Application of artificial neural networks for prediction of natural convection from a heated horizontal cylinder

Atayılmaz, Ş. Özgür; Demir, Hakan; Ağra, Özden

doi:10.1016/j.icheatmasstransfer.2009.08.009

Cited by 22 publications

(8 citation statements)

References 20 publications

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“…The results obtained from the neural network model and the CFD program showed a good fit with a maximum error of 1.8%. Atayılmaz [15] applied a three-layers network in analyzing natural convection heat transfer in a horizontal cylinder. They compared the results from the trained network with the experimental Nusselt number over the cylinder and the results were in a good agreement.…”

Section: Introductionmentioning

confidence: 99%

Comparison study of CFD and artificial neural networks in predicting temperature fields induced by natural convention in a square enclosure

Zhou

Liu

et al. 2019

THERM SCI

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Natural convection in an enclosure is a classical problem in heat transfer field. In this study, natural convection induced by the heat source in the enclosure is studied with two analysis methods, i. e. CFD and artificial neural networks (ANN). The heat transfer in the enclosure is an unsteady process. During this process, the temperature fields in the enclosure are changing with time. The vertical temperature field of y = 0 at one moment is picked up for investigation. Firstly, FLUENT software which is a simulation program of CFD is adopted to simulate the temperature fields under different computation conditions. Then part of the simulation condition's temperature data is picked for training an ANN model and the rest of data is used for validating the ANN model. It has been found from the comparison between the CFD simulation and ANN prediction that the two results have a good agreement with each other. In the comparison, the max relative errors are around 12%, mean relative errors are around 0.3%, mean square errors are around 0.6%, values of absolute fraction of variance are all not less than 0.99. The results demonstrated that the ANN prediction have enough accuracy.

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Section: Introductionmentioning

confidence: 99%

Comparison study of CFD and artificial neural networks in predicting temperature fields induced by natural convention in a square enclosure

Zhou

Liu

et al. 2019

THERM SCI

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“…The most important problems in engineering applications such as heat exchangers, boiler design and air cooling systems for air conditioning are study of natural convection heat transfer. Natural convection heat transfer from a horizontal cylinder has been studied numerically and experimentally for over 50 years but it is reported by the researchers (Atayılmaz et al 2010) that the obtained results show high levels of deviation among each other due to various reasons. Yamamoto et al (2004) studied Natural convection around a horizontal circular pipe coupled with heat conduction in the solid structure.…”

Section: Introductionmentioning

confidence: 99%

Numerical and Neural Network Analysis of Natural Convection from a Cold Horizontal Cylinder above an Adiabatic Wall

Tahavvor¹,

Nazari²

2019

JAFM

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Free convection around cold circular cylinder above an adiabatic plate at steady-state condition has been investigated both numerically and by artificial neural networks. There is a growing demand for a better understanding of free convection from a horizontal cylinder in the areas like air cooling, refrigeration and air conditioning system, etc. Governing equations are solved in some specified cases by finite volume method to generate the database for training the neural network in the range of Rayleigh numbers of 10 5 to 10 8 and a range of cylinder distance from adiabatic plate (L/D) of 1/4, 1/2, 1/1, 3/2 and 4/2, thereafter a Multi-Layer Perceptron network is used to capture the behavior of flow and temperature fields and then generalized this behavior to predict the flow and temperature fields for other Rayleigh numbers. Different training algorithms are used and it is found that the back-propagation method with Levenberg-Marquardt learning rule is the best algorithm regarding the faster training procedure. It is observed that ANN can be used more efficiently to determine cold plume and thermal field in less computational time and with an excellent agreement. From obtained results, average Nusselt number of the cylinder investigated to study the effect of adiabatic wall on the isothermal cylinder. It also observed that in spaces farther than L/D = 3/2, average Nusselt number is almost constant, so the affect is renouncement and it works like a cylinder in an infinite environment.

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“…Unlike circular [5][6][7]. Artificial neural networks (ANN) are used in numerous engineering applications because these tools provide excellent and highly reasonable solutions [8]. Ermis et al [9] used a feed-forward back-propagation ANN to conduct numerical and experimental analysis of the heat transfer resulting from the phase change process in finned tubes.…”

Section: Introductionmentioning

confidence: 99%

Hybrid CFD-ANN Scheme for Air Flow and Heat Transfer Across In-Line Flat Tubes Array

Rahman

Jassim

Tahseen

et al. 2018

Tikrit j. eng. sci.

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Flat tubes are vital components of various technical applications including modern heat exchangers, thermal power plants, and automotive radiators. This paper presents the hybridization of computational fluid dynamic (CFD) and artificial neural network (ANN) approach to predict the thermal-hydraulic characteristics of in-line flat tubes heat exchangers. A 2D steady state and an incompressible laminar flow in a tube configuration are considered for numerical analysis. Finite volume technique and body-fitted coordinate system are used to solve the Navier-Stokes and energy equations. The Reynolds number based on outer hydraulic diameter varies between 10 and 320. Heat transfer coefficient and friction are analyzed for various tube configurations including transverse and longitudinal pitches. The numerical results from CFD analysis are used in the training and testing of the ANN for predicting thermal characteristics and friction factors. The predicted results revealed a satisfactory performance, with the mean relative error ranging from 0.39% to 5.57%, the root-mean-square error ranging from 0.00367 to 0.219, and the correlation coefficient (R 2) ranging from 99.505% to 99.947%. Thus, this study verifies the effectiveness of using ANN in predicting the performance of thermal-hydraulic systems in engineering applications such as heat transfer modeling and fluid flow in tube bank heat exchangers.

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Application of artificial neural networks for prediction of natural convection from a heated horizontal cylinder

Cited by 22 publications

References 20 publications

Comparison study of CFD and artificial neural networks in predicting temperature fields induced by natural convention in a square enclosure

Comparison study of CFD and artificial neural networks in predicting temperature fields induced by natural convention in a square enclosure

Numerical and Neural Network Analysis of Natural Convection from a Cold Horizontal Cylinder above an Adiabatic Wall

Hybrid CFD-ANN Scheme for Air Flow and Heat Transfer Across In-Line Flat Tubes Array

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