Optimizing indoor air quality: CFD simulation and novel air cleaning methods for effective aerosol particle inhibition in public spaces - Geng, Chao-Long; Zhu, Xu-Yanran; Chen, Ning (Environmental science and pollution research international, 2023)
In contemporary building ventilation, displacement and mixing ventilation demand high air volumes for rapid virus elimination, resulting in elevated energy consumption. To minimize the spread of viruses and decrease energy consumption for ventilation, this study employed CFD to explore the efficacy of a downward uniform flow field in impeding the transmission of aerosol particles in a high-traffic public facility, like a supermarket. The findings indicate that the downward uniform flow field proves insufficient when individuals remain static for extended periods. A wind speed of 0.1 m/s or higher becomes essential to overpower the stationary thermal plume, which disrupts this flow field. In areas with human presence, however, this technique is found to be particularly efficient since mobile heat sources do not generate a fixed thermal plume. A 0.05 m/s downward uniform flow field can settle 90% of particles within just 22 s. This flow pattern contributes to the swift settling of aerosol particles and effectively diminishes their dispersion. Employing this flow pattern in public places with increased foot traffic, like supermarkets, can lower the risk of contracting novel coronavirus without augmenting energy consumption. In order to implement the flow field in a part of the domain, a new air purification device is proposed in this study. The device combined with shelves can optimize the flow field uniformity through the MLA (PSO-SVR) algorithm and alteration of the air distribution structure. The uniformity of the final flow field increased to 0.925. The combination of data-driven MLA with CFD showed good performance in predicting the flow field uniformity. These findings offer valuable insights and practical applications for the prevention and control of respiratory diseases, particularly in post-epidemic scenarios.