Optimal Design of Regenerative Cooling Structure Based on Backpropagation Neural Network

Abstract

Aiming at the problems of relying on traditional experience and high cost of schemes selection in the current research on regenerative cooling design, this paper establishes an optimal design model based on the backpropagation (BP) neural network method. In this paper, the cooling structure is parameterized to realize the segmented description. A BP neural network is constructed through the chamber performance simulation data sets to get the mathematical description between structural size parameters and optimization objectives. Taking the specified thrust chamber lifetime, safety margin of chamber wall, and upper limit of maximum wall temperature and pressure loss as constraints, the Globalsearch algorithm is applied to obtain the optimal design results of the regenerative cooling structural size parameters. The results show that the trained BP neural network model is able to predict the regenerative cooling performance parameters with the calculation accuracy similar to numerical simulation. The study of the space shuttle main engine–main combustion chamber indicates that under the same thrust chamber operating parameters and coolant inlet parameters, the optimized regenerative cooling channel reduces both the maximum wall temperature and the coolant pressure loss, and improves the overall cooling performance.