Inverse design of aircraft cabin environment by coupling artificial neural network and genetic algorithm

Abstract

An inverse design method is proposed to achieve the pre-set control objectives of the aircraft cabin environment. The method combines the artificial neural network and the genetic algorithm, and the training and testing data of the artificial neural network is obtained by computational fluid dynamics analysis. Both of the thermal comfort and energy consumption are considered in the inverse design. The artificial neural network is used to identify the relationship between the thermal comfort and the air supply parameters (inlet velocity magnitude and angle, inlet air temperature). The genetic algorithm coupled with the well-trained artificial neural network is used to design the aircraft cabin environment. Numerical results show that the Bayesian regularization algorithm is proved to have better generalization capability than the other training algorithms for the artificial neural network. The increase of training data quantity improves the generalization capability of the artificial neural network, while it spends more simulation time. A computational fluid dynamics database with 60 datasets is shown to be suitable to the present inverse design, and the testing error of the artificial neural network is below 8.2%. Several groups of optimal air supply parameters are found with different trade-offs between the thermal comfort and energy consumption. The best solution of thermal comfort, i.e., the percentage of zone with |PMV| >0.5 in all cabin control domains, is less than 7.8%.