翼面结构强度试验的数据驱动虚实融合方法

doi:10.7527/S1000-6893.2026.33068

Abstract

Abstract:

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.

Key words: digital twin, virtual-real fusion, structural strength test, random forest, multi-fidelity surrogate model

CLC Number:

V214.5

Yan WEI, Jianjiang ZENG, Xuan WANG, Xiaolong ZHU, Tao CHEN, Mingbo TONG. Data-driven virtual-real fusion method for wing structure strength test[J]. Acta Aeronautica et Astronautica Sinica, 2026, 47(16): 233068.

Figures/Tables 16

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References 24

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零件名称	编号	材料
蒙皮	上蒙皮、下蒙皮	玻璃纤维+碳纤维+泡沫夹心
前主梁、后主梁	2#翼梁、3#翼梁	30CrMnSiA
前梁、后梁、内侧肋、外侧肋	1#翼梁、4#翼梁、4#翼肋、5#翼肋	7075铝合金

模型参数	RF	PSO-RF
n	100	23
Sample	0.2	0.83
Depth	50	43
Features	1.0	0.89

编号	应变片位置	应变/με		应变绝对误差/με	相对误差/%
编号	应变片位置	预测值	试验值	应变绝对误差/με	相对误差/%
12	上蒙皮内表面	-332.68	-324.00	8.68	2.68
16	上蒙皮内表面	-1 072.91	-927.00	145.91	15.74
21	上蒙皮内表面	-573.18	-698.00	124.82	17.88
24	上蒙皮内表面	-756.63	-766.00	9.37	1.22
29	上蒙皮内表面	-1 074.6	-907.00	167.6	18.48
47	下蒙皮内表面	708.09	675.00	33.09	4.9
50	下蒙皮内表面	375.92	405.00	29.08	7.18
53	下蒙皮内表面	1 011.78	902.00	109.78	12.17
102	前主梁下翼面缘条转折处	1 610.44	1 714.00	103.56	6.04
118	前主梁上翼面缘条转折处	-2 219.49	-2 291.00	71.51	3.12
123	后主梁上翼面下缘条	668.89	-647.00	21.89	3.38
132	下蒙皮外表面	-474.75	481.00	6.25	1.3
151	上蒙皮外表面	-444.74	-383.00	61.74	16.12
184	加载段上翼面后过渡角盒	-101.61	-95.00	6.61	6.95
191	前主梁上翼面缘条	-643.95	-724.00	89.05	12.3

Data-driven virtual-real fusion method for wing structure strength test

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