航空学报2025，Vol.46Issue(19)：106-117,12.DOI:10.7527/S1000-6893.2024.30921
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
Neural network model for wing strain-load relationship based on fusion of real and virtual data
摘要
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 estab-lished and embedded into the compensation-based model.A neural network model with high accuracy,wide applica-bility,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 mod-els based on fusion of multi-source data demonstrate superior capabilities,and the compensation-based model is bet-ter 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.关键词
应变载荷关系/飞机结构/数据融合/神经网络模型/子学习器Key words
strain load relationship/aircraft structure/data fusion/neural network model/base learner分类
航空航天引用本文复制引用
施英杰,刘斌超,鲁嵩嵩,陈亮,尚海,鲍蕊..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[J].航空学报,2025,46(19):106-117,12.基金项目
强度与结构完整性全国重点实验室自主研究课题 National Key Laboratory of Strength and Structural Integrity Independent Research Project （）
