Reconstruction method for three-dimensional temperature field on the aircraft model surface

Tu Bing; Huang Danping; Li Ming

doi:10.7638/kqdlxxb-2024.0200

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Reconstruction method for three-dimensional temperature field on the aircraft model surface

更新时间：2026-05-28

- Reconstruction method for three-dimensional temperature field on the aircraft model surface
- Vol. 44, Issue 3, Pages: 35-43(2026)
- 作者机构：
  
  1. 四川轻化工大学机械工程学院,自贡,643000
  2. 中国空气动力研究与发展中心超高速空气动力研究所,绵阳,621000
  3. 飞行器流体物理全国重点实验室,绵阳,621000
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2024.0200
  CLC： TN21
- Published：2026
- 稿件说明：
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涂兵, 黄丹平, 李明. 飞行器模型表面三维温度场重建方法[J]. 空气动力学学报, 2026, 44(3): 35−43.

Tu Bing, Huang Danping, Li Ming. Reconstruction method for three-dimensional temperature field on the aircraft model surface[J]. 2026, 44(3): 35-43.
涂兵, 黄丹平, 李明. 飞行器模型表面三维温度场重建方法[J]. 空气动力学学报, 2026, 44(3): 35−43. DOI： 10.7638/kqdlxxb-2024.0200

Tu Bing, Huang Danping, Li Ming. Reconstruction method for three-dimensional temperature field on the aircraft model surface[J]. 2026, 44(3): 35-43. DOI： 10.7638/kqdlxxb-2024.0200.

摘要

针对风洞试验中红外图像分辨率低、测试角度限制导致飞行器表面温度场重建精度不足的问题，提出一种飞行器模型表面三维温度场的重建方法。首先采用基于多帧红外图像的特征点识别算法，对特征点进行识别定位；其次通过面元筛选方法将飞行器模型温度失真区域过滤；最后利用温度赋值将风洞试验获取的多视角温度数据进行还原。试验结果表明：相较于传统单帧识别方案，多帧处理算法可使特征点定位精度提升25%‌；在低分辨率（≤ 640 pixel × 512 pixel）红外图像环境下，该方法用于飞行器三维温度场重建的特征点识别率可达96.875%，比基于模板匹配和卷积神经网络算法的识别率（93.75%）高，使得重建的三维温度场更加精准。

Abstract

A method for reconstructing the three-dimensional temperature field of an aircraft model surface is proposed to address the issues of low resolution of infrared images and limited testing angles in wind tunnel tests

which result in insufficient accuracy in reconstructing the surface temperature field of the aircraft. Firstly

a feature point recognition algorithm based on multi-frame infrared images is adopted to identify and locate the feature points; Secondly

the temperature distortion area of the aircraft model is filtered through the panel selection method; Finally

the multi view temperature data obtained from wind tunnel tests are assigned using temperature assignment. The experimental results show that compared to conventional single-frame recognition methods

the multi-frame processing algorithm improves feature point positioning accuracy by 25%. Under low-resolution infrared imaging conditions (≤ 640 pixel × 512 pixel)

the proposed method achieves a feature point recognition rate of 96.875% for 3D temperature field reconstruction on aircraft surfaces

surpassing both normalized cross correlation and convolutional neural network-based algorithms (93.75%). This advancement significantly enhances the precision of the reconstructed 3D temperature field.

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Keywords

references

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