*3.1. Building Energy/Urban Microclimate-Coupled Simulations*

As presented in the above sections, currently there is a tremendous availability of computational tools and methods that can be used to conduct urban energy planning studies, even in completely simulated environments. The obvious opportunity that emerged is the ability to predict the energy performance of a group of buildings, taking into account microclimate variations in the vicinity of buildings, at least at a district level. Apparently, the designer may have all the necessary computer tools to conduct joint simulations of urban microclimate and building(s) energy performance, which, however, requires knowledge of building physics, specifically regarding indoor–outdoor interactions. The main question is how the practitioner can really develop such kind of co-simulations. The answer, of course, simply resides on the energy conservation of the control system building/outdoor space. The energy balance equation for a building may be expressed as follows: The heating/cooling load of the building equals the sum of the internal heat gain from lights, occupants, equipment, the convective heat transfer between building's interior surfaces and internal air, and the convective heat transfer due to air infiltration and the change of energy stored in the internal air. On the other hand, the energy balance equation for building exterior surfaces may be expressed as follows: The conduction heat flux through the wall equals the sum of the transmitted solar radiation, the absorbed solar radiation, the net long-wave radiation heat flux, and the convective heat flux exchanged with the outdoor air.

The above description of the heat exchange between indoor and outdoor spaces reveals the physical influences of the external environment to the internal space and vice versa. These influences may be described as follows:


Ideally, all the above influences should be adequately captured and participate in appropriate boundary conditions of the building energy simulation (BES) model. The last, however, often present some deficiencies in capturing all the impacts described above, such as the following:


On the other hand, as presented in previous sections, the UCM or CFD tools seem very promising towards the simulation of the urban microclimate. The CFD micro-scale models can simulate physical mechanisms that comprise the urban microclimate and by these means they can quantify all the influences of outdoor physical environment to indoor energy consumption. Consequently, the drawbacks reported above can be eliminated under the perspective of CFD/BES tools' coupling. Indeed, numerous authors in scientific literature succeeded to couple these methods based on information exchanging between the two tools in each given time interval as follows [55–57,155]:

• An initial value of external wall temperature in the CFD model is adopted as a wall boundary condition. Air properties of the incoming wind are taken from the nearest meteorological station and they are set as inflow boundary condition in the CFD model. Boundary conditions for physical features, such as trees and water surfaces, are also set as boundary conditions.


The iterative process above ends when the wall temperature computed by the BES tool, taking into account its pass from the CFD tool, presents a really small change from one loop to the other (convergence of solution). Then the solution is obtained and the building energy-related indicators are finally calculated.

As stated by Kato [21], the full coupling is practically absurd and sometimes impossible because of its enormous computation amount, especially when similarly small time-step scales over long periods are adopted in the two models. Alternatively, he suggests a coupled CFD network model in building energy (heat) and airflow simulation. However, the suggested approach again requires quite advanced knowledge of transport phenomena and computer skills; hence, again it may be considered difficult to use by practitioners, especially professionals conducting studies for compliance purposes with regulations, e.g., energy audits or energy studies for new or renovated buildings. Focusing on that target audience, an alternative practical, although less accurate, approach (let it be called "semi-coupled approach") would rely on the use of an urban microclimate model responsible for producing local climate data, and then automatically (or manually) passing them as input conditions to the BES tool. Essentially, this semi-coupled approach resides to only insert a weather file to the BES tool, which, instead of a default file of the wider climate zone, is now being produced in a control volume close to the district/building of interest from the micro-climate model. In such an approach, normally a UCM tool is preferred due to its simplicity and fast calculation [156]. To date, the main steps of such semi-coupled approach are the following:


Obviously, the tactic above is a one-way approach, i.e., the microclimate model is executed first and the climatic conditions that emerged are then passed to the BES tool in the format of the default weather file. It should be mentioned that, since this method treats field and zonal models separately, an average expertise is required by the user in order to obtain correct estimations of initial parameters used as boundary conditions. This means that the user should apply external or incorporated special models that solve for these parameters in order to provide boundary conditions, e.g., a correct "guess" of internal temperature and solution of conduction equations to estimate external surface temperatures, taking into account incident solar radiation. It may be concluded that BES/CFD coupling provides a more accurate prediction of energy-related indicators, hence, a more accurate selection of retrofit measures. Through this coupling procedure it becomes clear that energy-related indicators are only a "symptom" of the mathematical interpretation of building and urban physics and, more specifically, of indoor–outdoor interactions. It should be highlighted, however, that further research is required to confront the challenge of high CPU loads and time required for fully coupled approaches. Fortunately, the dramatic improvement of CPU technologies and resources promises such reliable studies in simulation environments.
