Open Access

REVIEW

Multiscale Numerical Simulation of Dynamic Damage and Fracture in Metallic Materials: A Review

Bin Gao¹, Xinyu Jiang¹, Lusheng Wang^1,*, Jun Ding¹, Yanhong Peng¹, Xin Yang², Hongzhou Yan³, Shaojie Gu^4,5,*

1 College of Mechanical Engineering, Chongqing University of Technology, Banan, China
2 School of Environment and Resources, Southwest University of Science and Technology, Mianyang, China
3 State Key Laboratory of Precision Welding and Joining of Materials and Structures, Harbin Institute of Technology, Harbin, China
4 Magnesium Research Center, Kumamoto University, Kumamoto, Japan
5 Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto, Japan

* Corresponding Authors: Lusheng Wang. Email: ; Shaojie Gu. Email:

Computers, Materials & Continua 2026, 87(3), 1 https://doi.org/10.32604/cmc.2026.077091

Received 02 December 2025; Accepted 26 February 2026; Issue published 09 April 2026

Abstract

This paper provides a comprehensive review of recent advances in multi-scale modeling for simulating dynamic damage and fracture in metallic materials, a critical area due to the widespread application of metals and their susceptibility to complex failure in engineering practice. The paper first outlines the mechanisms of damage evolution and crack propagation across different spatial and temporal scales. It then introduces commonly used simulation approaches spanning micro- to macro-scales for studying damage and fracture in metals, analyzing the evolution of mechanical properties from defect initiation to ultimate failure, and elucidating the underlying damage mechanisms at different scales. Finally, the review summarizes multi-scale coupling strategies and mechanisms, as well as the integration of machine learning (ML) into multi-scale frameworks. These advanced approaches are recognized as key tools for improving predictive accuracy and computational efficiency, facilitating the scalability of multi-scale damage modeling for metallic materials in large-scale engineering applications and digital twin platforms. This review aims to provide a theoretical foundation for future research toward more reliable, efficient, and predictive multi-scale modeling of metallic materials.

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Keywords

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