Current building energy simulation approaches usually do not take into account the influence of the surrounding urban neighborhood on the energy consumption patterns. This study aims to address this limitation and quantify the impact of an urban neighborhood. Therefore, the current study develops and validates a framework using Computational Fluid Dynamics (CFD) and building energy simulations. The developed framework in this study relies on deriving wind multipliers for eight principal directions to adjust the wind velocity encountering the building of interest. The purpose of these wind multipliers is to adjust wind velocities in the weather data input files for building energy models. To support the developed framework, this study validated the simulated CFD temperatures with on-site measured data as well as compared the simulated heating and electricity consumptions with the metered energy consumptions. A comparison between the building energy simulation results using both the adjusted and original weather data indicates that the total building energy consumption decreased by 5%. Among the heat gain components, the infiltration component had the maximum percentage of reduction with 31% and 29% sensible cooling and heating decrease, respectively. Overall, even though the annual cooling energy demand increased, the energy demand decreased due to the decrease in the heating demand for the studied neighborhood located in the Northeastern USA.

This paper considered an actual neighborhood to quantify impacts of the local urban microclimate on energy consumption for an academic building in College Park, USA. Specifically, this study accounted for solar irradiances on building and ground surfaces to evaluate impacts of the local convective heat transfer coefficient (CHTC), infiltration rate, and coefficient of performance (COP) on building cooling systems. Using computational fluid dynamics (CFD) allowed for the calculation of local temperature and velocity values and implementation of the local variables in the building energy simulation (BES) model. The discrepancies among the cases with different CHTCs showed slight influence of CHTCs on sensible load, in which the maximum variations existed 1.95% for sensible cooling load and 3.82% for sensible heating load. The COP analyses indicated windward wall and upstream roof are the best locations for the installation of these cooling systems. This study used adjusted infiltration rate values that take into account the local temperature and velocity. The results indicated the annual cooling and heating energy increased by 2.67% and decreased by 2.18%, respectively.

For buildings located in actual urban neighborhoods, modeling of outdoor airflow with Computational Fluid Dynamics (CFD) requires solar radiation at building surfaces to predict local environmental temperatures. This study conducts a parametric analysis to support the development of coupled simulations of outdoor airflow and solar radiation simulations at building surfaces. To account for different assumptions used in the outdoor modeling, this study uses OpenFOAM CFD and couples it with three different simulation engines, including EnergyPlus, Daysim, and Radiance to predict simulated outdoor solar irradiance for implementation in outdoor CFD simulations. The primary aim of selecting these three simulation engines is to evaluate tradeoffs between the model complexity and accuracy for simulated outdoor solar irradiance for outdoor CFD simulations. Examined parameters include: (i) surface representation with different mesh types, (ii) urban plan area density, and (iii) the impact of simulated solar irradiances on the simulated air temperature and velocity values in the CFD simulations. The study results showed that the surface representation has up to a 7.6% and 133% influence on the simulated outdoor global and local solar irradiances, respectively. The surface thermal boundary conditions have up to 1.5°C difference on the air temperature and negligible impacts on the air velocity.

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