航空学报 > 2025, Vol. 46 Issue (21): 532180-532180   doi: 10.7527/S1000-6893.2025.32180

中国飞机强度研究所建所 60 周年专刊

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复合材料结构力学性能分布的多保真度数据驱动预测框架及验证

唐铠瑞1, 王喆2,3, 陈向明3, 崔保让4, 陈艳辉4, 陈普会1()   

  1. 1.南京航空航天大学 机械结构力学及控制国家重点实验室,南京 210016
    2.北京航空航天大学 航空科学与工程学院,北京 100191
    3.中国飞机强度研究所 强度与结构完整性全国重点实验室,西安 710065
    4.中国商飞上海飞机制造有限公司 复合材料中心,上海 200123
  • 收稿日期:2025-04-29 修回日期:2025-06-17 接受日期:2025-07-03 出版日期:2025-07-21 发布日期:2025-07-15
  • 通讯作者: 陈普会 E-mail:phchen@nuaa.edu.cn

A multi-fidelity data-driven framework for predicting mechanical property distributions of composite structures and its validation

Kairui TANG1, Zhe WANG2,3, Xiangming CHEN3, Baorang CUI4, Yanhui CHEN4, Puhui CHEN1()   

  1. 1.State Key Laboratory of Mechanics and Control of Mechanical Structures,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China
    2.School of Aeronautic Science and Engineering,Beihang University,Beijing 100191,China
    3.National Key Laboratory of Strength and Structural Integrity,Aircraft Strength Research Institute of China,Xi’an 710065,China
    4.Center of Composite Materials,COMAC Shanghai Aircraft Manufacturing Company Limited,Shanghai 200123,China
  • Received:2025-04-29 Revised:2025-06-17 Accepted:2025-07-03 Online:2025-07-21 Published:2025-07-15
  • Contact: Puhui CHEN E-mail:phchen@nuaa.edu.cn

摘要:

碳纤维增强复合材料的失效机理复杂,试验成本高昂。受限于现有理论模型,传统的有限元方法难以精确模拟复合材料失效全过程,并存在显著的累积误差,造成了其精确模拟和不确定性量化的困难。机器学习方法为解决这一问题提供了潜在途径,但其有效性通常依赖于大量数据支撑。提出了一种多保真度数据驱动预测框架,旨在通过结合少量高保真度试验数据与大量低保真度仿真数据,实现对复合材料结构力学性能分布的准确预测。该框架的有效性经过切口层合板拉伸破坏试验验证。此外,为提高小样本试验数据的统计代表性,提出了一种基于贝叶斯理论的数据扩充方法,并推导了组间变异系数的理论分布以验证其有效性。交叉验证结果表明,所提方法在预测破坏载荷分布第10百分位数时的平均绝对误差率仅为4.99%。该研究有效缓解了复合材料结构性能预测过程中数值模型与物理试验数据关联困难及试验数据稀缺的问题。

关键词: 复合材料, 切口层合板, 贝叶斯理论, 多保真度方法, 破坏载荷分布预测

Abstract:

The failure mechanisms of carbon-fiber-reinforced composites are complex, and experimental tests are costly. Traditional finite element methods, limited by current theoretical models, struggle to accurately simulate the entire failure process and exhibit significant cumulative errors, complicating precise modeling and uncertainty quantification. Machine learning approaches offer a promising alternative but generally require extensive datasets to achieve satisfactory performance. We present a multi-fidelity data-driven framework that blends a small set of high-fidelity test results with an extensive collection of low-fidelity simulation data to predict the distribution of mechanical properties in composite structures. The framework is validated through tensile-failure experiments on notched laminates. To improve the statistical representativeness of the limited experimental samples, we introduce a Bayesian data-augmentation scheme and derive the theoretical distribution of the inter-group coefficient of variation to confirm its soundness. Cross-validation shows that the proposed method attains a mean absolute error of 4.99% when predicting the 10th percentile of the failure-load distribution. The study mitigates the twin challenges of scarce experimental data and weak coupling between numerical models and physical tests.

Key words: composite materials, notched laminates, Bayesian theory, multi-fidelity methods, failure load distribution prediction

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