Artificial neural network-based mapping of microscopic damage to macroscopic stiffness in solid propellants

ZHANG Taotao; YANG Yuxin; ZHANG Erhan; XIAO Jinyou; LV Haibao; WEN Lihua; LEI Ming; HOU Xiao

doi:10.13801/j.cnki.fhclxb.20240423.003

Volume 41 Issue 9

Sep. 2024

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ZHANG Taotao, YANG Yuxin, ZHANG Erhan, et al. Artificial neural network-based mapping of microscopic damage to macroscopic stiffness in solid propellants[J]. Acta Materiae Compositae Sinica, 2024, 41(9): 4693-4705. doi: 10.13801/j.cnki.fhclxb.20240423.003

Citation:

ZHANG Taotao, YANG Yuxin, ZHANG Erhan, et al. Artificial neural network-based mapping of microscopic damage to macroscopic stiffness in solid propellants[J]. Acta Materiae Compositae Sinica, 2024, 41(9): 4693-4705. doi: 10.13801/j.cnki.fhclxb.20240423.003

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Artificial neural network-based mapping of microscopic damage to macroscopic stiffness in solid propellants

doi: 10.13801/j.cnki.fhclxb.20240423.003

1.
School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China
2.
Xi'an Aerospace Power Technology Research Institute, Xi'an 710025, China
3.
Institute of Composite Materials and Structures, Harbin Institute of Technology, Harbin 150001, China
4.
China Aerospace Science and Technology Corporation, Beijing 100048, China

Funds: Project Cross Scale Analysis of Structural Damage to High Charge Ratio Energetic Propellant Charges (U22B20131) supported by National Natural Science Foundation of China - Joint Fund

Received Date: 2024-02-18
Accepted Date: 2024-04-12
Rev Recd Date: 2024-03-28

Available Online: 2024-04-23

Publish Date: 2024-09-15

Abstract

Abstract

As a particle-reinforced polymer composite with high inclusion ratio, the macro-mechanical properties of solid propellants depend on their meso-structures. Especially, under external loads, the stress concentration usually happens besides the regions of the initial imperfections and the particle agglomerations, leading to the interfacial debonding between the particles and the polymeric binders, consequently deteriorating macroscopic mechanical properties. How to build a relationship between the microscopic damage states and the macroscopic mechanical performances is the key issue for both the rational usage of the microscopic experimental results of solid propellants and the accurate prediction of structural disasters in solid rocket motors. For this purpose, this article develops an artificial neural network (ANN) based on the framework of continuum mechanics, with the scalar invariant of the deformation gradient tensor as the input and the scalar free energy as the output. Existing free energy functions and damage growth functions are selected as the activation functions of the ANN, and therefore the ANN can naturally satisfy the requirements of the continuum mechanics, including the deformation continuity, the coordinate invariance, and the thermodynamic consistency. These merits can guarantee the rapid convergence of the ANN with sparse training data, and additionally can obtain a bottom-up mapping of the microscopic damage states towards the macroscopic mechanical performances. Finally, using the dataset obtained from finite element analysis, the predictive ability of the ANN on the mechanical properties of solid propellants with different pre-damage states under uniaxial tension, biaxial tension, and pure shear are validated.
- solid propellants,
- constitutive model,
- artificial neural network,
- microscopic damage,
- macroscopic and microscopic scale-effect
Long Abstrsct
Innovation Points

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References(39)

References

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