ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Predicting method of aircraft mechanical response based on residual neural networks
Received date: 2024-09-30
Revised date: 2024-10-29
Accepted date: 2025-02-10
Online published: 2025-03-06
Supported by
National Defense Basic Scientific Research Program of China(JCKY2019205A006)
Digital twin technology for aircraft structures reflects the mechanical response and comprehensive performance of an aircraft’s lifecycle through high-fidelity and dynamically updated digital models. To improve the accuracy of structural response predictions， digital twin models often use multi-level， refined simulation techniques. However， this leads to a significant increase in model computation amount and cost， making it challenging to meet the real-time prediction needs for aircraft structural strength during flight. AI-based reduced-order prediction of structural strength is a key technology for real-time prediction of structural responses during flight. By combining high-order digital twin model simulation results with structural loads inferred from actual flight data such as flight parameters and strain， it is possible to rapidly and accurately predict the mechanical response of aircraft structures. This addresses the timeliness issue of aircraft structural performance prediction during actual flights， and has gained increasing attention in the field of aircraft digital twin technology. This paper proposes a method for pixelating structural cloud maps and load inputs to express spatial relationships between point cloud data. To construct an intelligent prediction method for mechanical responses Based on load inputs， a convolutional neural network with cross-layer connection mechanisms is also introduced. Results from numerical experiments on the wing structure under 329 conditions show that the pixelation method can retain structural response characteristics， while making the data compatible with pixel-based data processing methods like convolution. Compared to traditional convolutional neural networks， the proposed residual neural network model achieves improved prediction accuracy and reduced loss. Additionally， this intelligent method achieves more than two orders of magnitude efficiency improvement compared to traditional simulations. The predicted stress distribution shows a high similarity to finite element simulation stress distributions， highlighting its application value in the digital twin of aircraft structures.
Yinxuan ZHANG , Qi ZHANG , Zhenyong XU , Linshu MENG . Predicting method of aircraft mechanical response based on residual neural networks[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2025 , 46(19) : 531295 -531295 . DOI: 10.7527/S1000-6893.2025.31295
| [1] | BURGESS N. Advances in numerical methods for CREATE-AV analysis tools［C］∥ 52nd Aerospace Sciences Meeting. Reston： AIAA， 2014. |
| [2] | HALLISSY B P， HINE D， LAIOSA J P， et al. CREATE-AV quality assurance： best practices for validating and supporting computation-based engineering software［C］∥ 52nd Aerospace Sciences Meeting. Reston： AIAA， 2014. |
| [3] | KRAFT E. HPCMP CREAT-AV and the air force digital thread［C］∥ 53rd AIAA Aerospace Sciences Mee-ting. Reston： AIAA， 2015. |
| [4] | FALLON T， MAHAL D， HEBDEN I. F-35 Joint strike fighter structural prognostics and health management： An overview［C］∥ The 25th Symposium of the International Committee on Aeronautical Fatigue. Rotterdam： The National Aerospace Laboratory （NLR）， 2009： 1215. |
| [5] | 张宝珍， 王萍. 飞机PHM技术发展近况及在F-35应用中遇到的问题及挑战［J］. 航空科学技术， 2020， 31（7）： 18-26. |
| ZHANG B Z， WANG P. Recent development of aircraft PHM technology and problems and challenges encountered in the application on F-35［J］. Aeronautical Science & Technology， 2020， 31（7）： 18-26 （in Chinese）. | |
| [6] | HEBDEN I G， CROWLEY A M， BLACK W. Overview of the F-35 structural prognostics and health management system［C］∥ The 9th European Workshop on SHM. Manchester： University of Manchester， 2018： 10-13. |
| [7] | LIN M X， GUO S J， HE S， et al. Structure health monitoring of a composite wing based on flight load and strain data using deep learning method［J］. Composite Structures， 2022， 286： 115305. |
| [8] | SPILIOTOPOULOS P E， FERA F T， SARAMANTAS I E， et al. Development and experimental validation of a prototype system for machine learning based SHM in composite aerostructures［J］. Journal of Physics： Conference Series， 2024， 2692（1）： 012025. |
| [9] | JEONG S H， LEE K B， HAM J H， et al. Estimation of maximum strains and loads in aircraft landing using artificial neural network［J］. International Journal of Aeronautical and Space Sciences， 2020， 21（1）： 117-132. |
| [10] | REZAEI M， SHIRAZI K H， KHODAPARAST H H. Uncertainty quantification of aeroelastic wings flutter using an optimized machine learning approach［J］. Proceedings of the Institution of Mechanical Engineers， Part G： Journal of Aerospace Engineering， 2022， 236（15）： 3167-3185. |
| [11] | 柳家齐， 陈荣钱， 楼锦华， 等. 基于深度学习的高速直升机旋翼翼型气动优化设计［J］. 航空学报， 2024， 45（9）： 529828. |
| LIU J Q， CHEN R Q， LOU J H， et al. Aerodynamic shape optimization of high-speed helicopter rotor airfoil based on deep learning［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（9）： 529828 （in Chinese）. | |
| [12] | 田阔， 孙志勇， 李增聪. 面向结构静力试验监测的高精度数字孪生方法［J］. 航空学报， 2024， 45（7）： 429134. |
| TIAN K， SUN Z Y， LI Z C. High-precision digital twin method for structural static test monitoring［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（7）： 429134 （in Chinese）. | |
| [13] | 高天贺， 田阔， 黄蕾， 等. 数据驱动的曲面构件形状-拓扑协同优化方法［J］. 航空学报， 2024， 45（2）： 428806. |
| GAO T H， TIAN K， HUANG L， et al. Data-driven shape-topology optimization method for curved shells［J］. Acta Aeronautica et Astronautica Sinica， 2024， 45（2）： 428806 （in Chinese）. | |
| [14] | 常辉， 朱靖， 安朝， 等. 应用前馈神经网络的大柔性机翼阵风响应分析［J］. 计算机测量与控制， 2022， 30（8）： 236-244. |
| CHANG H， ZHU J， AN C， et al. Aeroelastic analysis of large flexile wing based on machine learning algorithm［J］. Computer Measurement & Control， 2022， 30（8）： 236-244 （in Chinese）. | |
| [15] | HUANG W B， WANG R， ZHANG M C， et al. The research on deep learning-driven dimensionality reduction and strain prediction techniques based on flight parameter data［J］. Applied Sciences， 2024， 14（9）： 3938. |
| [16] | ZHANG Y J， CAO S C， WANG B T， et al. A flight parameter-based aircraft structural load monitoring method using a genetic algorithm enhanced extreme learning machine［J］. Applied Sciences， 2023， 13（6）： 4018. |
| [17] | LI H Q， ZHANG Q H， CHEN X Q. Deep learning-based surrogate model for flight load analysis［J］. Computer Modeling in Engineering & Sciences， 2021， 128（2）： 605-621. |
| [18] | 赵燕， 宋江涛， 唐宁. 某机翼的安全预测载荷模型建立［J］. 航空学报， 2020， 41（10）： 223852. |
| ZHAO Y， SONG J T， TANG N. Construction of safety-predicting load model on certain wing［J］. Acta Aeronautica et Astronautica Sinica， 2020， 41（10）： 223852 （in Chinese）. | |
| [19] | 金鑫， 殷建业， 王健志. 基于深度学习的飞行载荷测试与反演方法研究［J］. 航空工程进展， 2020， 11（6）： 887-893. |
| JIN X， YIN J Y， WANG J Z. Research on deep-learning-based flight load test and estimation method［J］. Advances in Aeronautical Science and Engineering， 2020， 11（6）： 887-893 （in Chinese）. | |
| [20] | ZHANG W， DOI K， GIGER M L， et al. An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms［J］. Medical Physics， 1996， 23（4）： 595-601. |
| [21] | GOODFELLOW I， BENGIO Y， COURVILLE A. Deep learning［M］. Massachusetts： MIT Press， 2016： 413-417. |
| [22] | HE K M， ZHANG X Y， REN S Q， et al. Deep residual learning for image recognition［C］∥ 2016 IEEE Confe-rence on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2016： 770-778. |
| [23] | IOFFE S， SZEGEDY C. Batch normalization： accelerating deep network training by reducing internal covariate shift［C］∥ International Conference on Machine Lear-ning. Lille： International Machine Learning Society， 2015： 448-456. |
| [24] | PASCANU R， MIKOLOV T， BENGIO Y. On the difficulty of training recurrent neural networks［C］∥ International Conference on Machine Learning. Atlanta： International Machine Learning Society， 2013： 1310-1318. |
| [25] | CHARLES R Q， HAO S， MO K C， et al. PointNet： deep learning on point sets for 3D classification and segmentation［C］∥ 2017 IEEE Conference on Computer Vision and Pattern Recognition （CVPR）. Piscataway： IEEE Press， 2017： 77-85. |
| [26] | 韩冰， 张鑫云， 任爽. 基于三维点云的卷积运算综述［J］. 计算机研究与发展， 2023， 60（4）： 873-902. |
| HAN B， ZHANG X Y， REN S. Survey of convolution operations based on 3D point clouds［J］. Journal of Computer Research and Development， 2023， 60（4）： 873-902 （in Chinese）. |
/
| 〈 |
|
〉 |
CJA