In the present case study, the thermal performance of fins for a novel axial finned-tube heat exchanger is investigated and predicted using machine learning regression technique. The effects of variation in the fin spacing, fin thickness, material, and the convective heat transfer coefficient on the overall efficiency and total effectiveness have been analyzed and commented upon. The k-Nearest Neighbor (k-NN), a machine learning algorithm, is used for regression analysis to predict the thermal performance outputs and the results showed high prediction accuracies. The k-NN algorithm is robust and precise which can be used by thermal system design engineers for predicting output variables. The temperature profiles of various geometries have been depicted and compared in the results. It was concluded that the efficiency is increasing with fin thickness & decreasing with fin spacing and the maximum efficiency ${\eta }_{max}=0.99975$ is achieved at ${\delta }^{\ast }=0.1\&t^{\ast }=0.0133$ having $h=5W/m^2.K$ for copper material. The effectiveness is increasing with fin spacing & fin thickness and the maximum effectiveness ${\epsilon }_{max}=122.766$ is for ${\delta }^{\ast }=8\&t^{\ast }=0.4$ having $h=5W/m^2.K$.

在本案例研究中，使用机器学习回归技术研究并预测了新型轴向翅片管式热交换器的翅片热性能。分析并评论了翅片间距，翅片厚度，材料和对流传热系数变化对整体效率和总效率的影响。所述ķ -Nearest邻居（ķ -NN），机器学习算法，用于回归分析来预测热性能的输出和结果显示高的预测精度。该ķ-NN算法鲁棒且精确，热系统设计工程师可将其用于预测输出变量。描绘了各种几何形状的温度曲线，并在结果中进行了比较。结论是，效率随着翅片厚度的增加而增加，随着翅片间距和最大效率的增加而降低${\eta }_{米\mathrm{一种}X}=0.99975$ 达到 ${\delta }^{\ast }=0.1和{Ť}^{\ast }=0.0133$ 有 $H=5\mathrm{w\; ^}/{米}^2。ķ$用于铜材料。效率随着翅片间距和翅片厚度以及最大效率而增加${\epsilon }_{米\mathrm{一种}X}=122.766$ 是为了 ${\delta }^{\ast }=8和{Ť}^{\ast }=0.4$ 有 $H=5\mathrm{w\; ^}/{米}^2。ķ$。