is mandatory for their adaptation to today's comfort requests for indoor human activities. Moreover, promoting the control of hygrothermal parameters and indoor air quality in such buildings also means assuring better conservation of the decorative features that make them distinguishable and enhance their architectural quality.

The building envelope plays an important role in terms of energy transmission. Particularly, the opaque surface in historic buildings constitutes the largest surface of the envelope, and heat losses through this element are, therefore, of most importance [8–10]. In fact, some authors defend that in historic buildings heat loss through windows is only 10%, while walls and roof account for 60% (35% and 25%, respectively) [11,12]. This means that the intervention aiming to enhance the energy performance of the building should involve the envelope's components to reach a high level of efficiency. As well-known, sometimes, it is impossible due to the presence of architectural features to be preserved, and the project has to focus on different strategies. In other cases, the envelope's insulation is possible operating only on the inner façade of the building. Unfortunately, also in these situations, other difficulties may occur, hindering the good result of the operation.

One of the most significant issues in the field of efficiency topics is the buildings energy consumption gap [13–15] between design and post-occupancy phase [16,17]. In many cases, it has been verified that this gap is due to occupants' behavior [18,19], but it can also be justified by erroneous decisions or values accepted at the design phase (i.e., poor practice or uncertainty in building energy simulation—BES [20]). Many authors have been demonstrating the limitations of traditional BES tools and procedures for the estimation of energy performance of historical buildings [21–25]. This topic reaches a significant dimension in historic buildings refurbishment, once the real wall composition of such buildings is frequently unknown [26] and, for practical matters, in many occasions, several projects and estimations are based on general assumptions [27].

The calibration of the hygrothermal models with measured data is very important to avoid irreparable damage to historic buildings. The combination of several hygrothermal variables [e.g., heat flux (ϕ), surface temperature (Ts), air temperature (Ta), and relative humidity (RH)] should lead to more reliable models.

#### **2. Aims and Methodology**

The main purpose of this study was to contribute to data available on literature on the real hygrothermal behaviour of historic walls and the suitability of insulation technologies, also aiming at minimizing the energy simulation errors at the design phase and at improving their conservation features (i.e., avoiding risks of condensation or damaging their structure). These errors can become very significant. For example, the wrong definition of the thermal behaviour of a thick and heavy external wall, a very common situation in historic buildings (e.g., "the results divergence in thermal mass simulation using different tools" [28] (p. 74) or simply the use of different modelling tools [23]), might lead not only to the definition or to the choice of inappropriate insulation solutions (e.g., risking at generating condensation by changing the original hygrothermal wall behaviour), but also it can lead to mistaken thermal spatial requirements or to over dimensioning of HVAC (heating, ventilation and air conditioning) systems. The negative implication of miss-sized systems and the corresponding increased energy consumption has being recognized in the scientific community [29–31]. Furthermore, moisture reduces thermal performance and causes deterioration of insulation materials [7–9,25].

On the basis of these assumptions, the energy refurbishment of historic heritage with testimonial value is an asset. Given the impossibility to remove samples to be tested in the laboratory, and the likely unknown hygrothermal behaviour of historic walls, in situ measurement methods must be more frequently implemented, expeditious, simple to operate. Though each historic building presents unique features, the developed methods should be, preferably, replicable and repeatable.

In this framework, the HeLLo research project [32] created a real in situ laboratory of measurement to analyse different insulation technologies when applied on a real historic building to quantify the hygrothermal performance of a masonry building. As a first step of the research, the authors developed a version of a revised in situ metering hot box, topic of the present paper, perfectly thought for historic buildings, to adapt the standard in situ measurement techniques to the historic case study. The paper presents the main characteristics and uses of this hot box for in situ hygrothermal tests.

#### **3. State of the Art**

The literature shows two different kinds of in situ tests: (i) test for determining the thermal performance of building elements, in terms of thermal resistance (R-value or R), thermal conductance (C-value or C), or thermal transmittance (U-value or U) [33]; and (ii) hygrothermal monitoring for determining the hygrothermal behaviour of the various wall layers [7,34].

First, commonly used standard tests to experimentally determine the thermal performances of walls [35] were divided in two groups: (i) In situ tests measurements based on the use of the heat flow meter (HFM) method [36–38] or the quantitative infrared thermography testing (ITT) [39,40]; and (ii) laboratory tests performed on hot box chambers [41,42]. Soares et al. [33] and Bienvenido-Huertas et al. [43] have performed two of the most significant literature reviews on this subject. HFM method is a non-destructive testing (NDT) for determining the thermal transmission properties (R, C, or U values) of an existing building directly in situ. The apparatus was composed of a data-logger equipped with two thermal sensors and one heat flux plate for gathering the internal and external Ta or Ts and the ϕ through the element. The international standard ISO 9869 [37] defined the calibration and the installation procedures, the data processing techniques, the methodology for correcting systematic errors, and the reporting format. In parallel, the literature presents several methods to solve meteorological and practical issues to reduce the errors and the uncertainties due to the measurement location [44,45], the influence of the boundary conditions [44], [45], or the presence of non-homogeneity, high thermal inertia [44], or moisture content [45] in the structure. In addition, the quantitative ITT permits to measure directly in situ the R-value of a masonry, avoiding the problems related to non-correct locations, non-homogeneity in the walls, or the influence of the boundary conditions [46]. Otherwise, ITT was also used in a qualitative way to measure the thermal pattern of walls [47]. Laboratory tests permit to measure the thermal properties of building components in steady-state or dynamic controlled conditions. The guarded hot plate (GHP) measures the steady-state thermal conductivity (λ-value or λ) of homogeneous flat walls [46,47]. The international standard ISO 8302 [48] and the ASTM C177 [49] defined the minimum requirements for designing the apparatus and the testing procedure. The main problem was related to the errors connected to gaps and edge losses. Several studies proposed analytical calculation models for reducing this error [50–52]. The hot box apparatus measures the steady-state and the dynamic thermal performance (R, C, and U values, Ts, internal T, and RH) of inhomogeneous samples. Basically, it is composed of two climatic chambers maintained at different temperatures that simulate the internal and external conditions. The building element under measurement was inserted between the two chambers, and the thermal performance

was obtained, measuring the power required to keep the hot chamber at a constant temperature. The ISO standard 8990 [41], the American ASTM C1363 [53], the European EN 1934 [54], and the Russian GOST 26602.1 [55] defined the minimum requirements for designing the hot box apparatus and the measurement procedure. Two alternative methods are available: the guarded hot box (GHB) and the calibrated hot box (CHB). GHB is composed of a climatic chamber for simulating the exterior temperature, a metering chamber heated to simulate the indoor conditions, and a guard chamber for minimizing the lateral heat flows at the edges of the metering chamber [41,53]. CHB is composed only by a climatic and a metering chamber, surrounded by a "temperature-controlled space" to reduce the errors generated by the apparatus [41,53]. Concerning the hot box method, many researchers have developed their own compact facility, but only a very few correspond to in situ affectations. A significant majority of the examples found in the literature correspond to variations of the hot box method, e.g., facilities for laboratory tests more targeted at wall/materials sample testing [44,56]. In [57], the authors showed the new design of a compact hot box apparatus used for determining properties of wall samples, developed according to ISO 8990 [41]. Though upfront and useful in laboratory, this tool was not developed for in situ measurement and the test rig dimensions are ruled by the sample size requirement. One variation of these models are full scale boxes simulating entire ambiences/buildings [58,59], among which are distinguished outdoor test boxes solutions for building envelope experimental characterisation [60]. Once again, these intend to study new materials/walls, and not existing building construction solutions, for example: window shutters [61], heat insulation solar glass (a type of multifunction PV module) [62], glazed façades with water film [63], multilayer, inhomogeneous, and massive walls [64,65]. More common instead is the use of a combined strategy for data comparison, as for example the in situ testing coupled with computer modelling and steady-state testing in a GHB [66] or, the comparison of steady-state and in situ testing of high R walls incorporating vacuum insulation panels [66].

On the other hand, only a few scientific studies combine both methodologies, and solely for measuring the thermal performances of building elements: The 'chamber'/box and the HFM. In 2008, Peg and Wu [67] approached this strategy by designating an entire room of an apartment situated in a new residential development district in Nanjing as a 'test chamber' where in situ measuring method for the R-value of buildings was tested (defining 'measuring points' arrangement in several walls), but no box was in fact generated. In 2015, in their turn, authors had verified the feasibility of a new developed simple hot box-HFM method (SHB-HFM) to address an in situ measurement of wall thermal transmittance [68]. This SHB-HFM was preceded by another experiment developed by Chinese researchers in 2012, designated Temperature Control Box-HFM method (TCB-HFM) [69] cited in [70]. However, the authors of [70] (p. 748) described this TCB-HFM as not suitable for the in situ measurement, also noticing that measurement thermal transmittance results obtained in [69] were "55% higher than the design thermal transmittance and that the measurement error was attributed to high moisture", denoting the problem of not controlling for humidity in the test.

Besides the final aim of monitoring hygrothermal parameters instead of exclusively the thermal transmittance, the most significant difference between the boxes presented in [68] or [70] and the new one now presented lies in the dimension—none of the SHB-HFM boxes surpasses 0.90 m × 0.90 m × 0.30 m. Further developments on this topic are presented in Section 4.2.

In situ monitoring can be very significant in the case of historic buildings, since: (i) walls samples cannot be examined in the lab (for cultural heritage protection issues, no samples can be removed from original sites); (ii) many historic buildings are abandoned or not in use, and, therefore, are not heated; (iii) many of these building present particular features as high ceilings/volumes and therefore the traditional 1 m × 1 m lab measured surface might not be representative enough of the vertical heat stratification of a historic wall.

The hygrothermal monitoring of heritage buildings can be divided into: environmental monitoring and contact monitoring used, respectively, to assess the environmental condition of a room and the hygrothermal performance inside to a building element [34]. Skills and procedures for the environmental monitoring of Ta and RH are defined by several standards that focus particularly on cultural heritage (CH) [71,72], in order to avoid damage and risks for CH object and surface and users' discomfort [73–78]. Contact monitoring is used to quantify damage already occurred and to predict the presence of potential hygrothermal risks for CH [79]. The methodologies used can be divided into: (i) surface monitoring of Ts and RHs; and (ii) monitoring of T and RH inside the walls [34]. No standard procedures have been developed for the surface monitoring of CH building elements [79]. As a matter of fact, the procedure normally used for new and existing buildings without any heritage value cannot be applied to historic surfaces as risks and losses of historic materials should be avoided.

Moisture content within walls has proved difficult to measure because several variables are unknown, including the influence of the probe on the test results [80]. Moisture content inside the walls can be measured in two ways using: (i) direct methods based on the gravimetric analysis; and (ii) indirect methods based on the drilling of wooden dowels inserted into the building element. The gravimetric analysis consists in the measurement of water content in a building material sample, weighing its mass with analytical scales in a range of controlled wet and dry conditions [81,82]. Standard CEN EN 16,682 [81] and UNI 11,085 [82] define the operative procedure. This process involves the drill of samples at various heights and widths across the area being tested and thus, is not always suitable for CH building elements. Indirect methods have been categorized according to measurement principles in resistance, voltage, capacitance, thermal-based, and innovative (e.g., neutron probes, nuclear magnetic resonance, medical ECG electrodes, and fibre optic sensor) methods [80]. Resistance-based moisture methods are widely used, thanks to the variation of the electrical resistance of the materials under different moisture contents [80]. Particularly, this method has been successfully used mainly in timber construction [79,80,83], and, most recently, in solid brick walls [80]. No standards procedures are defined because several factors affect the electrical resistance, such as the timber species, the speed of growth, the origin, and the storage [80,83]. Otherwise, calibration factors exist for different timber species [80]. However, this method has proven to be stable for slow and long-term moisture measurements, with examples of sensors working for a minimum of 20 years [83]. The results obtained in shorter monitoring periods are not accurate.

Herein, a new approach is suggested: to assess in situ the hygrothermal performance of historic walls (aiming at testing future indoor insulation solutions), a new metering hot box is proposed in combination with T-RH sensors (and eventually added thermocouples if desired), through a low cost and simplified data acquisition system [34]. To the best of the authors' knowledge, the new box suggested within the next sections is the first of its kind, totally addressed to historic buildings in situ measurement. Moreover, the developed experiment allows long-term monitoring, against 'punctual' measurement in laboratories or short-time HFM measurements as proposed in [69], not addressed to historic material. Commonly, most studies of this kind and in this field involve the thermal behaviour of walls solely. Alike [56,84,85], the hygrothermal performance assessment is also intended.

#### **4. Case-Study Presentation and Experimental Methodology**

#### *4.1. Contextualization and Configuration of the Tested Wall*

The in situ test was being performed in Palazzo Tassoni Estense in Ferrara, Italy. This 15th century listed building is part of a UNESCO site [86], with characteristics representative of many historic buildings. Since 1997, the Palazzo has been the subject of several studies, which resulted in an architecture project and a scientific restoration intervention [87]. The complex of the Palace is located in the NW part of a block, currently housing almost exclusively the Department of Architecture of the University of Ferrara, near the ancient walls of the city.

In order to provide a proper background, it is opportune to recall "that it was built within the Borso Addition (an area of urban expansion wanted by Borso d'Este, who was then the Duke of Ferrara)" during the mid-15th century, then "confiscated by Ercole I d'Este and gifted to the Tassoni Counts in 1476" [87] (p. 129). By 1491, in a letter to the Duke, "the architect Biagio Rossetti affirmed being in charge of the renovation works of the palace." It "housed the Tassoni Estense family until 1858, when it was designated as the seat of the Provincial Psychiatric Hospital. ( ... ) The mental institution remained active until the 1970s" [87] (p. 129).

The Palace was built in masonry bricks and it has considerable architectural interest, e.g., (i) "the main entrance from the street is made of decorated white marble"; (ii) "the perron, in the upper floor, has been restored and it preserves only partially its original features"; (iii) "the access doors to the main hall are still the original and exquisite renaissance artifacts" [34] (p. 10).

The room (700 m3) and the wall under-study are part of this complex and are located on the ground floor of an area that has not been refurbished yet, currently unoccupied and without any HVAC system (Figures 1 and 2).

**Figure 1.** Ground floor plan of Palazzo Tassoni and location of the room where the experiment is carried out. External views of the surrounding buildings.

Facing the challenge of assessing the hygrothermal behaviour of the historic wall, the authors' option was twofold: (i) conditioning and buffering the openings of a 700 m3 space; or (ii) building an in situ chamber that simulated the conditions of a smaller room that still had as an external boundary, the original historic wall. The authors opted for the second hypothesis, both because of the sustainability of the experiment itself (less energy is required/wasted) either before the risks of the operation (limiting the intervention on the historic building, reduces the risks and impact on the heritage features).

Though the HFM method was probably the most internationally recognized and widely used method, it presented several disadvantages for the intended experiment. On one side, this method suggested high-temperature differences between the indoor and outdoor air (ΔTa). As a matter of fact, "[ ... ] the increase in the measurement temperature difference between the indoor and outdoor environment can weaken the influence of the temperature fluctuation and decrease the test error" [68] (p. 49) but, unfortunately, this difference cannot always be guaranteed in a real field situation (outdoor climate cannot be controlled). On the other side, aiming at the authors' future intention of testing indoor thermal solutions, the RH parameter could not be neglected, and the experience could not be limited to wall U-value measurement.

**Figure 2.** Ground floor plan of Palazzo Tassoni and signaling of the location of the room and wall on which the experiment is carried out (in yellow). Internal views of the room. Inside elevation of the wall (in yellow).

Due to all these premises, field restraints, and the final research goal, an in situ acclimatized box was built, aiming at simulating a 'standard' indoor environment (Ta ≈ 20 ◦C, RH ≈ 55%; in accordance with several standards/guidelines, e.g., EN ISO 7730 [88], EN ISO 13,788 [89], ISO 17772-1 [90]) that potentially guaranteed a satisfying ΔTa between the indoor face of the monitored wall and the external site condition (outdoor climate), 'business as usual' and that always allowed the collection of hygrothermal data of the wall behaviour (and, therefore, using combined T and RH sensors) [34].

#### *4.2. Design of the New Metering Hot Box*

As stated earlier, in order to overcome field experiment restraints, the authors proposed a combined strategy between the in situ monitoring and hot box method to enhance robust measurement and reliable data acquisition. As in [70] (p. 747), the idea was that this simplified solution "[ ... ] avoids the heavy equipment of the hot box method and overcomes outdoor and indoor thermal environment limitation of the HFM Method". Moreover, it was also worth mentioning that often in situ measurement was not done because using the standard method ISO 9869, a measurement period of more than 10 days was normally required [91]. Using the proposed method, the monitoring campaign can be performed almost continuously or with very limited interruption periods.

One of the common characteristics of most metering boxes is their mobile base design. Anticipating future studies on different case studies, risks on the selected room where the monitoring campaign was initially foreseen, or given its own weight (≈700 kg), alike in [92], the newly developed metering box was intentionally provided with wheels thus that it could be more easily moved. Moreover, it was built of a modular timber structure to be more simply dismantled or size adjusted in the event it had to be moved to another room or building with other specifications. This feature, i.e., the box possibility of re-assemblage and re-usability, emphasized the experiment's sustainability.

According to [70] (p. 752), for the SHB-HFM, the minimum box dimension should vary according to the different measurement walls. Considering this, for a wall thickness of 0.30 m it was recommended that the minimum box dimensions were about 1 m: i.e., for a wall thickness of 0.24 m or 0.360 m, for a 'preferred' temperature difference of 25 ◦C, the box dimension may vary between 0.7 m and 1.3 m [70] (Table 3, p. 755).

In the current study, authors have taken into account all these assumptions, but also the specificity of the field of the current and future case-studies: historic buildings, many times characterized by internal volumes with significant high ceilings. For this reason, the newly proposed box had dimensions significantly bigger than the minimum suggested by [70], closer to a climatic chamber than exactly a hot box, aiming at reproducing a fraction of a typical indoor volume of a Palazzo, for example.

The newly developed metering hot box used to perform the hygrothermal tests is depicted in Figure 3. This gross box size is 2.50 × 2.50 × 4.01 m, built with 'platform system' circa 0.13 m thick walls composed by two 0.018 oriented strand board (osb) panels mounted on a timber structure made of elements 0.09 × 0.09 m. To avoid thermal losses and maintain the setup temperature and humidity values, the 5 walls faces (including the pavement) were provided of 0.10 m high-density stone wool insulation material, then protected with a vapor barrier. The net size was 2.04 × 2.42 × 3.55 m (volume of the chamber).

**Figure 3.** Drawing of the metering box (horizontal plan and vertical section). Measurements expressed in meters.

Box dimensions were determined not only by the anticipated study of probable vertical heat stratification common in historic buildings often with high ceilings (see also Section 5.1), but also due to the favoured and anticipated study of at least 2 insulation systems put in parallel, as depicted in Figure 4, alike the experimental study developed by Kloseiko et al. [93]. In [93], the hygrothermal performance of an internally insulated brick wall was studied, with different insulation systems, measuring 1.00 m width each (this way, by placing sensors in the middle, a 0.50 m distance from each material border was assured).

**Figure 4.** Metering hot box positioning (plan). Relation between the box and sensor location if two insulation materials are tested.

#### *4.3. Construction of the Metering Box*

One of the main objectives of the HeLLo project, besides the general scientific final goal to make actors of buildings sector aware of strengths and weaknesses of the most common energy retrofit technical solutions when applied to historic buildings [32], was the development of a very wide and structured program of dissemination The idea was to open the door of the laboratory to other different stakeholders and involve them in the project activities. Among this open Labs program, in the one called SchoolLab activity, in a unique didactical approach, students of the 2nd year of the Degree of Architecture were involved in the activity of the box constructing—Figure A1 in Appendix A unveils some of the steps of the box construction. During this phase, only the 'outer-shell' was executed (the platform frame structure), being later internally coated with 10 cm high-density stone wool, covered by a vapor barrier, Figure 5.

**Figure 5.** Box finishing: Thermal insulation and vapor barrier application.

#### *4.4. Monitoring System of the Metering Box*

Monitoring the hygrothermal behaviour of historic building components was slightly more complex than in existing non-historic ones. As stated in [94] (p. 97), "[ ... ] common mounting systems for long-term surface measurements are risky to original surfaces in historic buildings", e.g., standard installation methods (e.g., adhesive bonds and sensors fixed to walls with holders and/or screws) might damage original surfaces when sensors are later removed. This assumption relates to cultural heritage protection requirements [95] of NDT or methods with the least damages [94].

The metering box was provided with a 2000 W heating convector (with 3 power levels), locally controlled by its own sensor (PID, as described below) and 2 ultrasonic humidifiers, (argo HYDRO digit), 30W/each, self-regulated, which guaranteed indoor air parameters at the desired setup conditions (T ≈ 20 ◦C, RH ≈ 55%), Figure 6.

**Figure 6.** Box indoor hygrothermal control equipment.

In [34], authors presented an innovative measuring method for the hygrothermal assessment of historic walls. In the current study, the same low-cost and conservation compatible technology was also used to control the hygrothermal parameters of the metering box system. The air temperature and RH inside and outside the metering box were controlled by T-RH combined sensors. "These sensors are based on a capacitive polymer RH sensor and a PTA (Proportional to Absolute) integrated temperature sensor (Telaire T9602; Amphenol). They were IP67 certified to guarantee protection in a harsh environment. These sensors used a PDM output signal, and a low pass RC filter was needed to have a voltage signal to acquire hygrothermal data" [34] (p. 7). The sensors of the metering box were connected and managed by a data acquisition system based on a Master Slave configuration.

The initial version of the developed remote sensing technology [34] was upgraded and tuned to fit the current requirements of the HeLLo project. The T-RH measurement system was unchanged, and it was still based on Amphenol probes coupled with an RC lowpass filter, and readout of the analog values was performed by Analog Input Seneca devices with Modbus communication. Old thermostatic heating control was replaced with a more sophisticated Seneca module based on retroactive PID (Proportional, Integrative, Derivative) algorithm and coupled with a triac solid state relay. The temperature probe of the PID module was a PT100 class B. In order to keep the temperature constant inside the metering box, the PID control works on cycles of 120 seconds. Temperature trends were evaluated in terms of temperature integral of previous cycles and heat was activated for a fraction of cycle. The PID module was connected on the same Modbus net and can be configured and monitored by the same software that acquires T-RH values.

The acquisition software was updated, including readout and control of PID modules, and in order to have a configurable number of probes and readout modules, dedicated features were introduced. Substantial updates were done with the main control software. Some of the newly implemented features are listed below:


#### **5. Results and Discussion of the Conditions inside the Box**

#### *5.1. Preliminary Test for the Evaluation of Heat Stratifications*

A simple test was performed to control vertical heat stratification inside the box. Five hand-made thermocouples (TC) with an accuracy of 0.5 ◦C (calibrated in the laboratory), were placed on the surface of the historic wall, between 0.90 m and 3.40 m from the floor to circa 0.50 m from the box boundaries (Figure 7a), during a four-day monitoring period. As shown in Figure 7b, between the highest TC (h = 3.40 m, in black) and the lowest one (h = 0.90 m, in pink) there was an average difference of 4 ◦C. This simple test has confirmed the anticipated heat stratification, common in historic buildings, justifying the height of the box.

**Figure 7.** Results of the simplified test to evaluate the heat stratification inside the box: (**a**) Vertical section of the metering box with the position of the TCs; (**b**) plot of the monitored T value of the five survey points (TC).

#### *5.2. Validation of the Hygrothermal Set-Up*

Figure 8 shows the distribution of the T-RH combined sensors (see Section 4.4) for the monitoring of the following environmental parameters (Ta and RH):


**Figure 8.** Vertical section of the metering box with the position of the T-RH combined sensors.

The validation of the maintenance of the desired setup conditions was reached after the first period of tests and tuning (27 December 2019–10 January 2020). Figure 9 shows a recently monitored two-week period of a very stable indoor environment. Moreover, in the figure, two small peaks can be observed, brief in time and amplitude, corresponding to the moment of maintenance procedures of the monitoring campaign. In other words, the moments when the door of the box was opened, and the conditions of the air inside the box naturally mixed with those of the room. The insignificance of these events can be further observed in detail in Figure 10.

**7HPSHUDWXUH**

**Figure 9.** *Cont*.

#### **5HODWLYH+XPLGLW\**

**Figure 9.** Graphical representation of the monitored parameters [T (◦C) and RH (%)] values between 27 December 2019–10 January 2020.

#### **7HPSHUDWXUH**

**Figure 10.** *Cont*.

#### **5HODWLYH+XPLGLW\**

**Figure 10.** Graphical representation of the monitored parameters [T (◦C) an RH (%)] values on 30 December 2019.

As declared, the door of the box was opened twice during this period, on the 30 December 2019 and 6 January 2020, i.e., the experiment might need to be controlled on-site up to once a week. Nonetheless, this action interferes with almost nothing with the continuity and stability of the indoor conditions. Looking at the most noticeable peak, registered on the 30th December, Figure 10, from the moment the door was opened, the indoor temperature suffered a maximum variation of 2.7 ◦C. Likewise, RH Δmax = 15%.

As shown in Figure 9, the outdoor climate in this winter period is quite varied. The same goes for the conditions of the room where the box is placed, which, as expected, were close to the outdoor conditions, less the thermal influence of the inertia of the historic building envelope. For the entire monitoring period (27 December 2019–10 January 2020), the conditions in the box were definitely stable, average T = 20.7 ◦C, average RH = 56.5%.

#### **6. Conclusions and Outlook**

The new developed device is absolutely disruptive in the field: until this moment, for similar studies, the developed in situ facilities addressed the wall thermal transmittance solely, neglecting the importance of the water vapor permeability factor on the overall wall performance. The feasibility of the new metering hot box has been verified by an in situ measurement for the hygrothermal survey of retrofit wall behaviour in a demonstration MSCA-IF project [32], creating a stable hygrothermal environment by the box. When compared to other boxes the new hot box presents other advantages compared to previous examples:


One other significant advantage could be pointed out: the box re-usability (i.e., enhanced sustainability). As it is provided with wheels, it can be easily moved against another wall in the same room or, more importantly, due to its construction by modules, it can disassemble and used in other case studies. Lastly, it also allows the realization of in situ tests with different settings, for example, 'stress test'.

**Author Contributions:** Authors are listed in alphabetical order. Conceptualization and methodology, M.C., L.D.P., E.L.; validation and formal analysis, M.A., M.C., L.D.P., R.M.; investigation, M.A., M.C., P.D., L.D.P.; data curation M.A., L.D.P., R.M.; supervision P.D.; writing, review, and editing: M.A., M.C., P.D., L.D.P., E.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** The results presented in this paper are part of the HeLLo project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 796712.

**Acknowledgments:** The authors acknowledge *Giorgi Roberto, Lavorazione Legno* and ROCKWOOL® Italia S.p.A. for the use of their materials and the contribution to the execution of the metering box.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

#### **Nomenclature**


#### **Appendix A**

**D E** 

**F G** 

**Figure A1.** Box construction during SchooLab activity with students (HeLLo [32]).

#### **References**


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

### *Article* **Mechanical and Thermal Performance Characterisation of Compressed Earth Blocks**

**Elisabete R. Teixeira <sup>1</sup> , Gilberto Machado 1, Adilson de P. Junior <sup>1</sup> , Christiane Guarnier 2, Jorge Fernandes <sup>1</sup> , Sandra M. Silva <sup>1</sup> and Ricardo Mateus 1,\***


Received: 30 April 2020; Accepted: 2 June 2020; Published: 10 June 2020

**Abstract:** The present research is focused on an experimental investigation to evaluate the mechanical, durability, and thermal performance of compressed earth blocks (CEBs) produced in Portugal. CEBs were analysed in terms of electrical resistivity, ultrasonic pulse velocity, compressive strength, total water absorption, water absorption by capillarity, accelerated erosion test, and thermal transmittance evaluated in a guarded hotbox setup apparatus. Overall, the results showed that compressed earth blocks presented good mechanical and durability properties. Still, they had some issues in terms of porosity due to the particle size distribution of soil used for their production. The compressive strength value obtained was 9 MPa, which is considerably higher than the minimum requirements for compressed earth blocks. Moreover, they presented a heat transfer coefficient of 2.66 W/(m2·K). This heat transfer coefficient means that this type of masonry unit cannot be used in the building envelope without an additional thermal insulation layer but shows that they are suitable to be used in partition walls. Although CEBs have promising characteristics when compared to conventional bricks, results also showed that their proprieties could even be improved if optimisation of the soil mixture is implemented.

**Keywords:** compressed earth blocks (CEBs); compressive strength; durability; guarded hot box; thermal transmittance

#### **1. Introduction**

Earth has been used as a building material since ancient times in several different ways around the world [1–4]. Industrialised building systems and the dissemination of materials, like concrete [5], have replaced earthen construction. Today, earthen construction is associated with poverty [2], and most of this type of construction is located in developing countries. The continuous increase in the energy cost of some building materials (cement and ceramic bricks) and environmental issues are promoting the use of sustainable materials, such as the earthen materials known by their abundance and low-cost production [5–7].

Compressed earth blocks (CEBs) are one of the most widespread earthen building techniques. They represent a modern descendent of the moulded earth block, commonly called as the adobe block [7]. The compaction of earth improves the quality and performance of the blocks [4] but also promotes several environmental, social, and economic benefits [8,9]. Regarding the environmental advantages of using earthen products, a previous study showed that in a cradle-to-gate analysis of

different walls, the use of earthen building elements could result in reducing the potential environmental impacts by about 50% when compared to the use of conventional building elements [2].

Earthen construction is known to undergo rapid deterioration under severe weather conditions [10]. If not built adequately, earthen buildings have lower durability and are more vulnerable to extreme weather conditions and rainfall than conventional buildings. This situation means higher maintenance and repair costs during the life cycle of a building [11].

In the last few years, there has been increasing interest in overcoming the mechanical and durability issues related to earthen blocks. Different stabilisation techniques were used to improve durability and compressive strength [6,11–13]. Dynamic compaction alone or together with chemical stabilisation using several additives has been shown to considerable improve the mechanical performance of CEBs [10,13]. In contrast, compaction increases thermal conductivity [14]. The stabilisation process of raw earth refers to any mechanical, physical, physicochemical, or combined methods that enhance its properties [10]. Bahar et al. [15] studied the effect of several stabilisation methods on mechanical properties. The results showed that the combination of compaction and cement stabilisation is an effective solution for increasing the strength of earth blocks. Amoudi et al. [16,17] developed an experimental program to study the mechanical properties of cement-stabilised earth blocks. They verified that cement in the presence of water forms hydrated products that occupy the voids and wrap the soil particles. That process leads to an improvement in compressive strength, water absorption, dimensional stability, and durability. In many countries, CEBs are stabilised with cement or lime, and there are successful examples of their use in the construction of buildings. Several studies highlighted their lower construction cost, simpler construction processes, and the contribution of this material to maintaining a better indoor environment quality when comparing to the use of conventional building materials [8,11,18–20]. Besides that, some studies show that the addition of lime to compressed earth blocks can improve their mechanical and hydrous properties [21,22].

Regarding the thermal proprieties of earthen building products, since they are massive, they contribute to increasing the thermal inertia of the buildings. This feature can have a positive influence on the thermal performance of buildings in certain climates. A previous study showed that in locations with hot summers and temperate winters, such as the Mediterranean areas, earthen construction could provide comfortable indoor temperature by passive means alone [23]. This property can reduce heating and cooling energy needs and therefore contribute to lower life cycle environmental and financial costs. Nevertheless, compared to the number of studies focusing on mechanical properties, there are fewer studies related to the thermal properties of compressed earth blocks [11]. Adam and Jones [24] measured the thermal conductivity of lime and cement stabilised hollow and massive earth blocks using a guarded hot box method. The authors verified that the thermal conductivity was higher on stabilised blocks (0.20 W/m2·K and 0.50 W/m2·K). The compressive strength used in the compaction of the blocks, the type of soil, and the additives used can significatively influence the thermal conductivity of a CEB building element [24]. For that reason, in the literature, it is possible to find very different thermal conductivity values for earthen products. For example, according to the Portuguese thermal regulation [25], the thermal conductivity to consider for adobe, rammed earth, and compressed earth blocks is 1.10 W/(m2·K). At the same time, other studies show quite different values, also depending on the considered earthen building technique—earth materials with fibres (0.42–0.90 W/(m2·K)), adobe (0.46–0.81 W/(m2·K)), or rammed earth (0.35–0.70 W/(m2·K)) [1,26].

When designing a sustainable building, the design team must have comprehensive information regarding the different building products they can use [19]. Information should include that related to the life cycle environmental (e.g., embodied energy and global warming potential), functional (e.g., mechanical and thermal) and economic (e.g., construction and maintenance cost) performances.

Based on this context, this research is within a series of studies that are being developed by the same authors to develop comprehensive information about earthen construction. Past studies include those related to analysing the contribution of this type of construction in improving the indoor environmental quality [23] and reducing the embodied environmental impacts [2].

The present research is focused on an experimental investigation to evaluate the mechanical, durability and thermal performances of compressed earth blocks produced by a Portuguese company. This study aims to analyse the functional quality of the abovementioned product and assess its potential to be used in the construction of buildings.

#### **2. Materials and Methods**

The compressed earth blocks tested are a commercial product made by a manufacturer located in the city of Serpa, district of Beja (southern Portugal), which is also a contractor that builds earthen and conventional buildings. This contractor is one of the leading earth building systems builders in Portugal. The share of the earthen building systems corresponds to around 12% of the total company's activity, and during the year 2014, the company produced 338 m3 of rammed earth and 36 m3 of compressed earth blocks. Usually, rammed earth is used to build 60-cm-thick walls, and the dimensions of the CEBs produced by this company varies. In this work, 30 cm × 15 cm × 7 cm compressed earth blocks were studied, since it is the most common block produced by the company. Additionally, this size is the most used in the Portuguese construction. The soil mixture is stabilised by using 6% by weight (wt) of hydraulic lime and 1% wt of hydrated lime. The mix also uses water, generally extracted on-site (groundwater) (10% by weight), which evaporates during the drying process. In the majority of cases, earthen building elements are built from soil extracted from the construction site. Additionally, according to the company's data, the compressed earth blocks are made and compacted using a mechanical tapping machine. The company provided compressed earth blocks and the soil used for their production. They were experimentally analysed in different labs of the Department of Civil Engineering of the University of Minho, located in the city of Guimarães, district of Braga (northern Portugal).

#### *2.1. Soil Characterisation*

The soil was characterised in terms of particle size distribution, sand equivalent, clay content, cohesion limits, and compaction properties. These properties evaluate the quality of soil to be used in earthen construction. The particle size distribution was determined according to the EN 196:1966 standard [27]. The main goal of the sand equivalent test is to estimate the percentage of sand that exist in a soil fraction with particles with less than 2 mm. This test was done according to EN 933-8:2002 [28]. The methylene blue test allows the quantification of clay content present in a soil sample through the ionic change between the cations that exist in the soil particles and was done according to EN 933-9:2002 [29]. The cohesion limits of soil are fundamental for the final quality of CEBs. The main goal of this test is assessing the liquid limit (LL), the plastic limit (LP), and the index of plasticity (IP) of the soil. The cohesion limits were determined and calculated according to the EN 143:1969 standard [30].

The compaction properties of soil are fundamental in earthen products since there is a direct relation between dry density and compressive strength of a product. A more compact product presents higher strength. The main goal of the Proctor compaction test consists in analysing the optimum water content. This water content corresponds to the water content of a soil that allows it to achieve its dry density for specific energy of compaction. This test was done according to LNEC E 197:1966 standard [31], considering two types of compaction (light and hard) in a small mould.

Table 1 and Figure 1 summarise the characteristics of the soil used for CEBs production. The soil presented a good particle size distribution and showed the four types of particles in significant percentages (15.9% pebble, 47.2% sand, 17.6% silt, and 19.4% clay). As shown in Table 1, the soil has a liquid limit of 29% and a plasticity index of 11%. Therefore, it can be classified as a fair to poor clayed soil (type A6) according to the American Association of State Highway Transportation Officials (AASHTO) system [32]. However, according to the CRATerre group [33], these figures are within the limits of the recommended classes for soil to be used as a construction material. The methylene blue test shows 2.28 g of methylene blue per 100 g of soil, indicating a low degree of expansion, as also confirmed by the plasticity index, suggesting a low clay content in the studied soil [34]. This result

is good since expansive soils are affected by humidity variations that change its consistency [35]. The analysed soil has a maximum dry density between 1.95 g/cm<sup>3</sup> and 1.99 g/cm3, which means that this soil is classified as "very good" to be used as construction material [33].


**Table 1.** Particle size distribution and Atterberg limits of the soil used.

**Figure 1.** Particle size distribution of the soil, according to the results of the sedimentation test.

#### *2.2. Compressed Earth Blocks Characterisation*

#### 2.2.1. Electrical Resistivity

The electrical resistivity was measured using the ResipodProceq equipment, made by Proceq SA (Schwerzenbach, Switzerland), which comprises four equidistant (38 mm) electrodes (Figure 2). During this test, an alternate current was provided between the external electrodes and the electrical potential difference between the internal electrodes was measured. The electrical resistivity was measured through the Ohm's law and computed by the equipment used. The tested samples were the ones used for the water absorption by capillarity test after they achieved the saturation point. Four measurements were done for each saturated compressed earth block sample.

**Figure 2.** Electric resistivity test: measurements were done in two samples faces—(**a**) width and (**b**) length.

#### 2.2.2. Ultrasonic Pulse Velocity

The Ultrasonic Pulse Velocity (UPV) test evaluates some materials properties, such as elasticity modulus, homogeneity, mechanical resistances, and cracking. It is also possible to calculate the propagation velocity [36]. The UPV tests consists of measuring the time that a given sound pulse takes to pass through a known section of a specimen. This is based on the wave propagation theory, where a sound pulse propagates faster in a dense material and slowly in a porous material. It is therefore possible to calculate the propagation velocity [36], and this test allows the indirect determination of the intrinsic characteristics of a given sample [36]. There is almost nothing in the literature about the use of this test in compressed earth blocks. Nevertheless, there are some studies that have already been performed on rammed earth [37,38] that disclose that there is a relation between the UPV and the compressive strength of earthen products. The UPV measurement was developed according to EN 12504-4:2007 [39] in two directions (direct and indirect—see Figure 3). The measure of UPV in the direct position was obtained with the transmitter and receiver transducers positioned on two opposite sides. The indirect measurements were done by placing the transmitter on one face and the receiver on a perpendicular side. An appropriate coupling gel was applied between the transducers and the sample to prevent the existence of voids in the contact area. Three independent readings were registered for each sample. Equation (1) is used to calculate the UPV, which is the ratio between the distance (*L*) between the transductors (emission and receptor) and the propagation time (*t*).

$$
\Delta IPV\,\left(m/s\right) = \frac{L}{t}\tag{1}
$$

**Figure 3.** Ultrasonic Pulse Velocity (UPV) test—(**a**) direct method and (**b**) indirect method.

#### 2.2.3. Compressive Strength

The compressive strength test used a hydraulic press machine with a capacity of 3000 kN (Figure 4), coupled with a hydraulic control system, according to NP EN 772-1 [40]. For the test, two transducers were used, one that belongs to the press and another external to measure the vertical displacement

(LVSTs). The test used displacement control, with a regular load velocity of 0.5 kN/s. The experiment consisted of applying an increasing compressive load until the load achieved 40% to 50% of the failure value after registering the maximum load peak. Six samples were tested to assess the compressive strength of CEBs.

**Figure 4.** Compressive strength test.

#### 2.2.4. Total Water Absorption

The assessment of the total water absorption of a block is essential since it can be used for routine quality checks, classification according to required durability and structural use, and to estimate the volume of voids [4,41]. Usually, the less water a block absorbs and retains, the better its structural performance and durability. Reducing the total water absorption capacity of a block has often been considered as one way of improving its quality [41].

The total water absorption test consists of immersing a block in the water until no further increase in apparent mass is observed. This experiment followed the LNEC E 394:1993 standard [42]. It is considered that there was no increase in the apparent mass when two consecutive measurements did not differ by more than 0.1% by mass. The test was carried out at atmospheric pressure in which three samples were immersed in water for 1, 2, 3, 4, and 5 h. After each period, the surface of the specimen was wiped with a cloth to remove any adsorbed water. Then the samples were weighed. Initially, the test was done for 24 h, as recommended by the standard and observed in other studies [4,41]. However, after 24 h immersion in water, the CEBs disintegrated, and it was not possible to measure its wet weight. The percentage of water absorbed (*A*) was calculated using Equation (2). *Wh* is the weight of the specimen after each period of immersion, and *Ws* is the dry block weight.

$$A\left(\%\right) = \frac{\left(\mathcal{W}\_{\text{li}} - \mathcal{W}\_{\text{s}}\right)}{\mathcal{W}\_{\text{s}}}\tag{2}$$

#### 2.2.5. Water Absorption by Capillarity

This test consists of quantifying the amount of water absorbed by capillarity in the compressed earth block. The experiment was performed in three entire blocks following the LNEC E 393:1993 standard [43]. This is a Portuguese standard for analysing the water absorption in concrete, and it was used since the procedure is similar to the international standards specific for earthen products [10,40,44]. Before being immersed, each specimen dried for 14 days in an oven at a controlled temperature of 60 ± 5 ◦C. In the following step, each sample was weighed with a precision of 0.1 g, and then its lower face was immersed in a 5 mm water bath. Samples were left in the bath for 10, 20, 30, 45, 60, and 90 min, and 2, 3, 4, 6, 24, and 72 h, to identify the water saturation point (Figure 5). The water absorption by capillarity coefficient, *Cb*, was calculated for 10 min, according to UNE 41410:2008 [44] and using Equation (3).

$$C\_b = \frac{100 \times (M\_1 - M\_0)}{\text{S } \sqrt{t}} \tag{3}$$

where

*Cb* is the water absorption by capillarity coefficient (g/cm2·min0.5); *M*<sup>1</sup> is the weight of the block after immersion in water (g); *M*<sup>0</sup> is the weight of the block before immersion in water (g); *S* is the immersed area (cm2); *t* is the immersion time (min).

**Figure 5.** Water absorption by capillarity test: (**a**) after 10 min of water immersion; (**b**) after 30 min of water immersion; (**c**) after 45 min of water immersion; and (**d**) after 60 min of water immersion.

#### 2.2.6. Accelerated Erosion

This test analyses the degradation process of a specimen caused by water falling on it. This experiment verifies the surface resistance to erosion, thus evaluating the durability of the analysed blocks. The test was performed according to NZS 4298:1998 [39], and a rain simulator was used (Figure 6). The climate parameters used were the ones for Penhas Douradas region, Guarda district since it is the Portuguese region with the highest precipitation values (1715 mm). In the experiment, a direct rainfall exposure index (worst scenario) was considered, which means that a flow rate of 14.26 L/min was used in the rainfall simulation. The outlet pressure in the water nozzle was 45 kPa, respecting the conditions recommended for erosion tests in the international standards.

**Figure 6.** Accelerated erosion test: (**a**) setup; (**b**) sample preparation; (**c**) and (**d**) sample test.

#### 2.2.7. Thermal Transmittance

The characterisation of the thermal properties of the CEBs is based on the analysis of the thermal transmittance (U-value) of the product. The thermal transmittance was measured using a guarded hot box set up apparatus, built for this study in the Department of Civil Engineering of the University of Minho, according to ASTM C1363-11:2011 [45] (Figure 7a). The hot box consists of two five-sided chambers (dimensions: 2.0 m × 1.4 m × 1.6 m), the cold and the hot one. The envelope is well insulated, made of extruded polystyrene (20 cm; U <sup>=</sup> 0.21 W/(m2·K)), to reduce the heat flux through the envelope and minimise heat losses by conduction. The specimen is placed in the mounting ring placed between the two chambers (Figure 7b). The setup is placed in an indoor environment with a controlled temperature below to the ones in the measurement chambers.

The thermal transmittance of the sample is obtained by measuring the heat flux rate needed to maintain the hot chamber at a steady temperature (in this study 35 ± 5 ◦C). Two ventilation devices were placed in the back wall of the cold chamber (Figure 7a), which allow the cold air to enter into the chamber and the hot air to exit. The ventilation is necessary to maintain uniform heat flux conditions through the specimen. In the hot chamber, there is a heating system, controlled by a temperature controller, that controls the defined temperature. The temperature in the chambers was measured by four thermocouples (two in each chamber—one in the middle of the chamber, and the other near the sample). A heat flux sensor was installed in the centre of the sample (Figure 7c). Preliminary calibration measurements were carried out successfully to evaluate the heat losses and the heat transfer through a wall with known thermal transmittance.

In this study, the heat flux method was used to determine the U-value. There is a heat flux through a material when there is a temperature difference between two sides. Heat flows from the warmer side to the colder side. It is possible to calculate the U-value of a specimen using the standardised methodology of ISO 9869-1:2014 [46] by assessing the heat flux together with the temperatures in both chambers. In this experiment, the greenTEG gSKIN® U-Value Kit (KIT-2615C) was used to automatically quantify the temperatures, the heat flux through the material and the U-value. The U-value is obtained from the average values of the heat flux through a small CEBs wall sample (composed of three blocks) and the temperature difference, Δ*T*, between the chambers, using Equation (4). In this experiment, the heat flux was assessed in two points of the CEBs wall.

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**Figure 7.** Hot box apparatus. Legend: (**a**) hotbox closed; (**b**) cold chamber, mounting ring, and hot chamber; (**c**) longitudinal plan view of the hotbox apparatus and position of the measurement equipment.

$$\Delta I - value = \frac{\sum\_{j=1}^{n} \varphi\_{j}}{\sum\_{j=1}^{n} \Delta T\_{j}} \Big( \mathcal{W} / (m^{2} \cdot \mathcal{K}) \Big), \tag{4}$$

where

*n* is the total number of data points;

ϕ is the heat flux in (W/m2);

Δ*T* is the temperature (◦C) difference between the two sides of the specimen.

#### **3. Results**

In this section, the mechanical, durability, and thermal characterisation of the CEBs will be described and discussed.

#### *3.1. Electrical Resistivity*

Electrical resistivity was measured to analyse the porosity of CEBs, and the results are presented in Figure 8. CEBs have electrical conductivity mainly because ions can propagate in their body. Electrical resistivity is directly dependent on CEBs permeability. In water-saturated CEBs with higher porosity, the propagation of ions is easier, and therefore, there is a lower electrical resistivity [47]. From Figure 8, it is possible to conclude that the measurements done in the direction of the bigger dimension (length) of the sample showed similar results for all samples. However, in the measurements carried out in the other direction (width), sample 2 presented higher values than samples 1 and 3. This result can be an indication that sample 2 was denser, with fewer pores or with pores with smaller dimensions (meaning reduced permeability and conductibility). These outputs disclose some disparity between the porosity of tested CEB samples.

**Figure 8.** Results of the electrical resistivity tests of the three specimens carried out in the two directions.

#### *3.2. Ultrasonic Pulse Velocity*

Figure 9 presents the results for the UPV for each sample, using the same samples used in the electrical resistivity test. Five measurements were carried out for every sample (for each direction, direct and indirect) and results presented in Figure 9 are the average of the results obtained for each sample.

**Figure 9.** Ultrasonic Pulse Velocity measurements.

A sonic impulse propagates with lower velocity in a porous body and with higher velocity in a denser one. Therefore, according to the analysis of the results, it is possible to conclude that sample 2 presented slightly lower UPV, which can be an indication of a higher number of voids. These results are similar to the electrical resistivity test results. The sample performance differences could be due to the incorrect homogenisation of the soil mixture and/or cracking.

#### *3.3. Compressive Strength*

The compressive strength test is considered a reference test for CEBs since it is regarded as an essential indicator of masonry strength. Figure 10 presents the results obtained for the compressive strength of six samples, as well as the average value obtained for this parameter. The results showed a variation on compressive strength between 7.8 MPa and 11.0 MPa, being the average 9.0 ± 1.3 MPa. These values are very good ones since it is known that the minimum compressive strength requirements for CEBs, varying between 1.0 MPa and 2.8 MPa [7,21]. These higher values can be related to the compaction process used since compacting the soil using a press improves the quality of the material. The higher density obtained by compaction significantly increases the compressive strength of the blocks [48]. Another reason for the higher compressive strength is the presence of lime in the mixture. Lime allows the development of calcium silicate hydrate (CSH) together with the formation of minor amounts of calcite, which causes increased strength [21].

**Figure 10.** Compressive strength results.

#### *3.4. Total Water Absorption*

Analysing Figure 11, the maximum values obtained for the total water absorption for each sample were 8.7%, 11.3%, and 10.0% for samples 1, 2, and 3, respectively. It is possible to conclude that the total water absorption varied between 8.7 and 11.3%, being these values favourable when compared with clay bricks (0–30%), concrete blocks (4–25%), or calcium silicate bricks (6–16%) [4]. Although this result seems good, the fast absorption and desegregation of blocks can influence the durability negatively. Total water absorption is influenced by the granulometry of the soil and compaction pressure. These two aspects have a meaningful impact in the density, mechanical strength, compressibility, permeability, and porosity of CEBs [4,48].

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**Figure 11.** Total water absorption results. The results highlighted by the orange box represents the maximum values obtained in the total water absorption test.

#### *3.5. Water Absorption by Capillarity*

CEBs used in a structure may undergo alternating phenomena of absorption and release of water, mainly because of the capillarity effect [49]. The curves shown in Figure 12 illustrate the variation of water absorption by capillarity until the total saturation of each sample is reached, as a function of the square root of test time. Figure 13 shows the variation of the water absorption coefficient (average of the three samples tested) of compressed earth blocks as a function of the square root of test time. This coefficient was determined for all test periods. However, the literature mentions that the value at the end of 10 min (represented with an orange rectangle in Figure 13) is representative of the behaviour of masonry exposed to a violent storm [21]. At the end of this period, the water absorption coefficient value was 34.6, and therefore, the blocks are classified as having high capillarity [21]. This result can be related to the fact that the soils used to manufacture the CEBs have a high percentage of sand (Table 1). In this study, the high presence of bigger particles (in terms of size) in CEBs seems to be an issue related with the quality of the particle size distribution of the soil, used for the CEBs production, than the quality of the soil itself, which did not cause good reaction with lime and resulted in CEBs with high porosity [10].

**Figure 12.** Total water absorption results.

**Figure 13.** Variation of the average water absorption during the test period.

#### *3.6. Accelerated Erosion*

The degradation analysis of CEBs was done, and the results are presented in Figures 14–16. A total of seven blocks were analysed in this test. Initially, three blocks were tested for one hour. Since blocks did not present any type of erosion, this test was repeated in four additional blocks, and the results were the same. Based on these results and according to NP EN 12504-4 [39], CEBs were classified with an erosion index of 1 (erosion depth between 0–20 mm/h), which means that they had very good results in terms of durability.

To understand how these blocks behave if exposed for more time to the erosion test and if there is any relation with the other physical properties mentioned before, the analysis was extended for one additional hour.

$$\mathbf{(a)}\tag{b}$$

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**Figure 14.** Accelerated erosion test. (**a**,**c**,**e**,**g**)—four sides of the exposed face of sample 1 before the test; (**b**,**d**,**f**,**h**)—four sides of the exposed face of sample 1 after two hours of testing.

**Figure 15.** Accelerated erosion test. (**a**,**c**,**e**,**g**)—four sides of the exposed face of sample 3 before the test; (**b**,**d**,**f**,**h**)—four sides of exposure face of sample 3 after two hours testing.

**ǻǼ ǻǼ**

**Figure 16.** Accelerated erosion test. (**a**,**c**,**e**,**g**)—four sides of the exposed face of sample 6 before the test; (**b**,**d**,**f**,**h**)—four sides of the exposed face of sample 6 after testing.

After two hours of water exposition, CEBs presented different behaviour. Sample 1 did not show significant damage in the majority of faces exposed to water but presented a loss in one part of the block where there was already a small defect before the test (Figure 14). Sample 2 showed similar behaviour to sample 1. Samples 3 (Figure 15), 4 and 5 presented some damage in the two smaller faces, even though the face in contact with the water was the one with a larger area (highlighted with a red cross in Figure 14). Samples 6 (Figure 16) and 7 suffered significant damage, and at the end of two hours, they were almost destroyed. It is then essential to analyse the reasoning behind the different deterioration levels of CEBs since they were manufactured using the same soil, mixture, and compaction process. The most probable explanation for the differences is the inadequate particle size distribution in the soil used. According to the literature in the field of earthen blocks, the soil should only present particles below 5 mm of diameter [48]. Nevertheless, it was possible to see particles with higher dimensions in the most damaged samples (Figure 16d,f,h). These big size particles affected the homogeneity of the mixture, which negatively influenced the porosity and the porous structure of the CEBs. The lack of uniformity in the structure of the blocks worsens their behaviour to water. This problem also explains the results achieved in the total water absorption and water absorption by capillarity tests. The presented porosity and durability issues can be minimised if proper soil preparation and/or selection is considered [18].

#### *3.7. Thermal Transmittance*

Regarding the thermal transmittance, Figure 17 shows the measurement results for the XPS wall (Figure 17a) and the compressed earth blocks small wall (Figure 17b,c). From the analysis of Figure 17, it is possible to verify that the values measured for temperature and the heat flux were very stable during the test period, in both cases. The temperature in the hot chamber practically did not change since the heat input to the box was controlled so that the temperature established was maintained (35 ◦C). The heating system turns on when the temperature drops below 35 ◦C and turns off when the temperature rises to 40 ◦C. The heat flux is presented as negative values due to heat flux sensor placement in the sample.

The results of the preliminary calibration measurements carried out, using a reference sample with known thermal transmittance (XPS 20 cm), showed an agreement with the technical data provided by the manufacturer (U-value of 0.21 W/(m2·K) and thermal conductivity of 0.60 W/(m·K). The measured thermal transmittance of the 15 cm CEBs wall was of 2.65 <sup>±</sup> 0.16 W/(m2·K) (thermal resistance of 0.21 (m2·K)/W on average).

The results indicate that the thermal conductivity of the CEBs studied is significantly lower than the reference values (1.1 W/(m·K)) listed by the Portuguese National Laboratory of Civil Engineering (LNEC) [25]. Considering the thickness of the CEBs wall analysed (15 cm), the thermal transmittance of the CEB wall would be approximately 3.26 W/(m2·K) (thermal resistance of 0.31 (m2·K)/W)) (for external walls). The thermal transmittance measured for the CEB wall sample was lower than the value obtained from technical data of LNEC. This result can be explained due to a higher porosity of these blocks, which increased their thermal resistance. This result is in accordance with the other analysis made in those blocks; as was seen in the accelerated erosion test, the CEBs presented a different soil composition, showing in some samples soil particles with a size larger than 5 mm, which leads to a higher thermal conductivity (Figure 16). Moreover, the results could be better if the granulometry of the soil was optimised, since in the experiments found soil particles larger than 5 mm and some small rocks that increase thermal transmittance.

**Figure 17.** Measurements of the heat flux, hot and cold superficial temperatures and U-value in the XPS wall (**a**) and in the small CEBs wall (**b**,**c**).

#### **4. Discussion**

The compressed earth blocks characterisation is summarised in Table 2. Overall, the results are consistent and show that these blocks presented good mechanical and durability properties and better thermal performance than the reference values listed in the technical data for Portugal, but they also present porosity issues. The non-destructive (electrical resistivity and ultrasonic pulse velocity) and the destructive (water absorption) tests for porosity analysis showed similar results. CEBs samples presented heterogeneity on their mixture composition, which led to the production of blocks with

different porosities. The high values obtained in the water absorption test highlight that this is the most problematic characteristic of the CEBs. Contrary, to the other results, the compressive strength analysis and the accelerated erosion test presented significant results. The compressive strength obtained was approximately three times higher than the minimum requirement for CEBs. Moreover, these CEBs were classified as erosion index of 1, which means that they have an erosion depth between 0–20 mm/h, are very resistant, and have good durability properties.


**Table 2.** Thermophysical proprieties of the analysed compressed earth blocks.

One of the essential features of these building elements is thermal resistance. In this study, thermal transmittance was analysed, corresponding to a U-value of 2.66 W/(m2·K). The results seem to be in accordance with the results obtained for the quality and durability parameters, showing that the results could be better if the granulometry of the soil was optimised, since in the experiments found soil particles larger than 5 mm and some small rocks that increase thermal transmittance. Therefore, the optimisation of the soil particle size distribution in the mixture before CEB production is necessary to increase thermal performance while maintaining high mechanical resistance.

#### **5. Conclusions**

The results presented in this study show a strong relationship between soil and mixture preparation and compaction with CEB properties. During the investigation, it was possible to observe that the use of soil with particles with higher dimensions than the ones recommended by the international standards for CEB production had a significant effect in some of CEBs properties, such as: porosity, water absorption, durability and thermal performance. However, even though these blocks did not have the proper production, they did not present mechanical resistance issues. In general, the analysed CEBs are adequate to be used for construction of partition walls. Moreover, it was seen in the literature that these blocks presented several benefits in terms of environmental performance. Taking this into account, optimising the soil particle size distribution in the mixture before CEB production could be a solution to minimise these issues (mainly in thermal performance) while maintaining high mechanical resistance. The optimisation of the distribution will lead to a production of elements with lower porosity, and it is known that porosity has a direct relation to mechanical resistance and durability. However, by reducing porosity, it is expected that thermal transmittance increases. The study of CEB optimisation and the characterisation of the optimised CEB properties will be studied in the future in order to assess the real contribution of this optimisation in CEB mechanical, durability, and thermal performance. In short, optimisation of compressed earth blocks is necessary to improve their functional quality and increase their potential for use in the construction of buildings.

**Author Contributions:** Conceptualization, E.R.T., J.F., and R.M.; methodology, E.R.T., S.M.S., and R.M.; validation, S.M.S. and R.M.; formal analysis, E.R.T. and J.F.; investigation, E.R.T., G.M., A.d.P.J., and C.G.; resources, R.M.; writing—original draft preparation, E.R.T.; writing—review and editing, E.R.T., A.d.P.J., C.G., J.F., S.M.S., and R.M.; supervision, S.M.S. and R.M.; project administration, R.M.; funding acquisition, R.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors would like to acknowledge the support granted by the FEDER funds through the Competitively and Internationalization Operational Programme (POCI) and by national funds through FCT (the Foundation for Science and Technology) within the scope of the project with the reference POCI-01-0145-FEDER-029328, and of the Ph.D. grant with the reference PD/BD/113641/2015, which were fundamental for the development of this study.

**Acknowledgments:** The authors would like to acknowledge the support granted by DANOSA "*Derivados asfálticos normalizados, S.A.*" industry for the hotbox construction by providing all the necessary insulation material.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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#### *Article*
