用双散热变量建模热传导:一种机制-数据融合方法
Leheng Chen1, Chuang Zhang2, Jin Zhao3,4
1School of Mathematical Sciences, <a href="https://ror.org/02v51f717">Peking University</a>, Beijing 100871, China.
Physical review. E
|September 19, 2024
概括
这项研究引入了一种新的数据驱动的深度学习方法,用于非里埃导热模型. 机制-数据融合方法准确地预测了扩散,水力动力和弹道系统的热传输.
科学领域:
- 物理 物理学 物理
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 传统的宏观非里埃导热模型通常依赖于小扰动方法,在高克努森数和非平衡运输中限制其准确性.
- 现有的模型在高度不平衡或弹道导热场景中难以捕捉复杂的热行为.
研究的目的:
- 开发一种新的数据驱动的深度学习策略,用于宏观非里埃导热模型.
- 克服传统基于扰动的方法的局限性,特别是对于高度不平衡和弹道运输系统.
主要方法:
- 介绍了机制-数据融合方法,将不平衡热力学与深度学习相结合.
- 利用保存-消散形式主义 (CDF) 和双消散变量来导出可解释的部分微分方程.
- 采用了一种训练策略,由波博尔兹曼运输方程数据和离散到连续系统桥接的内部步骤操作提供信息.
主要成果:
- 开发的模型在扩散式,水力动力学和弹道热传导模式中显示出出色的预测能力.
- 该方法表现出稳健性和精度,即使在处理不连续的初始条件时也是如此.
- 与传统方法相比,机制-数据融合方法为非里埃导热提供了更全面的方法.
结论:
- 数据驱动的深度学习方法与不平衡热力学相结合,为建模非里埃导热提供了一个强大而多功能工具.
- 这种方法成功地捕获了超出小扰动扩张的局限性的复杂热传输现象.
- 该模型能够处理多种不同的模式和初始条件,这突显了它在先进的热管理应用中的潜力.
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