热电制冷除湿模组输入参数多目标优化研究OA

The overall performance of thermoelectric cooling dehumidification modules is affected by input parameters of the integrated thermoelectric system.To optimize module performance,this paper proposes a hybrid optimization strategy combining response surface methodology(RSM)with the multi-objective genetic algorithm(NSGA-Ⅱ).First,a condensation simulation model is built to investigate the effects of thermoelectric current(I),cold-side airflow rate(v1),and hot-side airflow rate(v2)on dehumidification capacity(E)and energy efficiency ratio(η).Then,RSM-based fitting equations for E and ηas functions of input parameters are built.Finally,utilizing these equations as fitness functions,the NSGA-Ⅱalgorithm achieves collaborative optimization of E and η.This strategy may provide some insights into the applications and optimized design of thermoelectric dehumidification modules.