Estimation of Ranque-Hilsch vortex tube performance by machine learning techniques.

• The performance of counter-flow Ranque-Hilsch Vortex Tube (RHVT) was modelled with respect to pressure, working fluid and nozzle specifications. • Linear Regression (LR), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Regression Trees (RT) and Ensembles of Trees (ET) prediction methods were used. • Optimizing the performance of counter-flow RHVT, it is aimed to fill the gap in the literature by using LR, SVM, GPR, RT and ET methods among the machine learning methods. This study planned to model a counter-flow Ranque-Hilsch Vortex Tube (RHVT) using compressed air and oxygen gas by machine learning to separate the thermal temperature. From within machine learning models, Linear Regression (LR), Support Vector Machines (SVM), Gaussian Process Regression (GPR), Regression Trees (RT), and Ensemble of Trees (ET) were preferred. By leaving the outlet control valve on the hot fluid side fully open, data were received for each material and nozzle at RHVT with inlet pressure starting from 150 kPa and up to 700 kPa at 50 kPa intervals. In the counter flow RHVT, the lack in the literature has been tried to be eliminated by modeling the RHVT by finding the difference (ΔT) between the temperature of the cold flow exiting (T c) and the temperature of the leaving hot flow (T h). When analyzing each of the machine learning models in the study, 80% of all data was used as training data, 20% of all data was used for the test, 70% of all data was used as training data, and 30% of all data was used for the test. As a result of the analysis, when both air and oxygen fluids were used, the GPR method gave the best result with 0.99 among the machine learning models in two different test intervals of 70%–30% and 80%–20%. The success of other machine learning models differed according to the fluid and model used. [ABSTRACT FROM AUTHOR]