Neural Network and Regression Approximations in High-Speed Civil Aircraft Design Optimization

摘要

Nonlinear mathematical-programming-based design optimization can be an elegant method. However, the calculations required to generate the merit function, constraints, and their gradients, which are frequently required, make the process computationally intensive. The computational burden can be substantially reduced by using approximating analyzers derived from an original analyzer utilizing neural networks and linear regression methods. The experience gained from using both of these approximation methods in the design optimization of a high-speed civil transport aircraft is the subject of this paper. The NASA Langley Research Center's Flight Optimization System was selected for the aircraft analysis. This software was exercised to generate a set of training data with which a neural network and regression method were trained, thereby producing the two approximating analyzers. The derived analyzers were coupled to the NASA Lewis Research Center's CometBoards test bed to provide the optimization capability. Both approximation methods were examined for use in aircraft design optimization, and both performed satisfactorily. The CPU time for solution of the problem, which had been measured in hours, was reduced to minutes with the neural network approximation and to seconds with the regression method. Instability encountered in the aircraft analysis software at certain design points was also eliminated. However, there were costs and difficulties associated with training the approximating analyzers. The CPU time required to generate the I/O pairs and to train the approximating analyzers was seven times that required for solution of the problem.