Performance analysis of a parallel-counterflow vortex tube using machine learning methods - Korkmaz, Murat; Dogan, Ayhan; Kırmacı, Volkan; Kırmacı, Volkan (Springer, 2025)
In this study, these machine learning methods (MLMs) were used for the first time in the literature to compare the temperature performance of two counterflow Ranque-Hilsch vortex tubes (RHVT) connected in parallel. As input parameters, two different pressurized fluids, three distinct materials, and six types of nozzles were selected, while the temperature difference was obtained from the hot and cold fluid outlets was designated as the output parameter. For the first time under these experimental conditions, a sensitivity analysis was conducted to determine the impact of the input parameters on the output. For each pressurized fluid and material, six models were developed using MLMs such as elastic net (EN), Bayesian ridge (BR), category boosting (CB), and light gradient boosting machine (LightGBM). A total of 30 different experimental setups were established by creating five separate setups for each model. The performance of these models was evaluated and compared using the K-fold cross-validation method. Upon analyzing the results, the BR method demonstrated the best performance for Model 1 (air, aluminum), Model 3 (air, polyamide), Model 4 (oxygen, aluminum), Model 5 (oxygen, brass), and Model 6 (oxygen, polyamide), with R2 values of 94%, 68%, 96%, 94%, and 95%, respectively. For Model 2 (air, brass), the highest performance was achieved using the CB method, with an R2 value of 93%. In this study, the optimal cooling performance results from the two parallel-connected counterflow RHVT tubes were achieved with Model 5. This model, operating at a pressure of 700 kPa and utilizing a brass material with six nozzles, produced a cooling performance of -242.55 K. The primary contribution of this research lies in the first-time application of the specified ML methods collectively for predicting the performance of the PCRHVT system, resulting in highly accurate prediction outcomes.
Keywords: Machine Learning; Ranque-Hilsch Vortex Tube; Heating; Cooling; Sensitivity Analysis