A Fuzzy Logic Algorithm for Acceleration of Convergence in Solving Turbulent Flow and Heat Transfer Problems

Dragojlovic, Zoran

doi:10.1080/10407790490487677

Cited by 16 publications

(12 citation statements)

References 9 publications

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“…The term fuzzy logic denotes a modeling approach where by functional dependencies between the input and output variables are described by means of a set of IF-THEN rules following the reasoning with the operators AND, OR, and NOT in general linguistic usage. The rule base has been composed with experimental results and theoretical information [4,23,24]. The rules in FESs are usually of a form similar to the following:…”

Section: The Basis Of the Fuzzy Expert Systemmentioning

confidence: 99%

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

Dinçer

Taşdemir

Baskaya

et al. 2008

Numerical Heat Transfer, Part B: Fundamentals

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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, n, and L/D were chosen as input and DT h , DT c , DT 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 DT h ¼ 0.9801, the R 2 value for DT c values is 0.9841, and the R 2 value for DT values is 0.9748.

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Section: The Basis Of the Fuzzy Expert Systemmentioning

confidence: 99%

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

Dinçer

Taşdemir

Baskaya

et al. 2008

Numerical Heat Transfer, Part B: Fundamentals

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“…In the past years, several soft computing tools have been used for modeling of aerodynamic systems, for example Neural Networks (NN) in [1][2][3][4][5][6][7], fuzzy logic in [8][9][10][11] and SVM in [12].…”

Section: Introductionmentioning

confidence: 99%

“…Dragojlovic et al utilize the fuzzy logic in improving the performance of a Computational Fluid Dynamics (CFD) solver. The fuzzy control scheme guides the increment in the relaxation factor by using triangular membership functions, [10][11]. Depending on the past solution entries, the CFD solver automatically adjusts itself to exploit the best relaxation factor.…”

Section: Introductionmentioning

confidence: 99%

Support vector networks for prediction of floor pressures in shallow cavity flows

Efe¹,

Debiasi²,

Yan³

et al. 2006

2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006

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During the last decade, Support Vector Machines (SVM) have proved to be very successful tools for classification and regression problems. The representational performance of this type of networks is studied on a cavity flow facility developed to investigate the characteristics of aerodynamic flows at various Mach numbers. Several test conditions have been experimented to collect a set of data, which is in the form of pressure readings from particular points in the test section. The goal is to develop a SVM based model that emulates the one step ahead behavior of the flow measurement at the cavity floor. The SVM based model is built for a very limited amount of training data and the model is tested for an extended set of test conditions. A relative error is defined to measure the reconstruction performance, and the peak value of the FFT magnitude of the error is measured. The results indicate that the SVM based model is capable of matching the experimental data satisfactorily over the conditions that are close to the training data collection conditions, and the performance degrades as the Mach number gets away from the conditions considered during training.

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“…The most often used defuzzification operators are center of area gravity, center of sums, first of maxima, last of maxima and height defuzzification. For more details relating to the defuzzification , the reader can be go back to Dragojlovic and Kaminski [25]. Table 2 illustrates the selected structure of the proposed fuzzy model.…”

Section: Defuzzificationmentioning

confidence: 99%

“…This method was used to predict the dynamic behavior of a heat exchanger. Dragojlovic and Kaminski [25] used the fuzzy logic to guide the under-relaxation of the discretized Navier-Stroke equations during the simulation of turbulent flow and heat transfer problems . The results illustrated that the application of fuzzy logic improved the computational effort of solving various types of CFD problems with different geometries, boundary conditions and material properties.…”

Section: Fuzzy Logic Techniquementioning

confidence: 99%

Numerical Analysis of Transport Phenomenon inside a Pulse Tube Refrigerator Coupled with Fuzzy Logic Controller

Rout¹,

Hussein²,

Sahoo³

et al. 2013

ujme

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This research article illustrates a numerical study of single stage coaxial as well as inline Inertance-Type Pulse Tube Refrigerator (ITPTR). In this present work a computational fluid dynamic (CFD) approach has been adopted for numerical simulation purpose. The detail analysis of cool down behaviour, heat transfer at the cold end and the pressure variation inside the whole system has been carried out by using the most powerful computational fluid dynamic software package FLUENT. A number of cases have been solved by changing the porosity of the regenerator from 0.5 to 0.9 while the rest of the considered parameter are remains unchanged. The operating frequency for all the studied cases is (34 Hz) while the other system dimensions and the boundary conditions are considered unchanged for all cases. The results show that the porosity value of (0.6) produces a better cooling effect on the cold end of the pulse tube refrigerator. The variation of the pressure inside the pulse tube refrigerator during the process are also analysed. In this work an attempt has been made to make a thermally non-equilibrium model applied numerically to the porous region by considering two different energy equations to the solid matrix and for the fluid inside the porous medium and the results are compared with the thermally equilibrium model. In fact , to get an optimum parameter experimentally is a very tedious for iterance pulse tube refrigerator job , so that the CFD approach gives a better solution which is the main purpose of the present work. In the next part the fuzzy logic approach is applied to optimize the different component parameters that affect the cooling performance of the ITPTR. The investigation is performed with different design parameters such as the length and diameter of pulse tube and regenerator given as an input to the fuzzy controller while the output from the controller is the cold end temperature. The predicted optimum results have been verified by performing the confirmation tests.

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A Fuzzy Logic Algorithm for Acceleration of Convergence in Solving Turbulent Flow and Heat Transfer Problems

Cited by 16 publications

References 9 publications

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

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

Support vector networks for prediction of floor pressures in shallow cavity flows

Numerical Analysis of Transport Phenomenon inside a Pulse Tube Refrigerator Coupled with Fuzzy Logic Controller

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