In this study, the innovative compact heat exchanger (CHE) newly designed and manufactured using metal additive manufacturing technology were numerically and experimentally investigated. Some experiments were carried out to determine the hot water (\(hw\)) and cold water (\(cw\)) outlet temperatures of CHE. As a result of the CFD analysis, the average outlet temperatures of the \(hw\) and \(cw\) flow loops on the CHE were calculated as 48.24 and 35.38 °C, respectively. On the other hand, the experimental outlet temperatures were measured as being 48.50 and 35.72 °C, respectively. The studies showed that the numerical and experimental results of the CHE are compliant at the given boundary conditions. Furthermore, it was observed that the heat transfer rate of the CHE with lower volume is approximately 47.7% higher than that of standard brazed plate heat exchangers (BPHEs) produced by traditional methods. More experiments conducted on the CHE will inevitably have a negative effect on its manufacture time and cost. Thus, various models were developed to predict the results of unperformed experiments using the machine learning methods, ANN, MLR and SVM. In the models developed for each experiment, the source and inlet temperatures of \(hw\) and \(cw\), respectively, and the volumetric flow rate of \(cw\) were selected as input parameters for the machine learning methods. Thus, the \(hw\) and \(cw\) outlet temperatures of the CHE were estimated on the basis of these input parameters. The best performance was achieved by ANN. In addition, there is no significant performance difference between other methods.

在这项研究中，对使用金属增材制造技术新设计和制造的创新型紧凑型热交换器（CHE）进行了数值和实验研究。进行了一些实验以确定CHE的热水（\（hw \））和冷水（\（cw \））出口温度。CFD分析的结果是\（hw \）和\（cw \）的平均出口温度CHE上的流动环路分别计算为48.24和35.38°C。另一方面，实验出口温度分别测量为48.50和35.72°C。研究表明，在给定的边界条件下，CHE的数值和实验结果均符合要求。此外，已观察到，体积较小的CHE的传热速率比传统方法生产的标准钎焊板式换热器（BPHE）大约高47.7％。在CHE上进行的更多实验将不可避免地对其制造时间和成本产生负面影响。因此，使用机器学习方法ANN，MLR和SVM，开发了各种模型来预测未完成实验的结果。在为每个实验开发的模型中，源和入口温度为\（HW \）和（CW \）\，分别和的体积流速\（CW \）被选定为用于机器学习方法的输入参数。因此，\（HW \）和\（CW \）的CHE的出口温度估计这些输入参数的基础上。ANN取得了最佳性能。此外，其他方法之间没有显着的性能差异。