ANN based ternary diagrams for thermal performance of a Ranque Hilsch vortex tube with different working fluids

KORKMAZ, Murat; Binal, Adil; Kaya, Hüseyin; Kırmacı, Volkan

doi:10.1016/j.tsep.2023.101803

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…According to the investigation, counter flow produces superior results to uni-flow vortex tubes. Figure 1 depicts the flow process inside the vortex tube [3]. When the compressed gas injected through the inlet nozzle, a strong swirl of a gas stream is created in the centre and induces a high-speed rotation of the fluid flow [4].…”

Section: Introductionmentioning

confidence: 99%

Effect of Tube Length on the Performance of Cooling Necklace with Vortex Tube Cold Flow Generator

Adnan,

Hisamudin,

Yusof

et al. 2024

J. Phys.: Conf. Ser.

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The vortex tube, a compact and eco-friendly device, can generate both low-temperature cold flow and high-temperature hot flow from a room-temperature compressed gas supply. Despite its potential, there has been limited research on its application as a cooling device, particularly in the context of a cooling necklace. This study aims to fill this gap by examining the performance of the vortex tube under various conditions. The experiment was conducted indoors, with tube lengths ranging from 60 cm to 140 cm and inlet pressures from 0.2 MPa to 0.4 MPa. The results suggest that an inlet pressure of 0.4 MPa and a tube length of 60 cm are optimal for achieving the lowest temperature cold flow and minimizing heat transfer effects between the cold flow and the environment.

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Section: Introductionmentioning

confidence: 99%

Effect of Tube Length on the Performance of Cooling Necklace with Vortex Tube Cold Flow Generator

Adnan,

Hisamudin,

Yusof

et al. 2024

J. Phys.: Conf. Ser.

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“…Dincer et al [ 16 ] used the experimental data to simulate the effects of length-diameter ratio and the number of nozzles on the performance of counterflow Runque-Hilsch vortex tubes with artificial neural network (ANN). Korkmaz [ 17 ] Study of vortex tube temperature using artificial neural network ternary map with working fluid (oxygen, air) and nozzle material as influential parameters. Kaya [ 18 ] applied four methods, linear, KNN, RF, and SVM, to experimentally investigate the thermal performance of vortex tube with nitrogen as the working fluid.…”

Section: Introductionmentioning

confidence: 99%

Research on the prediction model of energy separation effect of vortex tube based on trajectory deflection characteristics and its numerical solution method

Liu,

Cao,

Han

et al. 2024

Heliyon

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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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ANN based ternary diagrams for thermal performance of a Ranque Hilsch vortex tube with different working fluids

Cited by 5 publications

References 26 publications

Effect of Tube Length on the Performance of Cooling Necklace with Vortex Tube Cold Flow Generator

Effect of Tube Length on the Performance of Cooling Necklace with Vortex Tube Cold Flow Generator

Research on the prediction model of energy separation effect of vortex tube based on trajectory deflection characteristics and its numerical solution method

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

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