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在随机多孔介质中通过生成深度学习模型准确预测不连续的裂纹路径
Yuxiang He1, Yu Tan2, Mingshan Yang1
1Applied Mechanics and Structure Safety Key Laboratory of Sichuan Province, School of Mechanics and Aerospace Engineering, Southwest Jiaotong University, Chengdu 610031, People's Republic of China.
概括
一个新的深度学习模型通过分析微观结构图像来预测多孔材料中的裂纹路径. 这一突破使复杂材料的断裂行为能够快速,准确地评估,达到90.25%的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
- 人工智能的人工智能
背景情况:
- 多孔介质具有可调节的特性,但容易发生故障.
- 了解微观结构-故障关系对于设计强大的材料至关重要.
- 在复杂的多孔结构中预测断裂在计算上具有挑战性.
研究的目的:
- 开发一种生成型深度学习模型,用于预测随机多孔介质中的裂纹传播.
- 为了弥合复杂的微观结构配置和断裂反应之间的差距.
- 为了能够对抗裂纹的多孔材料进行高通量评估.
主要方法:
- 采用了两步的深度学习策略,将断裂解构成弹性变形和裂传播.
- 微结构几何学被转化为弹性场作为中间变量.
- 训练了一个神经网络,直接从微观结构图像中预测裂路径.
主要成果:
- 该模型准确地预测了随机多孔介质中的裂纹路径,准确率为90.25%.
- 预测是从输入图像直接快速 (在几秒内) 实现的,不需要额外的物理信息.
- 该模型在各种微观结构中展示了强大的概括能力.
结论:
- 开发的深度学习模型为异质材料中断裂预测提供了一个计算可处理的解决方案.
- 这种方法有助于高通量评估和设计先进的抗裂纹多孔材料.
- 这项研究为预测材料断裂行为的准确性和速度设定了新的基准.
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