基于径向基神经网络的气动热预测代理模型

张智超; 高太元; 张磊; 拓双芬

doi:10.7527/S1000-6893.2020.24167

航空学报 >

2021 , Vol. 42 >Issue 4: 524167 - 524167

DOI: https://doi.org/10.7527/S1000-6893.2020.24167

流体力学与飞行力学

基于径向基神经网络的气动热预测代理模型

张智超 ,
高太元 ,
张磊 ,
拓双芬

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北京机电工程总体设计部, 北京 100039

收稿日期: 2020-04-30

修回日期: 2020-06-19

网络出版日期: 2020-07-10

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Aeroheating agent model based on radial basis function neural network

ZHANG Zhichao ,
GAO Taiyuan ,
ZHANG Lei ,
TUO Shuangfen

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Beijing System Design Institute of Electro-mechanic Engineering, Beijing 100039, China

Received date: 2020-04-30

Revised date: 2020-06-19

Online published: 2020-07-10

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摘要

为快速获取高超声速飞行器表面热流数据并缩短飞行器气动热设计周期，提出了一种基于径向基神经网络的气动热快速预测代理模型方法。首先，在飞行器表面每一个离散化的网格节点单独构造一种正则化的径向基神经网络。随后，通过训练集对所有网络同时进行训练，获得各自网络的连接权值。最后，所有网格节点的神经网络协同预测飞行器表面不同位置的热流。对NASA火星实验室的椭圆钝化高超声速飞行器的应用表明，所提出的代理模型方法在模型训练完成后能够快速进行飞行器表面热流预测，并且模型具有良好的泛化能力，在驻点及迎风大面积区域热流预测结果与数值模拟的偏差在10%以内。

关键词： 神经网络; 径向基函数; 高超声速; 气动热; 代理模型

本文引用格式

张智超 , 高太元 , 张磊 , 拓双芬 . 基于径向基神经网络的气动热预测代理模型[J]. 航空学报, 2021 , 42(4) : 524167 -524167 . DOI: 10.7527/S1000-6893.2020.24167

Abstract

An aeroheating agent model based on the radial basis function neural network is proposed to rapidly acquire heat flux on the surface of hypersonic vehicles and shorten the aeroheating design cycle. A regularization radial basis function neural network is firstly constructed on each grid node of the solid surface, followed by the acquisition of the connection weights of different nerve cells through simultaneous training of all neural networks based on the data of the train set. Finally, the heat flux results of different positions on the surface of vehicles are predicted synergistically by the neural networks on grid nodes. The simulation results of the elliptical blunt vehicle designed by the Mars Science Laboratory of NASA indicate that the agent model can be employed to rapidly predict heat flux of hypersonic vehicles with good generalization capability. In addition, the results of heat flux on the stagnation point and the windward wall prove that the heat flux deviations between the agent model and numerical simulation are smaller than 10%.

Key words： neural networks; radial basis function; hypersonic; aeroheating; agent models

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