|
| Data/physics-driven surrogate models for the nonlinear mechanical response of composite materials |
| Received:January 12, 2023 Revised:March 18, 2023 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20230112001 |
| KeyWord:composite material surrogate model multi-scale analysis data-driven modeling physics-driven modeling |
| Author | Institution |
| 明瑞典 |
北京理工大学 先进结构技术研究院, 北京 |
| 刘云飞 |
北京理工大学 先进结构技术研究院, 北京 |
| 王计真 |
中国飞机强度研究所 结构冲击动力学航空科技重点实验室, 西安 |
| 李想 |
海南师范大学 信息科学技术学院, 海口 |
| 曾庆磊 |
北京理工大学 先进结构技术研究院, 北京 |
|
| Hits: 410 |
| Download times: 118 |
| Abstract: |
| Composites composed of two or more different materials are widely used in industrial fields because of their excellent mechanical properties.The analysis of multi-scale response of macroscopic composite structures requires a large amount of computations,which brings challenges to the development of efficient numerical methods.In recent years,the rapid development of artificial intelligence such as machine learning has created great opportunities for the efficient and accurate mechanical analysis of composite materials.But most mechanical surrogate models for multi-scale analysis of composite materials are purely data-driven,which lack physical interpretation.For the nonlinear mechanical response of the representative volume element of hyperelastic composites,three kinds of surrogate models are established based on data/physics-driven neural networks,employing different construction strategies to integrate physical interpretation into the models.By predicting the equivalent response of the representative volume element,the performance of the three models is analyzed,considering computational efficiency,accuracy and the range of applications.This work sheds more light on the establishment of effective surrogate models for the mechanical response of composites,balancing data and physics. |
|
|
|