CFD-based multi-objective optimization of impinging jet ventilation (IJV) systems to improve the local indoor air quality (IAQ) indexes

Impinging jet ventilation (IJV) systems, despite some advantages, encounter some weaknesses in delivering local thermal comfort and detachment of particles from the floor. This study leverages the NSGA-II algorithm to optimize the design parameters of IJV systems, aiming to achieve concurrently optimal conditions for thermal comfort and particle suspension. In this context, a surrogate model based on an artificial neural network (ANN) coupled with computation fluid dynamics (CFD) is employed for the problem space. The Jonson-Kendall-Roberts (JKR) model is also implemented using a UDF in Fluent software to simulate particle detachment from the ground level. The optimization variables encompass the dimensions of the air inlet nozzle, the nozzle height, the mass flow rate of the nozzle inlet, and the objective functions comprise the vertical temperature difference, draft index, and the total residence time of suspended particles within the room space. A factorial analysis was performed to evaluate the influence of design variables on the objective functions. The analysis revealed that all design variables have a significant impact on the performance of impinging jets in achieving the desired objective functions. Mass flow rate emerged as the most influential parameter, exerting the strongest effect on the optimized objective functions. After optimization, the points X=۰.۱۹, Z=۰.۲۳, H=۱.۳۶, ṁ=۰.۰۳۴ are chosen from the Pareto front as the consensus optimal point with more favorable objective functions compared to other options.

Keywords:

Impingement Jet Ventilation (IJV) , Particle Detachment , Indoor Air Quality (IAQ) , Thermal comfort , artificial neural network (ANN)