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

doi:10.7527/S1000-6893.2025.32180

Abstract

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

CLC Number:

V257

Kairui TANG, Zhe WANG, Xiangming CHEN, Baorang CUI, Yanhui CHEN, Puhui CHEN. A multi-fidelity data-driven framework for predicting mechanical property distributions of composite structures and its validation[J]. Acta Aeronautica et Astronautica Sinica, 2025, 46(21): 532180.

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References 36

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参数	数值	参数	数值
E₁/MPa	166 000	X_T /MPa	3 242
E₂/MPa	9 600	X_C /MPa	1 654
v₁₂	0.31	Y_T /MPa	63.9
G₁₂/MPa	4 600	Y_C/MPa	253
G₁₃/MPa	4 600	S₁₂/MPa	80
G₂₃/MPa	3 330	S₁₃/MPa	80

铺层编号	铺层参数/（°）	铺层比例/%［0/±45/90］	铺层数	切口长度/mm（数量）
L1	［45/-45/90/45/-45/45/-45/0/45/-45］_S	10/80/10	20	13（3），25（3），50（3）
L2	［45/-45/90/0/45/-45/45/-45/90/0］_S	20/60/20	20	13（6），25（6），50（6）
L3	［45/0/-45/90/45/0/90/-45/90/0］_S	30/40/30	20	13（6），25（6），50（6）
L4	［45/0/-45/90/45/0/-45/0/90/0］_S	40/40/20	20	13（3），25（3），50（3）
L5	［45/90/-45/0/0/45/0/0/-45/0］_S	50/40/10	20	13（3），25（3），50（3）
L6	［45/0/0/-45/0/0/90/0/90/0］_S	60/20/20	20	13（6），25（6），50（3）
L01	［45/0/-45/90］_2S	25/50/25	16	25（6）
L0	［45/0/-45/90］_3S	25/50/25	24	13（3），25（3），50（3）
L02	［45/0/-45/90］_4S	25/50/25	32	25（6）

超参数	数值
批次大小	10
学习率	0.001
LSTM规模	［64，40，40，8］
神经网络结构	［10，120，120，1］
激活函数	ReLu

超参数	数值
批次大小	10
学习率	0.001
LSTM规模	［64，40，40，8］
低保真度网络结构	［10，120，120，1］
高保真度网络结构	［11，120，120，1］
激活函数	ReLu

超参数	数值
批次大小	8
学习率	0.001
LSTM规模	［64，40，40，8］
低保真度网络规模	［10，64，64，1］
高保真度网络规模	［11，64，64，1］
激活函数	ReLu
GPR核函数	Matern
GPR更新间隔	5

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

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