High-dimensional multi-objective optimization algorithm for combustion chamber of aero-engine based on artificial neural network-multi-objective particle swarm optimization

This paper considers optimizing the performance of high-temperature combustion chamber of an aero-engine based on a concentric and hierarchical model. First, sample data for the design variables are obtained based on Latin hypercube sampling method, and a one-dimensional program is used to obtain the true values of combustion efficiency and total pressure loss corresponding to each group of variables. The obtained data are then pre-processed to establish a dataset. Second, a multi-layer artificial neural network (ANN) architecture is designed and a surrogate model of the combustion-related performance of the combustor is established using a data-driven method. The results of global sensitivity analysis based on variance show that ratio of fuel flow to air flow (fuel–air ratio) and the total inlet pressure are the most important factors influencing the two objective functions. Finally, we optimize the multi-objective combustion-related performance of the surrogate model by applying the particle swarm optimization algorithm to it. The results of experiments show that the ANN-based model could accurately predict the efficiency of combustion and total pressure loss of the chamber, yielding root mean-squared errors of 0.0107 and 0.3032%, respectively. It also had better generalization ability than the cubic polynomial surrogate model. Compared with the cubic polynomial model, it generated an optimal Pareto solution set as prediction that had higher values in both objective functions. The proposed model might require better data that can be obtained using intelligent sampling methods so that deeper neural networks can be designed to reduce error and improve its optimization design.