Volume 20, Issue 8 (August 2020)                   Modares Mechanical Engineering 2020, 20(8): 2139-2157 | Back to browse issues page

XML Persian Abstract Print

1- Department of Mechanical Engineering Urmia University of Technology, Urmia, IRAN , m.sadeghiazad@uut.ac.ir
2- Mechanical Engineering Department, Mechanical Engineering Faculty, Urmia University of Technology, Urmia, Iran
Abstract:   (1844 Views)
The vortex tube is one of the widely used cooling systems in the industry. Investigating the effect of all input variables on the outlet cold temperature difference in laboratory state is time-consuming and costly. To this purpose, in the current study, attempts were made to model and predict the effect of all input variables on the outlet cold temperature difference of air and inlet air using adaptive neuro-fuzzy inference system (ANFIS) method. The ANFIS method was designed with three structures of fuzzy inference systems called subtractive clustering (SC) algorithm, fuzzy c-means (FCM), and grid partition (GP) with four types of input membership functions including trimf, gaussmf, gbellmf, and pimf. For model training and testing, 326 laboratory data were used. The developed models were compared using statistical parameters of correlation coefficient, mean absolute relative deviation, standard deviation, and root mean square error (RMSD) together with general desirability function. The results showed that GP algorithm with input pimf membership function with the greatest value of correlation coefficient (0.9975) and lowest value of RMSD for test data (0.4199) and general desirability function value of 0.71 is the best method to predict outlet cold temperature difference. Using the above-mentioned method, the most optimum state of vortex tube performance for industrial applications was found to be the use of 3 or 6 nozzels, at the pressure range of 0.55 to 0.6MPa and the nozzle angle of 20 to30 degrees, and for laboratory applications was obtained to be the use of 6 nozzles, at the pressure range of 0.55 to 0.6MPa, and the nozzle angle of 25 to 35 degrees.
Full-Text [PDF 1635 kb]   (1236 Downloads)    
Article Type: Original Research | Subject: Heat & Mass Transfer
Received: 2019/02/28 | Accepted: 2020/06/8 | Published: 2020/08/15

1. Li N, Jiang G, Fu L, Tang L, Chen G. Experimental study of the impacts of cold mass fraction on internal parameters of a vortex tube. International Journal of Refrigeration. 2019;104:151-160. [Link] [DOI:10.1016/j.ijrefrig.2019.05.002]
2. Xuea Y, Binnsa JR, Arjomandi M, Yanb H. Experimental investigation of the flow characteristics within a vortex tube with different configurations. International Journal of Heat and Fluid Flow. 2019;75:195-208. [Link] [DOI:10.1016/j.ijheatfluidflow.2019.01.005]
3. Bazgir A, Heydari A, Nabhani N. Investigation of the thermal separation in a counter-flow Ranque-Hilsch vortex tube with regard to different fin geometries located inside the coldtube length. International Communications in Heat and Mass Transfer. 2019;108:104273. [Link] [DOI:10.1016/j.icheatmasstransfer.2019.104273]
4. Hamdan MO, Al-Omari SAB, Oweimer AS. Experimental study of vortex tube energy separation under different tube design. Experimental Thermal and Fluid Science. 2018;91:306-311. [Link] [DOI:10.1016/j.expthermflusci.2017.10.034]
5. Eiamsa-ard S. Experimental investigation of energy separation in a counter-flow Ranque-Hilsch vortex tube with multiple inlet snail entries. International Communications in Heat and Mass Transfer. 2010;37(6):637-643. [Link] [DOI:10.1016/j.icheatmasstransfer.2010.02.007]
6. Tiwari NK, Sihag P, Kumar S, Ranjan S. Prediction of trapping efficiency of vortex tube ejector. ISH Journal of Hydraulic Engineering. 2018;26(1):59-67. [Link] [DOI:10.1080/09715010.2018.1441752]
7. Attalla M, Ahmed H, Ahmed MS, El-Wafa AA. An experimental study of nozzle number on Ranque Hilsch counter-flow vortex tube. Experimental Thermal and Fluid Science. 2017;82:381-389. [Link] [DOI:10.1016/j.expthermflusci.2016.11.034]
8. Pouraria H, Kia SM, Park WG, Mehdizadeh B. Modeling the cooling performance of vortex tube using a genetic algoritm based artificialneural network. Thermal Science. 2016;20(1):53-65. [Link] [DOI:10.2298/TSCI140126112P]
9. Li N, Zeng ZY, Wang Z, Han XH, Chen GM. Experimental study of the energy separation in a vortex tube. International Journal of Refrigeration. 2015;55:93-101. [Link] [DOI:10.1016/j.ijrefrig.2015.03.011]
10. Han X, Li N, Wu K, Wang Z, Tang L, Chen G, et al. The influence of working g:as char:acteristics on energy separation of vortex tube. Applied Thermal Engineering. 2013;61(2):171-177. [Link] [DOI:10.1016/j.applthermaleng.2013.07.027]
11. Valipour MS, Niazi N. Experimental modeling of a curved Ranque-Hilsch vortex tube refrigerator. International Journal of Refrigeration. 2011;34(4):1109-1116. [Link] [DOI:10.1016/j.ijrefrig.2011.02.013]
12. Devade K, Pise A. Effect of cold orifice diameter and geometry of hot end valves on performance of converging type ranque hilsch vortex tube. Energy Procedia. 2014;54:642-653. [Link] [DOI:10.1016/j.egypro.2014.07.306]
13. Rafiee SE, Sadeghiazad MM. Three-dimensional and experimental investigation on the effect of cone length of throttle valve on thermal performance of a vortex tube using K-ε turbulence model. Applied Thermal Engineering. 2014;66(1-2):65-74. [Link] [DOI:10.1016/j.applthermaleng.2014.01.073]
14. Sadi M, Gord Mahmood F. Introducing annular vortex tube and experimental comparison of its performance with vortex tube. Modares Mechanical Engineering. 2015;14(11):166-167. [Persian] [Link]
15. Ahadiyan J. Application of anfis adaptive system to estimate the potential consolidation of clay soils. Journal of Modeling in Engineering. 2016;14(45):17-31. [Persian] [Link]
16. Koohsari H, Najafi A, Alielahi H, Adampira M. Evaluation of influential factors on the dynamic compaction operation of granular soils based on fuzzy method. Journal of Modeling in Engineering. 2016;13(43):143-158. [Persian] [Link]
17. Rafiee SE, Sadeghiazad MM. Experimental and 3D CFD investigation on heat transfer and energy separation inside a counter flow vortex tube using different shapes of hot control valves. Applied Thermal Engineering. 2017;110:648-664. [Link] [DOI:10.1016/j.applthermaleng.2016.08.166]
18. Salehi A, Montazeri M, Mohammadi E. Induction motor control using ANFIS controller and IWO optimization algorithm for using in HIL testing of turbojet FCU. Modares Mechanical Engineering. 2014;13(11):77-87. [Persian] [Link]
19. Khajavi M, Nasernia E. Applications of intelligent methods in online diagnosis of tool wear in milling operation using vibration analysis. Modares Mechanical Engineering. 2015;15(2):261-269. [Persian] [Link]
20. Jahanbakhshi A, Ahmadi Nadoshan A. Simulation of passive heating solar wall and prediction the temperature by Artificial Neural Networks and Adaptive Neuro-Fuzzy model (ANFIS). Modares Mechanical Engineering. 2018;18(2):159-169. [Persian] [Link]
21. Sedghee Rostami H, Rezaie B. Controlling state of quantum system using fuzzy controller. Modares Mechanical Engineering. 2016;16(9):124-134. [Persian] [Link]
22. Saidi MH, Valipour MS. Experimental modeling of vortex tube refregration. Applied Thermal Engineering. 2003;23(15):1971-1980. [Link] [DOI:10.1016/S1359-4311(03)00146-7]
23. Promvonge P, Eiamsa-ard S. Investigation on the vortex thermal separation in a vortex tube refregration. Science Asia Journal. 2005;31(3):215-223. [Link] [DOI:10.2306/scienceasia1513-1874.2005.31.215]
24. Dincer K, Tasdemir S, Baskaya S, Uysal BZ. 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. Applied Thermal Engineering. 2008;28(17-18):2380-2390. [Link] [DOI:10.1016/j.applthermaleng.2008.01.016]
25. Tatar A, Barati-Harooni A, Najafi-Marghmaleki A, Mohebbi A, Ghiasi MM, Mohammadi AH, et al. Comparison of two soft computing approaches for predicting CO2 solubility in aqueous solution of piperazine. International Journal of Greenhouse Gas Control. 2016;53:85-97. [Link] [DOI:10.1016/j.ijggc.2016.07.037]
26. Najafi-Marghmaleki A, Khosravi-Nikou MR, Barati-Harooni A. A new model for prediction of binary mixture of ionic liquids+ water density using artificial neural network. Journal of Molecular Liquids. 2016;220:232-237. [Link] [DOI:10.1016/j.molliq.2016.04.085]
27. Nasery S, Barati-Harooni A, Tatar A, Najafi-Marghmaleki A, Mohammadi AH. Accurate prediction of solubility of hydrogen in heavy oil fractions. Journal of Molecular Liquids. 2016;222:933-943. [Link] [DOI:10.1016/j.molliq.2016.07.083]
28. Tatar A, Nasery S, Bahadori A, Bahadori M, Najafi-Marghmaleki A, Barati-Harooni A. Implementing radial basis function neural network for prediction of surfactant retention inpetroleum production and processing industries. Petroleum Science and Technology. 2016;34(11-12):992-999. [Link] [DOI:10.1080/10916466.2016.1177548]
29. Tatar A, Nasery S, Bahadori M, Bahadori A, Bahadori M, Barati-Harooni A, et al. Prediction of water removal rate in a natural gas dehydration system using radial basis function neural network. Petroleum Science and Technology. 2016;34(10):951-960. [Link] [DOI:10.1080/10916466.2016.1166131]
30. Hilsch R. The use of expansion of gases in a centrifugal field as a cooling process. Review of Scientific Instruments. 1947;18(2):108-113. [Link] [DOI:10.1063/1.1740893]
31. Stephan K, Lin S, Durst M, Huang F, Seeeer D. An investigation of energy separation in a vortex tube. International Journal of Heat and Mass Transfer. 1983;26(3):341-384. [Link] [DOI:10.1016/0017-9310(83)90038-8]
32. Zadeh LA. Fuzzy sets. Information and Control. 1965;8(3):338-353. [Link] [DOI:10.1016/S0019-9958(65)90241-X]
33. Safari H, Nekoeian S, Shirdel MR, Ahmadi H, Bahadori A, Zendehboudi S. Assessing the dynamic viscosity of Na-K-Ca-Cl-H2O aqueous solutions at high-pressure and high-temperature conditions. Industrial & Engineering Chemistry Research. 2014;53(28):11488-11500. [Link] [DOI:10.1021/ie501702z]
34. Takagi T, Sugeno M. Fuzzy identification of systems and its application to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics. 1985;15(1):116-132. [Link] [DOI:10.1109/TSMC.1985.6313399]
35. Sharifi A, Aliyari Shoorehdeli M, Teshnehlab M. Semi-polynomial Takagi-Sugeno-Kang type fuzzy system for system identification and pattern classification. The Journal of Organic Chemistry. 2010;4(3):15-28. [Persian] [Link]
36. Mostafaei M. ANFIS models for prediction of biodiesel fuels cetane number using desirability function. Fuel. 2018;216:665-672. [Link] [DOI:10.1016/j.fuel.2017.12.025]

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.