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:10.2139/ssrn.4236310

Cited by 1 publication

(3 citation statements)

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“…The choice of the backpropagation algorithm as the learning mechanism is motivated by its widespread adaptability and suitability. 29,33 As for the transfer functions, the sigmoidal function is applied to the hidden layer, while the linear function was used for the input/output layer. The optimal number of neurons in the hidden layer is determined through a trial-and-error approach, with the overarching objective of minimizing the mean square error (MSE, which refers to the average of the squared distances between the model's predicted value f(x) and the sample's true value y.…”

Section: Feature Screeningmentioning

confidence: 99%

“…In the model development process, 70% of the total data points are randomly allocated for training, 15% are selected as validation, and the rest is chosen for testing. The choice of the backpropagation algorithm as the learning mechanism is motivated by its widespread adaptability and suitability 29,33 . As for the transfer functions, the sigmoidal function is applied to the hidden layer, while the linear function was used for the input/output layer.…”

Section: Ann Model Constructionmentioning

confidence: 99%

“…[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. The highest coefficient of determination has been obtained when pressure, number of nozzles, and thermal conductivity parameters are considered in the process.…”

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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Section: Feature Screeningmentioning

confidence: 99%

Section: Ann Model Constructionmentioning

confidence: 99%