Fuzzy-based estimation of mixed convection heat transfer in a square cavity in the presence of an adiabatic inclined fin

Selimefendigil, Fatih; Öztop, Hakan F.

doi:10.1016/j.icheatmasstransfer.2012.10.006

Cited by 54 publications

(19 citation statements)

References 33 publications

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“…Numerous studies have been conducted in this area. Among the notable works are those by Khanafer and Vafai, 96 Oztop and Dagtekin, 97 Sharif, 98 Basak et al, 99 Chung and Vafai, 100 Basak et al, 101 Grosan and Pop, 102 Karimipour et al, 103 Rahman et al, 104 Ramakrishna et al, 105,106 Selimefendigil and Oztop, 107 and Alipanah et al 108 Another technique is to introduce hot or cold fluid from one side through the isothermal walls and have the fluid exit from the other side. A number of researchers have imposed a constant heat flux on the wall as the fluid passes through the channel and subsequently analyzed the heat transfer effect.…”

Section: Inside Cavities and Enclosuresmentioning

confidence: 99%

A survey on experimental and numerical studies of convection heat transfer of nanofluids inside closed conduits

Safaei

Shadloo

Goodarzi

et al. 2016

Advances in Mechanical Engineering

106

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Application of nanofluids in heat transfer enhancement is prospective. They are solid/liquid suspensions of higher thermal conductivity and viscosity compared to common working fluids. A number of studies have been performed on the effect of nanofluids in heat transfer to determine the enhancement of properties in addition to rearrangement of flow passage configurations. The principal objective of this study is to elaborate this research based on natural, forced, and the mixed heat transfer characteristics of nanofluids exclusively via convection for single-and two-phase mixture models. In this study, the convection heat transfer to nanofluids has been reviewed in various closed conduits both numerically and experimentally.

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Section: Inside Cavities and Enclosuresmentioning

confidence: 99%

A survey on experimental and numerical studies of convection heat transfer of nanofluids inside closed conduits

Safaei

Shadloo

Goodarzi

et al. 2016

Advances in Mechanical Engineering

106

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“…x À c ðx À cÞ 2 þ ðy À dÞ 2 (5) where g,c and d represent the magnetic field strength and location of the magnetic dipole source. The term in the momentum equation is the force per unit volume when the spatially non-uniform magnetic field is employed to the magnetic fluid.…”

Section: Physical Model and Numerical Studymentioning

confidence: 99%

Mixed convection of ferrofluids in a lid driven cavity with two rotating cylinders

Selimefendigil

Öztop

2015

Engineering Science and Technology, an International Journal

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a b s t r a c tMixed convection of ferrofluid filled lid driven cavity in the presence of two rotating cylinders were numerically investigated by using the finite element method. The cavity is heated from below, cooled from driven wall and rotating cylinder surfaces and side vertical walls of the cavity are assumed to be adiabatic. A magnetic dipole source is placed below the bottom wall of the cavity. The study is performed for various values of Reynolds numbers (100 Re 1000), angular rotational speed of the cylinders (À400 U 400), magnetic dipole strengths (0 g 500), angular velocity ratios of the cylinders ð0:25 U i =U j 4Þ and diameter ratios of the cylinders ð0:5 D i =D j 2Þ. It is observed that flow patterns and thermal transport within the cavity are affected by variation in Reynolds number and magnetic dipole strength. The results of this investigation revealed that cylinder angular velocities, ratio of the angular velocities and diameter ratios have profound effect on heat transfer enhancement within the cavity. Averaged heat transfer enhancements of 181.5 % is achieved for clockwise rotation of the cylinder at U ¼ À400 compared to motionless cylinder case. Increasing the angular velocity ratio from U 2 =U 1 ¼ 0:25 to U 2 =U 1 ¼ 4 brings about 91.7 % of heat transfer enhancement.

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“…ANFIS [23][24][25] is a fuzzy inference system [26,27] implemented in the framework of neural networks [28,29]. This combination merges the advantages of fuzzy systems and neural networks.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Neuro-Fuzzy Modeling of the Free Convection Heat Transfer From a Wavy Surface

Karami

Yousefi

Harsini

et al. 2014

Heat Transfer Engineering

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In this paper an adaptive neuro-fuzzy inference system (ANFIS) is applied to model and predict the experimental results of free convection heat transfer from a vertical array of attached cylinders, which can be considered as a wavy surface, in the presence of a vertical wall. The effects of the wall-wavy surface spacing and Rayleigh number variation on average heat transfer from the wavy surface are considered via this prediction. The training data for optimizing the ANFIS structure are based on available experimental data. A hybrid learning algorithm consisting of gradient descends method and least-squares method is used for ANFIS training. The proposed ANFIS model is developed using MATLAB functions. For the best ANFIS structure obtained in this study, the mean relative errors of the train and test data were found to be 0.02% and 1.2%, respectively. The predicted results showed that ANFIS can predict the experimental results precisely.

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Fuzzy-based estimation of mixed convection heat transfer in a square cavity in the presence of an adiabatic inclined fin

Cited by 54 publications

References 33 publications

A survey on experimental and numerical studies of convection heat transfer of nanofluids inside closed conduits

A survey on experimental and numerical studies of convection heat transfer of nanofluids inside closed conduits

Mixed convection of ferrofluids in a lid driven cavity with two rotating cylinders

Neuro-Fuzzy Modeling of the Free Convection Heat Transfer From a Wavy Surface

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