Flow stability and non-monotonic contaminant removal in electronic cleanrooms with pulsating air supply

Pulsed airflow supply is a promising strategy for enhancing cleanroom ventilation by disrupting stagnant zones; yet its dynamic interaction with air change rates (ACH) and human respiration remains complex and under-investigated. This study employed Computational Fluid Dynamics (CFD) coupled with a User-Defined Function (UDF) to model realistic human respiratory effects, comparing pulsed and constant airflow modes across standard cleanroom ACH levels (50, 55 and 60 h ⁻¹ ). The numerical model integrated turbulence modelling, Lagrangian particle tracking and transient boundary conditions to capture airflow evolution, particle dispersion and breathing-zone exposure. A quantitative analysis utilizing the Mean Age of Air (MAA), Contaminant Removal Efficiency (E _T ) and velocity attenuation coefficient was conducted to evaluate the airflow organization, ventilation effectiveness and contaminant removal performance of each airflow mode. The results showed that pulsed airflow significantly outperformed constant modes, reducing MAA by 42.2% and nearly doubling E _T at 60 h ⁻¹ . Furthermore, the relationship between ACH and removal effectiveness exhibited non-linear behaviour under dynamic conditions; specifically, performance temporarily degraded at 55 h ⁻¹ compared with 50 and 60 h ⁻¹ . Consequently, the optimal operational strategy lies in the 55–60 h ⁻¹ transition range, balancing momentum-driven scouring with flow stability. These findings provide a robust basis for optimizing cleanroom environments.