融合试验-仿真标定数据的机翼应变载荷关系神经网络模型

doi:10.7527/S1000-6893.2024.30921

Abstract

Abstract:

When establishing a strain-load relationship model for aircraft structures， ground calibration tests can obtain high-fidelity data but are trapped with limited test ranges， while finite element simulations are not limited by test ranges but the data fidelity is low. This leads to difficulties in achieving win-win situation of accuracy and applicability based solely on either ground calibration test data or finite element simulation data. To address the above issue， two multi-level neural network models fusing real and virtual data are put forward， a mapping-based model and a compensation-based model. A method for measuring the model’s cognitive degree based on the variance of base learners is established and embedded into the compensation-based model. A neural network model with high accuracy， wide applicability， and the capability to forewarn unreliable prediction results is then developed. This developed model is validated using a scaled-down wing. Compared with complete reliance on real data from ground calibration tests， the load models based on fusion of multi-source data demonstrate superior capabilities， and the compensation-based model is better than the mapping-based one. Moreover， the compensation-based model can effectively identify the data samples with poor cognitive degree of the load model and thereby provide warnings for unreliable prediction results.

Key words: strain load relationship, aircraft structure, data fusion, neural network model, base learner

CLC Number:

V214.1

Yingjie SHI, Binchao LIU, Songsong LU, Liang CHEN, Hai SHANG, Rui BAO. Neural network model for wing strain-load relationship based on fusion of real and virtual data[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(19): 530921.

Figures/Tables 13

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References 21

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测点编号	位置
1	后梁上表面
2	后梁下表面
3	主梁上表面
4	主梁腹板中间厚度位置
5	前梁上表面
6	前梁腹板中间厚度位置

子学习器编号	神经元数量
1	100
2	110
3	120
4	130
5	140
6	150
7	160
8	170
9	180
10	190
11	200
12	210

测试样本编号	误差/%
测试样本编号	未嵌入子学习器的补偿式神经网络模型	嵌入子学习器的补偿式神经网络模型
1	0.80	-0.13
2	-3.72	-2.91
3	45.79	42.06
4	1.17	0.50
5	4.95	3.96
6	0.34	0.25

Neural network model for wing strain-load relationship based on fusion of real and virtual data

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