Heat Flux Estimation in Nonlinear Materials Using Kalman Filter-Enhanced Neural Network

摘要

This paper presents an efficient technique for analyzing the surface heat flux of a space shuttle upon reentry using a nonlinear inverse heat conduction technique hased upon a Kalman filter-enhanced Bayesian backpropagation neural network. The continuous-time analog Hopfield neural network is used to solve various forward problems to obtain training data for the Kalman filter-enhanced Bayesian backpropagation neural network. The calibrated Kalman filter-enhanced Bayesian backpropagation neural network is then used to inversely compute the boundary conditions from given sets of temperature data obtained using the continuous-time analog Hopfield neural network. The results show that the proposed method can predict the unknown parameters of the current inverse problems with an accuracy of 0.001%. The performance of the Kalman filter-enhanced Bayesian backpropagation neural network scheme is shown to be better than that of a Bayesian backpropagation neural network or a stand-alone backpropagation scheme calibrated using a Levenberg-Marquardt backpropagation algorithm.