一个深度学习框架,用于预测表面地形对热接触电阻的影响
Man Zhou1, Zhuoyan He1, Peiyao Guo1
1School of Mechanical and Electrical Engineering, Guilin University of Electronic technology, Guilin, Guangxi, China.
Communications engineering
|October 10, 2025
概括
深度学习通过分析表面地形学,准确地预测热接触电阻. 这种方法揭示了影响热传输的关键特征,改善了热管理策略.
科学领域:
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
- 计算科学 计算科学
背景情况:
- 有效的传热对于热管理至关重要.
- 由于表面地形造成的热接触阻力阻碍了热传递.
- 现有的方法很难完全解释影响热接触电阻的因素.
研究的目的:
- 开发一种深度学习模型,用于预测热接触电阻.
- 想象和理解影响热接触电阻的关键表面特征.
- 通过实验数据验证模型的预测.
主要方法:
- 开发了一个卷积神经网络 (CNN) 模型.
- 在使用表面碎形理论生成的数据集上训练了CNN.
- 用实验测量钢样本的表面地形和热电阻来验证模型性能.
主要成果:
- CNN模型准确地预测了热接触电阻.
- 该模型有效地估计了表面之间的实际接触面积.
- 解释性分析强调了接触和非接触表面区域对传热的重大影响.
结论:
- 深度学习为理解和预测热接触电阻提供了一个强大的工具.
- 该模型在解释热传递变化方面超越了传统的粗度参数.
- 这种方法提高了对热管理应用的基本理解和预测能力.
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