可靠的模型来计算不同的流动方向的光滑螺旋管内部的冷凝热传递,使用智能计算技术
Chou-Yi Hsu1, Nikunj Rachchh2, T Ramachandran3
1Thunderbird School of Global Management, Arizona State University Tempe Campus, Phoenix, AZ, 85004, USA.
Scientific reports
|August 19, 2025
概括
机器学习模型准确地预测螺旋管中的凝结热传递系数 (HTC). 高斯过程方法 (GPM) 为各种工业应用提供了比现有模型更高的精度.
科学领域:
- 热传递热量转移的方法
- 流体动力学 流体动力学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 带螺旋管的冷凝器在各种行业中至关重要.
- 准确预测凝结热传递系数 (HTC) 对最佳设备设计至关重要.
- 现有的HTC文献模型仅限于特定条件.
研究的目的:
- 开发可靠的机器学习 (ML) 模型,用于光滑螺旋管中的凝结HTC.
- 涵盖所有流动方向和各种运行条件.
- 确定两个相的努塞尔特数的主导无维参数.
主要方法:
- 实施的支持矢量机 (SVM) 和高斯过程方法 (GPM) ML技术.
- 利用来自10项研究的563个实验HTC数据点进行模型开发和验证.
- 进行了灵敏度分析以确定关键的无维参数.
主要成果:
- 在测试数据集上,GPM实现了最高的准确性,MAPE为3.36%,R2为99%.
- 超过96%的GPM预测在±5%的误差范围内.
- 文献相关性显示超过25%的MAPE,而ML模型准确地预测了HTC的所有流动方向.
结论:
- 开发了高精度的ML模型 (特别是GPM) 来预测螺旋管中的凝结HTC.
- 机器学习模型克服了传统相关性的局限性,在所有流量条件下提供精确的预测.
- 确定了影响螺旋管中双相努塞尔特数的关键无维参数.
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