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

Shirzadi, Mohammadreza; Mirzaei, Parham; Naghashzadegan, Mohammad; Tominaga, Yoshihide

Abstract

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 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 (2015) was firstly constructed and validated. In the next step, the closure coefficients of themodel 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.

Citation

Shirzadi, M., Mirzaei, P., Naghashzadegan, M., & Tominaga, Y. (2018). Modelling enhancement of cross-ventilation in sheltered buildings using stochastic optimization. International Journal of Heat and Mass Transfer, 118, 758-772. https://doi.org/10.1016/j.ijheatmasstransfer.2017.10.107

• Journal Article Type: Article
• Acceptance Date: Oct 27, 2017
• Online Publication Date: Nov 22, 2017
• Publication Date: 2018-03
• Deposit Date: Mar 31, 2020
• Publicly Available Date: Apr 8, 2020
• Journal: International Journal of Heat and Mass Transfer
• Print ISSN: 0017-9310
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 118
• Pages: 758-772
• DOI: https://doi.org/10.1016/j.ijheatmasstransfer.2017.10.107
• Public URL: https://nottingham-repository.worktribe.com/output/1217651
• Publisher URL: https://www.sciencedirect.com/science/article/abs/pii/S0017931017315351