翼面结构强度试验的数据驱动虚实融合方法
收稿日期: 2025-11-10
修回日期: 2025-12-30
录用日期: 2026-03-09
网络出版日期: 2026-03-19
基金资助
四川省重大科技专项(25ZDZX0059)
Data-driven virtual-real fusion method for wing structure strength test
Received date: 2025-11-10
Revised date: 2025-12-30
Accepted date: 2026-03-09
Online published: 2026-03-19
Supported by
Sichuan Province Major Science and Technology Special Project(25ZDZX0059)
飞机结构强度试验是目前航空结构最重要的一种验证手段，是飞机研制过程中不可或缺的重要环节。目前，飞机结构强度试验的测量手段相对单一，只能获取有限且离散的响应数据，难以获取试验全过程及全场域的信息，从而限制了数据的全面分析和处理。鉴于此，以一体化机翼盒段为研究对象，提出一种数据驱动的虚实融合算法，通过融合仿真数据和试验数据构建适用于结构强度试验的数字孪生模型，从而实现对试验对象力学性能的高精度预测。该算法分为预训练和实时预测2个阶段。在预训练阶段，采用仿真数据训练粒子群优化随机森林（PSO-RF）模型。在实时预测阶段，基于已训练好的PSO-RF模型与试验数据之间的误差，训练径向基函数多保真度代理模型（RBF-MFS）。最终，通过融合PSO-RF模型和RBF-MFS模型，构建试验对象的数字孪生模型。结果表明，该模型在测试集上的精度在R2=0.97左右，且在33万网格节点上的计算耗时仅为0.8 s，针对机翼盒段的危险区域，该模型的预测误差小于10%，满足实际工程应用需求，为飞机结构强度试验数字化提供了参考依据。
魏岩 , 曾建江 , 王旋 , 朱小龙 , 陈涛 , 童明波 . 翼面结构强度试验的数据驱动虚实融合方法[J]. 航空学报, 2026 , 47(16) : 233068 -233068 . DOI: 10.7527/S1000-6893.2026.33068
Aircraft structural strength test is the most important verification method of aviation structure at present， and it is an indispensable and important link in the aircraft development process. At present， the measurement method of aircraft structural strength test is relatively simple， and only limited and discrete response data can be obtained， and it is difficult to obtain information during the whole process and the whole field of the test， which limits the comprehensive analysis and processing of data. In view of this， this paper proposes a data-driven virtual-real fusion algorithm to construct a digital twin model suitable for structural strength test by fusing simulation data and test data， so as to achieve high-precision prediction of the mechanical properties of test objects. The algorithm is divided into two stages： pre-training and real-time prediction. In the pre-training stage， the simulation data is used to train the Particle Swarm Optimization Random Forest （PSO-RF） model. In the real-time prediction stage， based on the error between the trained PSO-RF model and the experimental data， the Radial Basis Function Multi-Fidelity Surrogate （RBF-MFS） model is trained. Finally， by fusing the PSO-RF model and the RBF-MFS model， a digital twin model of the test object is constructed. The results show that the accuracy of the model on the test set is basically about R2=0.97， and the calculation time on the 330 000 grid nodes is only 0.8 s， and the prediction error of the model is less than 10% for the danger area of the wing box segment， which meets the needs of practical engineering applications and provides a reference for the digitization of aircraft structural strength tests.
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