Fuzzy Modeling of Performance of Counterflow Ranque-Hilsch Vortex Tubes with Different Geometric Constructions

In this article, we present the development of a fuzzy expert system (FES) for fuzzy modeling of the performance of counterflow Ranque-Hilsch vortex tubes for different geometric constructions. Experimental values were obtained from a detailed experimental investigation. With these experimental values, FES models of the Ranque-Hilsch vortex tube behavior were designed using the MATLAB 6.5 fuzzy logic toolbox in Windows XP running on an Intel 3.0-Ghz PC. For this process P, N, ξ, and L/D were chosen as input and ΔT h , ΔT c , ΔT as output parameters. FES results agree well with experimental data. It was found that the coefficient of multiple determination (R 2 value) between the actual and fuzzy predicted data is ΔT h = 0.9801, the R 2 value for ΔT c values is 0.9841, and the R 2 value for ΔT values is 0.9748.

[1]  Soteris A. Kalogirou,et al.  Artificial intelligence for the modeling and control of combustion processes: a review , 2003 .

[2]  K. Dincer,et al.  Experimental investigation of the effects of length to diameter ratio and nozzle number on the performance of counter flow Ranque–Hilsch vortex tubes , 2007 .

[3]  I Aluclu,et al.  A fuzzy logic-based model for noise control at industrial workplaces. , 2008, Applied ergonomics.

[4]  Arzu Şencan,et al.  Artificial intelligent methods for thermodynamic evaluation of ammonia–water refrigeration systems , 2006 .

[5]  Ugur Kesgin,et al.  Simulation of thermodynamic systems using soft computing techniques , 2005 .

[6]  S. Wehofer,et al.  From Measurement Uncertainty to Measurement Communications, Credibility, and Cost Control in Propulsion Ground Test Facilities , 1985 .

[7]  K. Chiang,et al.  Application of grey-fuzzy logic on the optimal process design of an injection-molded part with a thin shell feature , 2006 .

[8]  S. Iniyan,et al.  A review of energy models , 2006 .

[9]  Marcel Staroswiecki,et al.  Multi-modelling of an industrial steam generator ☆ , 2000 .

[10]  Stephen J. Kline,et al.  The Purposes of Uncertainty Analysis , 1985 .

[11]  Arthur L. Dexter,et al.  A fuzzy decision-making approach to temperature control in air-conditioning systems , 2005 .

[12]  Llanos Mora López,et al.  Fuzzy inference systems applied to the daily ultraviolet radiation evaluation (295–385 nm) from daily global radiation , 2003 .

[13]  Katsutoshi Kuribayashi,et al.  A fuzzy model to control the temperature in the cooling of metal molds with a spray robot , 2006, Artificial Life and Robotics.

[14]  R. Moffat.  Using Uncertainty Analysis in the Planning of an Experiment , 1985 .

[15]  R. Hall,et al.  Fuzzy-logic modeling of land suitability for hybrid poplar across the Prairie Provinces of Canada , 2008, Environmental monitoring and assessment.

[16]  R. P. Benedict,et al.  ASME Measurement Uncertainty , 1985 .

[17]  Flávio Vasconcelos da Silva,et al.  Experimental evaluation of fuzzy controllers for the temperature control of the secondary refrigerant in a liquid chiller , 2006 .

[18]  Pongjet Promvonge,et al.  Numerical investigation of the thermal separation in a Ranque–Hilsch vortex tube , 2007 .

[19]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[20]  Borut Zupančič,et al.  Fuzzy control for the illumination and temperature comfort in a test chamber , 2005 .

[21]  Antonio Casimiro Caputo,et al.  Fuzzy control of heat recovery systems from solid bed cooling , 2000 .

[22]  Bauke de Vries,et al.  Assessing working conditions using Fuzzy Logic , 2008 .

[23]  Deborah A. Kaminski,et al.  A FUZZY LOGIC ALGORITHM FOR ACCELERATION OF CONVERGENCE IN SOLVING TURBULENT FLOW AND HEAT TRANSFER PROBLEMS , 2004 .

[24]  Paul B. Lochert,et al.  A fuzzy logic approach for dealing with qualitative quality characteristics of a process , 2008, Expert Syst. Appl..

[25]  Norbert Stoll,et al.  An energy-gain bounding approach to robust fuzzy identification , 2006, Autom..

[26]  Kyoung Kwan Ahn,et al.  Modeling and control of shape memory alloy actuators using Preisach model, genetic algorithm and fuzzy logic , 2008 .

[27]  R. J. Moffat,et al.  Contributions to the Theory of Single-Sample Uncertainty Analysis , 1982 .

[28]  R. Koray Kıyıldı,et al.  The capacity analysis of the check-in unit of Antalya Airport using the fuzzy logic method , 2008 .