Airborne transmission is an important route of spread of viral diseases (e.g., COVID-19) inside the confined spaces. In this respect, computational fluid dynamics (CFD) emerged as a reliable and fast tool to understand the complex flow patterns in such spaces. Most of the recent studies, nonetheless, focused on the spatial distribution of airborne pathogens to identify the infection probability without considering the exposure time. This research proposes a framework to evaluate the infection probability related to both spatial and temporal parameters. A validated Eulerian-Lagrangian CFD model of exhaled droplets is first developed and then evaluated with an office case study impacted by different ventilation strategies (i.e., cross- (CV), single- (SV), mechanical- (MV) and no-ventilation (NO)). CFD results were analyzed in a bespoke code to calculate the tempo-spatial distribution of accumulated airborne pathogens. Furthermore, two indices of local and general infection risks were used to evaluate the infection probability of the ventilation scenarios. The results suggest that SV has the highest infection probability while SV and NO result in higher dispersions of airborne pathogens inside the room. Eventually, the time history of indices reveals that the efficiency of CV and MV can be poor in certain regions of the room.

The accuracy of the computational fluid dynamics (CFD) to model the airflow around the buildings in the atmospheric boundary layer (ABL) is directly linked to the utilized turbulence model. Despite the popularity and their low computational cost, the current Reynolds Averaged Navier-Stokes (RANS) models cannot accurately resolve the wake regions behind the buildings. The default values of the RANS models' closure coefficients in CFD tools such as ANSYS CFX, ANSYS FLUENT, PHOENIX, and STAR CCM+ are mainly adapted from other fields and physical problems, which are not perfectly suitable for ABL flow modeling. This study embarks on proposing a systematic approach to find the optimum values for the closure coefficients of RANS models in order to significantly improve the accuracy of CFD simulations for urban studies. The methodology is based on stochastic optimization and Monte Carlo Sampling technique. To show the capability of the method, a test case of airflow around an isolated building placed in a non-isothermal unstable ABL was considered. The recommended values for this case study in accordance with the optimization method were thus found to be 1.45 ≤ 1 ≤ 1.5, of 2.7 ≤ 2 ≤ 3, and 0.12 ≤ ≤ 0.15. The default value of = 1 is suggested to be acceptable while the value of is obtained through a correlation. The error of the estimated reattachment length behind the building decreased form 170% for the default values to 28% for the modified values.

Accurate representation of turbulence phenomenon in Computational Fluid Dynamics (CFD) modeling of cross-ventilation around and inside buildings is a challenging and complex problem, especially under the sheltering effect of surrounding buildings. Steady Reynolds Averaged Navier-Stokes (RANS) models are broadly used in many practical applications. However, these models mainly fail to predict accurate distribution of flow characteristics in the cavity formed between the buildings, and hence miscalculate the attributed cross flow and airflow rate through buildings. In this study, a novel and systematic methodology is proposed to enhance the accuracy of the 𝑘 − 𝜀 model for the urban study applications such as cross-ventilation in the sheltered buildings.A microclimate CFD model for a case study of a cross-ventilation experimental work by Tominaga and Blocken [1] was firstly constructed and validated. In the next step, the closure coefficients of the 𝑘 − 𝜀 model were modified using a stochastic optimization and Monte Carlo Sampling techniques. The probability density function (PDF) of all closure coefficients were given to the optimizer and proper objective function defined in terms of different validation metrics. The modified coefficients obtained from the developed systematic method could successfully simulates the cross-ventilation phenomena inside the building with an airflow rate prediction error less than 8% compared to the experiment while other RANS models predicted the airflow rate with up to 70% error. The effectiveness of the optimization technique was also discussed in terms of validation metrics and pressure coefficients.

Computational Fluid Dynamics (CFD) simulations are widely used in many wind-related studies, including cross-ventilation, in urban areas. The accuracy of the CFD models, however, is still a challenging issue for accurate prediction of the complex flow behavior around and inside the buildings. Application of sophisticated CFD models, such as Large Eddy Simulation (LES) and unsteady Reynolds averaged Navier-Stokes (RANS), are generally limited, so many researchers and designers utilize the steady RANS models for design and analysis of crossventilation performance in urban areas. The RANS models, however, provide poor results in predicting the cross-ventilation in street canyons.Thus, this study aims to understand and quantify limitations of the steady RANS models for cross-ventilation applications in highly-packed urban areas. To this end, a series of CFD simulations were conducted for a group of buildings, which were arranged in regular and staggered orders with different urban area densities. Both sealed-body and cross-ventilated scenarios were considered in this study while the surface-averaged and local values of the wind pressure were compared with the results from a wind tunnel measurement by Tamura (2012).Furthermore, the possibility of the RANS model improvement was considered using a parameter sensitivity study over the closure coefficients of a RANS model. Therefore, new coefficients for urban area with densities between 0.2 and 0.4 were found to significantly improve the accuracy of the RANS model. Nonetheless, as an interesting finding of this study, for higher values of urban area densities above 0.4, CFD results went outside the expected measurement ranges; this implies that CFD modeling of higher density urban areas should be treated with more cautious and further studies are required to develop a guideline for such applications.