Flow field prediction and reconstruction of aircraft based on flexible smart skin

LAI Wuxing; ZHAO Haozhe; HUANG Lin; JI Jingjing; HUANG Yongan

doi:10.7638/kqdlxxb-2024.0139

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Flow field prediction and reconstruction of aircraft based on flexible smart skin

更新时间：2026-05-28

- Flow field prediction and reconstruction of aircraft based on flexible smart skin
- Vol. 43, Issue 12, Pages: 121-134(2025)
- 作者机构：
  
  华中科技大学智能制造装备与技术全国重点实验室,武汉,430074
- 作者简介：
- 基金信息：
- DOI：10.7638/kqdlxxb-2024.0139
  CLC： V211.3
- Published：2025
- 稿件说明：
移动端阅览
来五星, 赵浩哲, 黄林, 等. 基于柔性智能蒙皮的翼型流场预测和重建方法[J]. 空气动力学学报, 2025, 43(12): 121−134.

LAI Wuxing, ZHAO Haozhe, HUANG Lin, et al. Flow field prediction and reconstruction of aircraft based on flexible smart skin[J]. 2025, 43(12): 121-134.
来五星, 赵浩哲, 黄林, 等. 基于柔性智能蒙皮的翼型流场预测和重建方法[J]. 空气动力学学报, 2025, 43(12): 121−134. DOI： 10.7638/kqdlxxb-2024.0139

LAI Wuxing, ZHAO Haozhe, HUANG Lin, et al. Flow field prediction and reconstruction of aircraft based on flexible smart skin[J]. 2025, 43(12): 121-134. DOI： 10.7638/kqdlxxb-2024.0139.

摘要

飞行器的气动问题会引发机翼结冰、飞行失速等安全隐患，通过监测气动载荷数据、研究其流场特性有助于解决这一问题。柔性智能蒙皮提供了温敏漆、压敏漆等风洞试验手段不具备的飞行过程数据采集功能，在获取飞行数据方面展现出巨大潜力。但感知的多物理场数据分布稀疏、数量有限，难以实现转捩、失速位置精确识别。本文针对机翼全流场重构问题，提出了一种基于柔性智能蒙皮的流场高分辨率的快速重建方法，以获取空间密度更大的气动载荷数据，开发了基于编码器-解码器网络架构的双分支通道注意力融合模型，该模型能够快速预测52种NACA翼型在156种不同自由来流条件下的二维流场，实现相对误差为3.68%的二维翼型流场快速预测；利用快速预测模型生成的M6翼型周围流场数据，基于浅层神经网络开发了流场重建模型，在2.73%相对误差内快速重建空间分辨率更高的全域流场分布，为智能蒙皮进一步飞行应用提供数据支撑。

Abstract

Real-time analysis and prediction of flow field characteristics are essential for flight safety and necessitating in-flight data acquisition

an almost impossible task for traditional techniques such as temperature/pressure-sensitive paints. Flexible smart skins demonstrate great promise for acquiring in-flight data. Nevertheless

the multi-physical data collected are usually sparsely in distribution and limited in quantity

making it difficult to accurately identify transition and stall locations. To overcome this limitation

we introduce a novel method for rapidly reconstructing high-resolution flow fields around aircraft wings from sparse data. Leveraging the flow fields computed by CFL3D

a dual-branch attention fusion model is developed based on an encoder-decoder network. This model facilitates rapid prediction of 2D flow fields for 52 types of NACA airfoils under 156 different freestream conditions

achieving an average relative error of 3.68% and providing a substantial amount of high-fidelity training data for the reconstruction process. Furthermore

using flow fields around the M6 airfoil generated by the rapid prediction model

a flow field reconstruction model is developed based on a shallow neural network. This model fulfills rapid reconstruction of high-resolution flow fields with a relative error of 2.73%. This work establishes a valuable data foundation for the in-flight application of smart skins

enhancing real-time monitoring capabilities and improving flight safety through accurate flow field reconstructions and insights.

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references

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