Prediction of temperature separation of a nitrogen-driven vortex tube with linear, kNN, SVM, and RF regression models

In this study, the performance of a counter-flow Ranque–Hilsch vortex tube (RHVT) was investigated experimentally using working fluid nitrogen, and the thermal performance was modeled using different modeling methods with these experimental results. Each method was compared with the others. The variation of ΔT, which is the measure of temperature separation in RHVT, was investigated by using nozzle number, the thermal conductivity of nozzle material, inlet pressure, specific heat, and density of working fluid. In the study, the prediction models of linear, k-nearest neighbor (kNN), random forest (RF), and support vector machine (SVM) regression were trained with measured thermal performance using a specific portion of the experimental data and tested with the remaining data. Two different train and test datasets were used with the ratio of experimental data as 90–10% and 80–20%, respectively. The highest accuracy ratio was determined at the end of the four methods with SVM regression as 96.01% when using the train and test datasets as 90–10%, respectively. The percent accuracy of the other models under the same conditions was calculated as 95.7, 90.87, and 78.36% for RF, kNN, and linear, respectively.