Multi-objective optimization of supply air inlet structure for impinging jet ventilation system based on radial basis function neural network

Abstract

A multi-objective optimization of the supply air inlet structure for Impinging Jet Ventilation (IJV) was conducted based on the Radial Basis Function Neural Network (RBFNN) and using a genetic optimization algorithm. The Predicted Mean Vote at the occupant's ankle level (PMV0.1) and the Energy Utilization Coefficient (Et) exhibited significant variability across different inlet structures, thus they were selected as optimization objectives. The predicted results showed substantial consistency with numerical simulations. Within the selected parameter range, the optimal PMV0.1 value was −0.17, and the optimal Et value was 3.57. Furthermore, by adjusting the weights of different optimization objectives, suitable structural parameters can be determined. It was also concluded that, for the given indoor ventilation conditions, the length of the supply air inlet structure should be shorter than its width to better enhance the PMV0.1 value in the areas surrounding occupants.

Keywords

• Radial basis function neural network
• Genetic optimization algorithm
• Impinging jet ventilation
• Supply air inlet structure
• Indoor ventilation