Modeling of the effects of length to diameter ratio and nozzle number on the performance of counterflow Ranque–Hilsch vortex tubes using artificial neural networks

Dinçer, Kevser; Taşdemir, Şakir; Baskaya, Senol; Uysal, Bekir Zühtü

doi:10.1016/j.applthermaleng.2008.01.016

Cited by 91 publications

(36 citation statements)

References 20 publications

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“…(Rafiee & Sadeghiazad, 2014) on number of nozzles and size of nozzles. The findings were similar to that of experimental results, a mix prediction exists, wherein some researchers (Dincer, Tasdemir, Baskaya, & Uysal, 2008;Uluer, Kirmaci, & Atas, 2009;Kocabas, Korkmaz, Sorgucu, & Donmez, 2010;Polat & Kirmaci, 2011;Pourmahmoud, Hassanzadeh, & Moutaby, 2012) have observed drop in performance of energy separation with increase in nozzle number. Some of these (Ameri & Behnia, 2009;Rafiee & Rahimi, 2013) have noticed increase in cooling capacity/energy separation/ performance for converging shape nozzles.…”

Section: Effect Of Nozzle Number and Nozzle Geometry (Nn)supporting

confidence: 79%

“…Similarly, common nozzle geometry is circular and square slot along with the other geometries as seen in Experimental work has been performed by these (Saidi & Valipour, 2003;Promvonge & Eiamsaard, 2005;Dincer, Tasdemir, Baskaya, & Uysal, 2008;Takahama & Soga, 1966;Kirmaci, 2009;Dincer, Avci, Baskaya, & Berber, 2010;Eiamsa-ard, Wongcharee, & Promvonge, 2010;Chang, Li, Zhou, & Qiang, 2011;Mohmmadi & Farhadi, 2013) with regard to nozzle number (Avci, 2013;Bovand, Rashidi, & Esfahani, 2016) and nozzle geometry. Researchers have obtained varying results for effect of nozzle numbers on energy separation.…”

Section: Effect Of Nozzle Number and Nozzle Geometry (Nn)mentioning

confidence: 99%

“…Behera (Behera U. , Paul, Dinesh, & Jacob, 2008) has shown that increase in length, increases efficiency by 25% because of displacement of stagnation point. Dincer et al (Dincer, Tasdemir, Baskaya, & Uysal, 2008) related the performance of tube with stagnation point. The finding of the study was stagnation point depends upon the length and the other geometry of the tube.…”

Section: Effect Of L/d Ratiomentioning

confidence: 99%

“…Effect of L/D ratio was analysed numerically by (Behera U. , Paul, Dinesh, & Jacob, 2008;Dincer, Tasdemir, Baskaya, & Uysal, 2008;Ameri & Behnia, 2009;Bramo & Pourmahmoud, 2010;Pourmahmoud & Bramo, 2011;Kandil & Abdelghany, 2015;Pourmahmoud, Feyzi, Orang, & Paykani, 2015;Rafiee & Sadeghiazad, 2014;Linhart, Kalal, & Matas, 2005). Behera (Behera U. , Paul, Dinesh, & Jacob, 2008) has shown that increase in length, increases efficiency by 25% because of displacement of stagnation point.…”

Section: Effect Of L/d Ratiomentioning

confidence: 99%

“…These methods include, ANN (Artificial Neural Network) (Dincer, Tasdemir, Baskaya, & Uysal, 2008;Kocabas, Korkmaz, Sorgucu, & Donmez, 2010;Uluer, Kirmaci, & Atas, 2009;Korkamaz, Gumusel, & Markal, 2012), EVOP (Evolutionary Operations) (Soni & Thomson, 1975) Taguchi and ANOVA(Analysis of Variance) (Pinar, Uluer, & Kırmaci, 2009;Kumar, Padmanabhan, & Sarma, 2014), ODFS (Output Dependent Feature Scaling) ANFIS (Adaptive Network based Fuzzy Information System) (Polat & Kirmaci, 2011), RSM (Roughness Surface Methodology) (Bovand M. , Valipour, Dincer, & Eiamsa-ard, 2014) and MADM (Multiple Attribute Decision Making) . These are the few studies carried out to perform the theoretical optimization of the vortex tubes.…”

Section: Other Related Theoretical Workmentioning

confidence: 99%

See 4 more Smart Citations

Parametric Review of Ranque-Hilsch Vortex Tube

Devade¹,

Pise²

2017

AJHMT

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Vortex tube is the device that separates pressurized fluid into hot and cold fluid streams simultaneously. The scientific community has done extensive experimental and theoretical studies since invention of vortex tube to augment the performance of the tube by varying geometrical and operational parameters; the paper deals with parametric review of all such work. The review takes into account effect of almost 14 parameters on performance of vortex tube. It includes developments in tube geometry like L/D ratio, number of nozzles; hot end valve angle and cold orifice diameter etc. are discussed along with different fluids and operational parameters like CMF and stagnation point etc. The focus of most of the theoretical and experimental studies was to investigate mechanism of thermal separation and geometry optimization. The main objective of this review is to discuss efforts made in order to enhance the refrigeration effect so that, missing links could help for future research.

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Section: Effect Of Nozzle Number and Nozzle Geometry (Nn)supporting

confidence: 79%

Section: Effect Of Nozzle Number and Nozzle Geometry (Nn)mentioning

confidence: 99%

Section: Effect Of L/d Ratiomentioning

confidence: 99%

Section: Effect Of L/d Ratiomentioning

confidence: 99%

Section: Other Related Theoretical Workmentioning

confidence: 99%

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Parametric Review of Ranque-Hilsch Vortex Tube

Devade¹,

Pise²

2017

AJHMT

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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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