TRNSYS

TRNSYS (Transient system simulation program) [92] is a program with a modular structure that implements a component-based approach. Its components extend from simulating a single pump or pipe to a multi-zonal building model. Its components assemble in a fully integrated visual interface called TRNSYS Simulation Studio, while building input data are entered through a dedicated visual interface (TRNBuild). The simulation engine then solves the algebraic system of the discretized differential conservation equations consisting of the energy system. HVAC system components are solved simultaneously with heat conservation through the building envelope and the air network at each time step. In addition, the TRNSYS library includes components for solar thermal and photovoltaic systems, low-energy buildings, HVAC systems, renewable energy systems, cogeneration, fuel cells, etc. The modular nature of TRNSYS facilitates the compilation and integration of new mathematical models to the program regarding, for example, walls' boundary conditions, systems' properties, and operation schedules. It presents high flexibility and compatibility with other software (e.g., Matlab/Simulink, Excel/VBA) for co-simulation, optimization, and optimal control purposes. TRNSYS can generate redistributable applications that allow less-skilled users to run simulations and parametric studies. It has been widely used and tested for whole building energy simulations for more than 20 years. It exhibits perhaps the highest sophistication regarding modelling of solar radiation passing through windows since it considers variable optical properties with incidence angle and in terms of treatment of direct and diffuse solar radiation distribution into a zone [62].

Ibanez et al. [93] used TRNSYS to simulate the impact of Phase Change Materials (PCM) integrated into walls, ceiling, and floor of an experimental room built with concrete panels with PCM, on the whole building energy balance. An acceptable agreement between the simulated and experimental results was obtained. Beausoleil-Morrison et al. [94] developed an ESP-r/TRNSYS co-simulator, which was applied for evaluating the performance of a solar-thermal system in a low-energy building. The suggested co-simulation environment proved to be an effective tool for designing solar buildings, particularly when architectural, energy conversion, and storage systems are all integrated. The software has been also used to present and compare a series of passive and active measures for energy upgrading of various building types (educational, museum, sports facility, Municipal Office building, and a residential, detached building) in a typical Mediterranean climate [95]. In

such climatic conditions, Pérez-Andreu et al. [96] applied TRNSYS to study the benefits of passive construction measures in a typical Mediterranean dwelling, in terms of energy consumption and thermal comfort, taking into account site wind and occupants' behavioral conditions. Validation and model-calibration processes revealed excellent agreement between simulated and actual (measured) data referring to indoor monthly averaged air temperature and relative humidity.

### *2.2. Urban Microclimate Modelling*

The global trend towards urbanization in parallel with climate-change implications justifies the growing interest in the study of combating adverse effects of extreme microclimate conditions on urban activities relating to building energy consumption and health. The Urban Heat Island (UHI) effect presented evermore high intensities during the last 10 years, which significantly impacted pedestrians' thermal comfort and perception of air quality as well as energy demand of buildings in dense urban environments. Landsberg [97] states that the UHI phenomenon is the most obvious climatic manifestation of urbanization. Indeed, numerous studies in the scientific literature have highlighted the adverse effects of urban extreme microclimates, especially UHI, on building energy demand and consumption as well as thermal comfort and well-being [98–100]. In accordance with the scientific evidence, the European Commission indicated the requirement to account for local climate, especially in developing strategies to meet the Nearly-Zero Energy Building (NZEB) goal (refer, for example, to its 2012 release "Evaluating and Modelling Near-Zero Energy Buildings: are we ready for 2018?" [101]). Considering the latest research findings as well as trends in energy policies that necessitate building energy design with accurately predicted performance indicators, building simulation techniques, taking into account the external microclimate effects, should no longer be considered as "for research purposes only" and move to the practitioner level at the early design stages. Accepting the suggestion that in modern case studies indoor and outdoor physical effects are inseparable, this paper extends the review to include basic computational methods and tools for quantifying urban microclimate effects. The present section reviews the methods and popular computational tools that can be used to quantify the physical variables comprising urban microclimate (mainly by means of its UHI manifestation) in open spaces, such as wind speed, temperature, and relative humidity, including thermal comfort indicators of pedestrians.

The Urban Heat Island effect is related to higher urban temperatures in city centres compared to the surrounding rural or suburban areas [102]. This situation emanates from anthropogenic heat sources, e.g., vehicles, power plants, air-condition units, etc., as well as by other heat stresses produced by the use of ground or building materials of poor thermal behaviour and the lack of heat sinks (e.g., water surfaces) and of vegetation [103]. Fundamental causes of the UHI were indicated by Oke [104] and their relative importance was further validated in numerous follow-up studies:


Studies of the UHI are usually focused on the so-called heat island intensity, which is the maximum temperature difference between the city and the surrounding rural or suburban area. The intensity is mainly determined by the heat conservation of the region and is, therefore, subject to diurnal variations and short-term weather conditions [105,106]. There are two major simulation methods often used to assess UHI [107]:


In the following subsections, the background of the simulation methods and a comparative analysis between them is discussed, while the most popular computational tools of each method are briefly described in terms of their strengths and weaknesses.

#### 2.2.1. Energy Balance Models

The energy-balance (or urban energy-budget) concept was first suggested by Oke [104]. This method adopts the principle of energy conservation for a given control volume, and manipulates the wind-induced phenomena, i.e., turbulence and velocity fields, as simple heat fluxes. These fluxes are generally defined by analytical or empirical equations. In the last two decades the energy-budget concept has been enhanced to the so-called Urban Canopy Model (UCM), which is derived from the energy balance equation for a control volume containing two adjacent buildings. The model considers the energy exchanges between solid surfaces of the domain and the urban canopy and predicts the ambient temperature and solid-surfaces' temperature of the urban fabric components. However, the airflow is decoupled from the temperature field, being treated as a separate input into the control volume. For this purpose, the logarithmic or the power law [16] is widely used in order to represent airflow in the domain of interest. In the UCM approach, all surfaces and control volumes are interconnected by means of an electrical analogue. The energy conservation equation [107] is then applied to each node, thus being discretized to an algebraic system comprised of matrices of temperature and humidity coefficients. An iterative solution of the system provides the temperature and relative humidity distributions throughout the domain. One-layer [108] and multiple-layer [109] schemes depend on the nodes' number on the building walls, while such models can be also developed into one to three dimensions. This approach is fast, in general, as it treats building canopies with a low number of nodes. It provides acceptable predictions but mainly in large-scale cityscapes.

The omission of an air velocity pattern represents the major drawback of UCM models. Indeed, the resolution of the air velocity field facilitates the study of special airflow effects, e.g., eddy circulation and dissipation, wake regions, and turbulence intensity, and of the atmospheric phenomena (e.g., precipitation and stratification), towards the determination of heat fluxes' components. The consequent approximation of heat fluxes using empirical correlations in UCM models rarely captivate the interaction between velocity and temperature fields. Provided that data for three-dimensional geometries of building canopies and urban structures correspond to high computer loads, the urban complex is often represented by homogeneous columns as building boxes. Cityscape geometry is also approximated with coarse grids on ground, roofs, and walls, hence, weakening the reliability of the energy-conservation solution, especially when the focus is on pedestrians' thermal comfort.

#### 2.2.2. Computational Fluid Dynamics

Unlike the energy-balance models, CFD simultaneously solves all the governing equations of airflow within the urban fabric, i.e., conservation equations of mass, momentum, thermal energy, chemical species, and turbulence parameters for single- and multi-phase flow phenomena. As a result, CFD can produce more accurate information about the UHI effect within and above building canopies compared to the energy budget models. Consideration of complex details in addition to complicated atmospheric interactions of the cityscape is, nonetheless, both a computational and theoretical challenge. The former refers to the high number of the computational nodes to simulate the airflow, while the latter is related to the unmatched temporal and spatial resolution of the physical mechanisms occurring within the cityscape. For example, turbulence length scales within and above the canopy differ significantly; thus, they cannot be modelled in the same scale. This suggests the division of the CFD simulation into different scales for UHI studies [107]: Meso-scale and Micro-scale (within the urban canopy).

Meso-scale models present horizontal resolutions ranging from one to several hundreds of kilometres. Vertically, they vary with the depth of the so-called Planetary Boundary Layer (PBL) (the layer between the earth surface and geostrophic wind), i.e., in between 200 m and 2 km [107]. In such models, large-scale interactions under the PBL are analysed, involving treatment of the atmospheric stratification and surface layer. In this approach, the atmospheric stratification is resolved by adopting either the hydrostatic or the nonhydrostatic assumption in the Navier–Stokes equations. The hydrostatic assumption refers to a simplified motion equation in the vertical axis in terms of a balanced correlation between the buoyancy and the pressure term. On the other hand, the non-hydrostatic assumption refers to the full Navier–Stokes equation in the vertical axis. Meteorological schemes mostly use Monin–Obukhov or other similarity schemes to model the surface sublayer [110], and building canopies are simulated by means of aerodynamic roughness. This means meso-scale models manipulate the complex phenomena within the urban canopy only by a roughness value. Consequently, information about variations of dependent variables within the canopy layer is extremely limited. However, this simplification facilitates the understanding of physical phenomena (for instance, surface drag, shear stress) at least within the urban surface layer but above the canopy layer. The precision of meso-scale modelling is strongly dependent on the available land-use parameters. Detailed information of solid surfaces at micro-scale level (e.g., thermo-physical properties, geometry, optical properties) is rarely available for the entire urban area of interest. Even in the contrary case, applying these details to a meso-scale model increases the required computational resources. Since the spatial resolution is in the order of a few kilometres, it is also necessary to assume a meso-scale zone as a homogeneous area and estimate the surface properties with bulk values, e.g., albedo, emissivity, and roughness.

On the other hand, micro-scale CFD resolves the conservation equation inside the canopy layer. In the meso-scale layer, the horizontal spatial quantities are usually accounted for as homogeneous values, while the quantities within the actual geometry are simulated in detail, taking into account surface physical interactions in the micro-scale layer. These interactions are generally represented by the Monin–Obukhov similarity theory to represent the PBL in meso-scale layers. Obviously, it is not realistic to apply micro-scale modelling for an entire city, with all geometric details, due to the high computational cost. Therefore, the common approach is to limit the simulation into a small domain in the magnitude of some blocks of buildings (few hundreds of meters), as done, for example, by Stavrakakis et al. [103]. On the other hand, the treatment of the PBL in a micro-scale model is not as comprehensive as in the meso-scale model, which means that micro-scale modelling does not account for atmospheric interactions such as vertical mixing or Coriolis effect. Observational schemes [107] can significantly improve the aforementioned limitations. However, providing boundary conditions in the micro-scale model is even more complicated than in the meso-scale model. In micro-scale modelling more measurements are necessary due to high fluctuations of airflow quantities near surfaces. Although the assumptions of a homogeneous boundary layer [111] and corresponding boundary conditions [103] may be adopted, these approaches are physically weak considering the stochastic nature of airflow velocity and the variety of height and geometry of buildings. Similar to the meso-scale modelling, the treatment of turbulent closure and radiation significantly affects the precision of the micro-scale model prediction.

As far as turbulence modelling is concerned, many theories have been proposed, such as the Direct Navier–Stokes (DNS) simulation, Large Eddy Simulation (LES), and Reynolds Averaged Navier–Stokes (RANS) [16]. Although the precision can be improved using LES and DNS, the application of these schemes is very demanding in terms of CPU resources. On the other hand, RANS models (such as the Standard k-ε model or its modifications [112]) are widely used for turbulence modelling in UHI studies as their requirements for computational resources are moderate in comparison to LES and DNS. However, it should be mentioned that RANS modelling provides limited representation of physical phenomena such as the so-called "horse-shoe vortex" around buildings [113]. This implies that accurate modelling of turbulence phenomena is still one of the weakest points of RANS modelling. Additionally, the size scale of the case considered substantially affects RANS modelling as it is related to the turbulence-length scale i.e., the size of the large energy-containing eddies in the turbulent layer.

### 2.2.3. Collation of Urban Microclimate Modelling Methods

Table 3 contrasts the capabilities of UHI study methods by means of the governing equations, limitations, domain-size restrictions, resolution in time and space, and computational cost. It becomes obvious that the meso-scale method is practical when urban surface details are less important, i.e., heat transfer at the urban scale, pollutant dispersion, and thermal comfort are not adequately assessed by this method. On the contrary, for cases that such information is required, meaning that the physical phenomena within the urban canopy are of interest, micro-scale CFD or UCM methods are more useful. It should be pointed out, however, that when CFD models are applied in near real-time and -size manner, small time steps and detailed geometries may be prohibitive due to extremely high computational costs for simulations of whole cityscapes. This implies that major assumptions should often be adopted in order to produce realistic results, at least for practical engineering purposes. The most common assumptions followed when micro-scale CFD models were applied for UHI assessments are:



**Table 3.** Collation of major UHI simulation methods.

Although these assumptions may cause deviations of predictions in comparison with measurements (if available), it has been extensively demonstrated in simulated, measured data comparative studies in real-scale cases that the produced deviations (even when applying the RANS model) are considered acceptable at least for practical engineering purposes [114–116]. It has been pointed out, however, that it is still a research challenge to bridge the gap between micro-scale and meso-scale modelling techniques [117] towards perhaps integrated models utilizing the respective benefits of highresolution analysis and large urban scales, in order to achieve more accurate predictions at simulation environments.
