A Multi-Objective Optimization Study of Supply Air Parameters in a Supersonic Aircraft Cabin Environment Combined with Fast Calculation

Supersonic cabins are characterized by high heat flux and high occupant density, which can adversely affect passenger comfort, health, and energy efficiency. This study proposed a multi-objective optimization framework for determining supply air parameters in a supersonic aircraft cabin, evaluating the performances of different optimization methods. The optimization focused on three design objectives: thermal comfort (PMV), air freshness (air age), and the temperature differential between the supply and exhaust air. Two fast calculation methods—Proper Orthogonal Decomposition (POD) and Artificial Neural Networks (ANN)—were compared alongside two optimization algorithms: Multi-Objective Genetic Algorithm (MOGA) and Pareto search. The results indicate that the POD method has a smaller relative root mean square error compared to the ANN method. The relative root mean square error of the ANN method in predicting PMV is 2.7 times higher than the POD method and 3.9 times higher in air age prediction. The Pareto search algorithm outperformed MOGA in computational efficiency, generating 3.3 times more Pareto-optimal solutions in less time. The entropy weight method was used to assign weight for both optimization algorithms, revealing that neither algorithm achieved universally optimal performance across all objectives. Therefore, selecting the best solution requires aligning optimization outcomes with specific design priorities.