Material Fracturing and Failure Simulation Datasets

Fracturing is a fundamental physics phenomena with broad relevance across multiple domains, ranging from infrastructure integrity, aerospace durability, reservoir production, and seismic events. We present a diverse dataset of simulated fracture evolution and material failure generated from two numerical solvers: the phase-field method and the combined finite-discrete element method (FDEM). These solvers differ in formulation, physical fidelity, and computational efficiency. The dataset includes five materials: PBX, anisotropic shale, tungsten, aluminum, and steel. For each, phase-field simulations span 400,000 cases: 200,000 under uniaxial tension and 200,000 under biaxial tension. The computationally expensive FDEM simulations include 90,000 split evenly among PBX, shale, and tungsten under uniaxial loading. All simulations begin with randomized initial fracture patterns. Each entry includes temporal data capturing fracture propagation dynamics. This comprehensive dataset is designed to support the development of foundational or surrogate machine learning approaches for predicting material failure. While no such models are introduced here, the dataset lays a robust foundation for advancing future research and innovation in these areas.

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Publication Details

Journal
Scientific Data
Published
2025-12-13
DOI
https://doi.org/10.1038/s41597-025-06412-8
Primary Topic
Numerical methods in engineering
Type
article
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article

Material Fracturing and Failure Simulation Datasets

Zhou Lei, Vinamra Agrawal, Esteban Rougier, Aleksandra Pachalieva et al.
Scientific Data
Numerical methods in engineering
article

Material Fracturing and Failure Simulation Datasets

Zhou Lei, Vinamra Agrawal, Esteban Rougier, Aleksandra Pachalieva, Abigail Hunter, Jeffrey D. Hyman, Xiaoyu Wang, Agnese Marcato, Qinjun Kang, Ke Gao, Daniel O’Malley, Hari Viswanathan, Christine Sweeney, Javier E. Santos, Pascal Grosset, Ryley Hill, Earl Lawrence, Nathan DeBardeleben
article en

Abstract

Fracturing is a fundamental physics phenomena with broad relevance across multiple domains, ranging from infrastructure integrity, aerospace durability, reservoir production, and seismic events. We present a diverse dataset of simulated fracture evolution and material failure generated from two numerical solvers: the phase-field method and the combined finite-discrete element method (FDEM). These solvers differ in formulation, physical fidelity, and computational efficiency. The dataset includes five materials: PBX, anisotropic shale, tungsten, aluminum, and steel. For each, phase-field simulations span 400,000 cases: 200,000 under uniaxial tension and 200,000 under biaxial tension. The computationally expensive FDEM simulations include 90,000 split evenly among PBX, shale, and tungsten under uniaxial loading. All simulations begin with randomized initial fracture patterns. Each entry includes temporal data capturing fracture propagation dynamics. This comprehensive dataset is designed to support the development of foundational or surrogate machine learning approaches for predicting material failure. While no such models are introduced here, the dataset lays a robust foundation for advancing future research and innovation in these areas.

Scientific DataVol. 13(1)
Los Alamos National Laboratory (US), Los Alamos National Security (United States) (US)
Openalex Percentile: Top 56%
Numerical methods in engineering
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