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基于机器学习的预测,以功能分级材料的环状的传热性能
Muhammad Sulaiman1, Osamah Ibrahim Khalaf2, Naveed Ahmad Khan3,4
1Department of Mathematics, Abdul Wali Khan University, 23200, Mardan, Pakistan.
Scientific reports
|April 16, 2024
概括
这项研究分析了功能分级材料 (FGM) 环状,以改善传热. 机器学习准确地预测了的性能,有助于设计高效的热管理系统.
科学领域:
- 热力学和热传递 热力学和热传递
- 材料科学 材料科学 材料科学
- 计算工程 计算工程
背景情况:
- 环状可以在热交换器和电子冷却等系统中增强热传输.
- 功能分级材料 (FGM) 提供量身定制的热性能.
- 了解的性能对于高效的热管理至关重要.
研究的目的:
- 为了研究FGM环状的传热性能和温度分布.
- 在对流和辐射下分析环状的效率.
- 应用机器学习进行深入的性能分析.
主要方法:
- 使用了带有反架构的监督机器学习方法.
- 在数学模型分析中使用了BFGS 准牛顿训练算法 (trainbfg).
- 通过常规方法 (同位体扰动,有限差异,Runge-Kutta) 和统计分析进行比较分析验证结果.
主要成果:
- 准确预测FGM环状的传热特性和温度分布.
- 证明了机器学习方法在分析复杂的热问题中的有效性.
- 在各种物理参数下量化了FGM环状的性能.
结论:
- 机器学习为研究FGM环状表现提供了可靠和高效的方法.
- 这些发现为热系统的设计和优化提供了宝贵的见解.
- 优化的环状设计可以带来更好的热量管理和空间利用.
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