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

DOĞAN, Ayhan; KORKMAZ, Murat; Kırmacı, Volkan

doi:10.1016/j.ijrefrig.2023.01.021

Cited by 5 publications

(2 citation statements)

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“…Given the intricate and nonlinear correlations among multiple parameters and the thermal effect of the vortex tube, the machine learning technologies with robust regression mapping capabilities have been employed to estimate the vortex tube performance. [29][30][31][32] These approaches have showed great potential in quantitatively modeling and predicting the temperature separation effect. For example, Korkmaz et al 33 employed an artificial neural network (ANN) method and developed a ternary diagram to assess temperature differences for oxygen gas and air.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine learning‐assisted analysis and prediction for the thermal effect of various working fluids in a vortex tube

Wang,

Li,

Zhong

et al. 2023

Asia-Pacific J Chem Eng

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With the widespread application of vortex tube in various fields, it becomes essential to quantitatively explore the separation effect of different fluids (natural fluids, hydrocarbons, etc.) within the vortex tube and to promote its utilization. A new approach has been developed in this study to establish quantitative models for predicting the thermal effects of different fluids in a vortex tube. These models are based on both the macro properties of the working fluid and micro molecular descriptor through a molecular scale. A dataset of 11 numerical simulation results of hydrocarbons and hydrofluorocarbons refrigerants is employed. Three operating conditions, 10 property parameters, and 115 molecular descriptors are screened and identified using random forest feature analysis. Two types of models (micro and macro) have been developed by employing artificial neural network (ANN) modeling techniques. In the result, four key influencing fluid property parameters (the specific heat ratio γ, the multiplication of heat capacity and the molar weight cp·M, the kinematic viscosity ν, and the thermal conductivity λ) and nine molecular descriptors in affecting the thermal effect are identified and respectively chosen as the input in the macro and the micro ANN model establishment. Both types of developed models show a high correlation coefficient (R > .999) and a comparatively low mean square error (MSE). When R600 is employed in the validation, most of the relative error is less than 10%, suggesting both types of models can work effectively in predicting the thermal effect for other fluid. The findings contribute to a deeper understanding of the thermodynamic effect of vortex tubes and provide a valuable tool for selecting and optimizing working fluids in various applications.

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