Sensitivity analysis of influence factors on multi-zone indoor airflow CFD simulation

Sci Total Environ. 2021 Mar 20:761:143298. doi: 10.1016/j.scitotenv.2020.143298. Epub 2020 Nov 4.

Abstract

Computational fluid dynamics (CFD) is a powerful tool for performing indoor airflow analysis. The simulation results are usually validated with measurement results for accuracy in reflecting reality. When conducting CFD for simulating air flow in a multiple-zone indoor environment with different boundary conditions in different regions, the validation of the CFD model becomes sophisticated. To improve the accuracy of the simulation, boundary conditions need to be adjusted based on how significant the influence factors are affecting the multi-zone CFD model, which few studies have been conducted on. The objective of this study is to investigate the impact of influence factors on temperature and carbon dioxide concentration distribution of a validated CFD model of a typical office floor using ANSYS Fluent. This study provides insights on how to fine-tune a complex model to reflect the actual air flow and how the air quality and human comfort in different zones on the same floor could be affected by influence factors. The influence factors investigated are: (1) size of door gaps, (2) solar radiation and (3) number and orientation of occupants. The velocity variations caused by different door gap sizes were studied for improving multi-zone simulation accuracy by adjusting door gap sizes. To study the significant impact of solar heat on multi-zone environment, the sensitivity of different regions of the office floor to solar heat amount and distribution was analyzed by conducting solar analysis under different weather conditions. Impact of occupants on temperature and carbon dioxide concentration distributions in multi-zone environment were investigated by considering different numbers and facing directions of occupants in different regions of the office floor. In addition, this study demonstrates how to modify the influence factors efficiently using building information modeling (BIM).

Keywords: Building information modeling; Computational fluid dynamics; Indoor air quality; Indoor partition; Solar analysis; Thermal comfort.