Modelling enhancement of cross-ventilation in sheltered buildings using stochastic optimization

A microclimate CFD model for a case study of a cross-ventilation experimental work by Tominaga and Blocken (2015) 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.