Airborne Pollutant Removal Effectiveness and Hidden Pollutant Source Identification of Bionic Ventilation Systems: Direct and Inverse CFD Demonstrations

Abstract

A healthy and efficient ventilation system is essential for establishing a comfortable indoor environment and significantly reducing a building’s energy demand simultaneously. This paper proposed a novel ventilation system and applied it to the IEA Annex 20 mixing ventilation enclosure to verify its feasibility through mathematical modeling and CFD simulations. First, two bionic ventilation systems, single-side and dual-side ventilations, were compared to a conventional constant-volume supply system using CFD simulations, with the results demonstrating that the bionic ventilation system could provide higher ventilation efficiency and more effective pollutant removal from stagnant regions. Furthermore, the present work exercised these two bionic ventilation systems with different temporal periods of sine and rectangular wave functions, identifying a turning point at a period of 0.06 ${\tau }_n$ , which could contribute to further enhancement of these bionic ventilation systems. Finally, a methodology depending on the Bayesian inference algorithm was developed for identifying pollution sources in the bionic ventilation system with unstable flow fields, and factors influencing source identification accuracy were discussed. The results show that the peaks of the KDE distributions and the sampling average values of both the source location and intensity are all consistent with the actual source parameters. The potential of the proposed bionic ventilation systems has been well demonstrated by direct and inverse CFD models, paving the way for further engineering applications.