论文标题
用扭曲的胶带插入的热液相关性的机器学习辅助建模
Machine learning assisted modeling of thermohydraulic correlations for heat exchangers with twisted tape inserts
论文作者
论文摘要
本文介绍了机器学习（ML）算法在传热相关性（例如Nusselt数量和摩擦系数）建模中的应用，并在带有扭曲胶带插入的热交换器中。在不同的雷诺数和扭曲比率下进行热交换器的实验数据用于相关建模。三种机器学习算法：多项式回归（PR），随机森林（RF）和人工神经网络（ANN）用于数据驱动的替代建模。 ML模型的超参数仔细优化，以确保概括性。分析了不同ML算法的性能参数（例如$ r^2 $和$ MSE $）。据观察，热传递系数的ANN预测在不同的测试数据集上优于PR和RF的预测。基于我们的分析，我们为将来的数据驱动建模工作提出了建议，对传热相关性和类似研究提出了建议。
This article presents the application of machine learning (ML) algorithms in modeling of the heat transfer correlations (e.g. Nusselt number and friction factor) for a heat exchanger with twisted tape inserts. The experimental data for the heat exchanger at different Reynolds numbers and twist ratios were used for the correlation modeling. Three machine learning algorithms: Polynomial Regression (PR), Random Forest (RF), and Artificial Neural Network (ANN) were used in the data-driven surrogate modeling. The hyperparameters of the ML models are carefully optimized to ensure generalizability. The performance parameters (e. g. $R^2$ and $MSE$) of different ML algorithms are analyzed. It was observed that the ANN predictions of heat transfer coefficients outperform the predictions of PR and RF across different test datasets. Based on our analysis we make recommendations for future data-driven modeling efforts of heat transfer correlations and similar studies.
