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A Method for Studying the Temperature Dependence of Dynamic Fracture and Fragmentation
Published on: June 28, 2015
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Phase-field simulation of thermal cracking with length-scale insensitive degradation functions
Lusheng Yang1, Gengyin Yang2, Yujing Ma2
1College of Basic Courses, Shanxi Institute of Energy, Jinzhong, China.
Science Progress
|March 19, 2025
Summary
This study introduces a new method to speed up simulations of thermal cracking by using a length-scale insensitive degradation function. This approach accurately predicts crack formation in ceramics, aiding large-scale thermal stress analysis.
Area of Science:
- Computational mechanics
- Materials science
- Thermal engineering
Background:
- Thermal cracking is a critical failure mechanism in many materials, particularly ceramics.
- Simulating crack propagation under thermal loads is computationally intensive.
- Existing phase-field models often require fine meshes, increasing computational cost.
Purpose of the Study:
- To develop an accelerated simulation method for thermal cracking problems.
- To introduce a length-scale insensitive degradation function for phase-field models.
- To accurately predict thermal crack initiation and propagation in solids.
Main Methods:
- Coupling solid deformation, damage evolution, and heat conduction.
- Representing cracks using diffusive phase-field variables.
- Employing a length-scale insensitive degradation function to decouple phase-field and physical scales.
Main Results:
- The proposed method significantly accelerates thermal cracking simulations.
- Numerical results for ceramic examples show good agreement with experimental data.
- The length-scale insensitivity alleviates the meshing burden associated with phase-field models.
Conclusions:
- The developed length-scale insensitive degradation function effectively simulates thermal cracking.
- This approach offers a computationally efficient tool for predicting thermal failure in materials.
- The findings have significant implications for large-scale thermal cracking predictions.
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