基于卷积神经网络的气动热预测方法

袁佳铖; 宗文刚; 曾磊; 李强; 张昊元; 蔺佳哲

doi:10.7638/kqdlxxb-2023.0072

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基于卷积神经网络的气动热预测方法

研究论文 | 更新时间：2026-07-27

- 基于卷积神经网络的气动热预测方法
- CNN-based method for predicting aerodynamic heating
- 空气动力学学报 2024年42卷第1期页码：13-25
- 作者机构：
  
  1. 四川大学化学工程学院,成都,610065
  2. 中国空气动力研究与发展中心,绵阳,621000
- 作者简介：
  
  袁佳铖（1997—），男，四川成都人，硕士研究生，研究方向：数据挖掘. E-mail：yuanjiacheng@stu.scu.edu.cn
  宗文刚*，研究员，研究方向：气动热预测. E-mail：wgzong@gmail.com
- 基金信息：
  
  国家重点研发计划资助项目（2019YFA0405202）
- DOI：10.7638/kqdlxxb-2023.0072
  中图分类号： V211.3
- 纸质出版：2024
- 稿件说明：
移动端阅览
袁佳铖, 宗文刚, 曾磊, 等. 基于卷积神经网络的气动热预测方法[J]. 空气动力学学报, 2024, 42(1): 13−25.

YUAN Jiacheng, ZONG Wengang, ZENG Lei, et al. CNN-based method for predicting aerodynamic heating[J]. 2024, 42(1): 13-25.
袁佳铖, 宗文刚, 曾磊, 等. 基于卷积神经网络的气动热预测方法[J]. 空气动力学学报, 2024, 42(1): 13−25. DOI： 10.7638/kqdlxxb-2023.0072

YUAN Jiacheng, ZONG Wengang, ZENG Lei, et al. CNN-based method for predicting aerodynamic heating[J]. 2024, 42(1): 13-25. DOI： 10.7638/kqdlxxb-2023.0072.

摘要

严重的气动加热现象会威胁飞行器的飞行安全，因此在飞行器设计期间需要对其气动热环境进行预测以辅助热防护设计，气动热的预测速度直接影响了飞行器的设计效率。为了探索气动热的快速预测方法以缩短高超声速飞行器的设计周期，本文基于卷积神经网络建立了数据驱动的气动热预测模型。首先，为了实现不同外形飞行器的表面热流预测，提出了一种能够用于卷积神经网络的三维外形几何表达方法。然后，基于该方法分别采用编码器-解码器架构和U-Net架构建立了两种神经网络模型，实现了气动热的快速预测。最后，选取钝锥、钝双锥、升力体和双椭球4类高超声速飞行器典型外形作为研究对象，采用CFD数值模拟方法构建了气动热数据集，在不同的气动热数据集上对建立的模型进行了训练和测试。计算验证结果表明，两种模型针对简单外形预测精度良好，但当外形变复杂时，U-Net模型对外形的感知能力更强，具有更高的预测精度。与其他数据驱动的方法相比，U-Net模型具有更强的学习能力，能够在较少的训练样本下达到相对较高的预测精度。另一方面，由于该方法采用了大量卷积神经网络结构，因此具有更高的建模效率。

Abstract

Serious aerodynamic heating is a threat to the safety of aircraft

so it is necessary to predict the aerodynamic thermal environment during the design of the thermal protection system of aircraft. Moreover

the prediction speed of the aerodynamic heating directly affects the design efficiency of aircraft. This study establishes a data-driven aerodynamic heating prediction model based on convolutional neural networks (CNN) to fast aerodynamic heating prediction and shorten the design period of hypersonic vehicles. Firstly

a three-dimensional geometric representation method suitable for the convolutional neural networks is proposed

which can predict the heat flux of aircraft with different shapes. Then

based on the proposed method

two neural network models are established to predict the aerodynamic heating by using the encoder-decoder architecture and the U-Net architecture

respectively. Finally

to verify the effectiveness of the proposed method

four types of typical geometries of hypersonic vehicles

i.e.

a blunt cone

a double cone

a lifting body

and a double ellipsoid

are selected as the research objects

and the aerodynamic heating datasets are constructed using CFD simulations. The models are trained and tested on different aerodynamic heating datasets

and the results show that both models perform well in predicting the aerodynamic heating of simple geometries

but when the geometry becomes more complex

the prediction accuracy of the U-Net model is higher due to its greater sensitivity to the geometry change. Compared with other data-driven methods

the U-Net model has stronger learning ability and can obtain relatively higher prediction accuracy based on fewer training samples. Furthermore

thanks to the use of a large number of convolutional neural network structures

the proposed method has higher modeling efficiency.

关键词

Keywords

references

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