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عنوان فارسی مقاله:
سیستم استنتاج فازی عصبی تطبیقی ضریب اصطکاک و انتقال حرارت نانو سیال جریان آشفته در یک لوله گرم
عنوان انگلیسی مقاله:
Adaptive Neuro-Fuzzy Inference System of friction factor and heat transfer nanofluid turbulent flow in a heated tube
سال انتشار : 2016
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مقدمه انگلیسی مقاله:
1. Introduction
Large wide of world using tube in engineering applications and is significant in practical applications, such as heat exchangers, steam generators, chemical reactors, membrane separations, and piping systems [1]. Recent research has been focused on practical tube applications based on emerging both soft computing fields like Computational fluid dynamic (CFD), and computational intelligence such as ANN, GA, PSO, and fuzzy logic [2]. The heat transfer enhancement by used aluminum oxide nanofluid with different volume concentration and constant wall temperature studied experimentally by Sundar and Sharma [3]. It was concluded that the friction factor and heat transfer enhancement by 10% and 40% respectively. The single-phase approach may be used for heat transfer and pressure drop prediction of new nanofluids. Numerical study of convection flow of a Al2O3-water nanofluid inside the tube under turbulent flow with the wall uniform temperature was presented by Bianco et al. [4]. Their results showed the convective heat transfer coefficient for conventional liquid is lower than nanofluids and friction factor data was agreed with experimental data of Pak and Cho[5]. Many researchers have introduced different forms of neural-fuzzy networks and its applications in bioinformatics, petroleum engineering and pattern recognition [6–7]. Group of Artificial intelligence methods was used to estimate the convective heat transfer coefficient and pressure drop during annular flow numerically such as multilayer perceptron (MLP), generalized regression neural network (GRNN) and radial basis networks (RBFN), likewise, the Adaptive Neuro-Fuzzy Inference System (ANFIS) have been used to decide best approach of heat transfer [8].
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کلمات کلیدی:
Adaptive Neuro-Fuzzy Inference System of friction factor and heat ... www.sciencedirect.com/science/article/pii/S2214157X16300302 by AM Hussein - 2016 - Related articles In this paper, estimating of hydrodynamics and heat transfer nanofluid flow through heated tube has been conducted by using Adaptive Neuro-Fuzzy Inference ... [PDF]Adaptive Neuro-Fuzzy Inference System of friction factor and heat ... iranarze.ir/wp-content/uploads/2016/11/E2822.pdf Jun 8, 2016 - ANFIS model has three input data presented by Reynolds number, ... ANFIS model, in addition, the simulation results of friction factor were ... SID.ir | ADAPTIVE NEURO-FUZZY COMPUTING TECHNIQUE FOR ... en.journals.sid.ir/ViewPaper.aspx?ID=320859 by M GIVEHCHI - 2013 In this study in order to estimate the friction coefficient in pipes, using adaptive neuro-fuzzy inference systems (ANFIS), grid partition method was used. [PDF]Adaptive Neuro Fuzzy Inference System and Artificial Neural Networks ... cpm.nioc.ir/.../Adaptive_Neuro_Fuzzy_Interface_System_And_Artificial_Neural_Net... Adaptive Neuro Fuzzy Inference Systems are utilized in order to predict pipe sticking. ..... Another parameter that influences pipe sticking is the friction factor. Application of adaptive Neuro-Fuzzy inference system for the ... https://www.researchgate.net/.../272389015_Application_of_adaptive_Neuro-Fuzzy_inf... Oct 14, 2016 - Application of adaptive Neuro-Fuzzy inference system for the estimation of roughness coefficient of a meandering open-channel flow on ... River Basin Management VII - Page 76 - Google Books Result https://books.google.com/books?isbn=1845647122 C. A. Brebbia - 2012 - Technology & Engineering The roughness coefficient in these cases is represented as c, n and f respectively. ... In the present study, using ANFIS (Adaptive Neuro-Fuzzy Inference System) ... Topics in Modal Analysis I, Volume 5: Proceedings of the 30th IMAC, ... https://books.google.com/books?isbn=1461424259 R. Allemang, J. De Clerck, C. Niezrecki - 2012 - Technology & Engineering Correlation coefficient (R2) value was 0.9976 and 0.9825 for training and ... Therefore, it is concluded that an ANFIS trained with just natural frequencies ... fuzzy inference system (ANFIS) for prediction of friction coefficient in open channel flow.
