基于自适应径向基网络的热防护结构可靠性评估

RELIABILITY EVALUATION OF THERMAL PROTECTION STRUCTURE UPON ADAPTIVE RBF NEURAL NETWORK

针对复杂载荷下热防护结构可靠性评估效率低、分析精度差等问题,该文提出一种基于自适应径向基神经网络的可靠性评估方法。通过引入非线性收敛因子,对传统灰狼优化算法进行改进;采用改进后的灰狼算法优化径向基神经网络的中心点个数和扩展常数,建立精确预示热防护结构应力响应的自适应径向基网络模型;开展热防护结构的仿真和试验研究。结果表明:通过引入非线性收敛因子,大幅提高了灰狼算法的优化性能;该文提出的自适应径向基网络可以在小样本条件下建立高精确的代理模型;基于该文方法获得的可靠性分析结果与蒙特卡罗仿真结果、试验结果具有较好的一致性。

An evaluation method based on adaptive radial basis function(RBF)neural network is proposed to solve the problems of low efficiency and poor analysis accuracy in the reliability assessment of thermal protection structures(TPS)under complex loads.The traditional grey wolf algorithm is improved by introducing a nonlinear convergence factor.The improved grey wolf algorithm is applied to optimize the number of central points and expansion constant of the radial basis function to establish an adaptive radial basis function neural network,which can accurately predict the stresses.The reliability of the thermal protection structure is numerically and experimentally investigated.It is concluded that the optimization performance of the grey wolf algorithm is significantly improved by introducing a nonlinear convergence factor.The proposed adaptive RBF model could quickly realize the data prediction through small samples while ensuring the accuracy.The reliability obtained by the method proposed matches well with that of Monte Carlo simulation and of experimental results.