Performance predictions of laminar heat transfer and pressure drop in an in-line flat tube bundle using an adaptive neuro-fuzzy inference system (ANFIS) model

Tahseen, Tahseen Ahmad; Ishak, M.; Rahman, M. M.

doi:10.1016/j.icheatmasstransfer.2013.11.007

Cited by 35 publications

(22 citation statements)

References 27 publications

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“…The numerical model was validated with some of the previously published standard problems. The comparison between the code results and Bahaidarah et al [3] is shown in a previous publication by Tahseen et al [26].…”

Section: Methodsmentioning

confidence: 59%

“…The error between the experimental results and ANNs approached an approximation of the mean absolute relative error of less than 4%. In a recent study, Tahseen et al [26] numerically analysed the thermal and fluid characteristics of airflow in an in-line configuration of flat tube bundles. The neurofuzzy inference system (ANFAS) model was used to predict the coefficient of heat transfer and pressure drop.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Heat Transfer and Pressure Drop Prediction in an in-Line Flat Tube Bundle by Radial Basis Function Network

Tahseen

Rahman²

2014

Int. J. Automot. Mech. Eng.

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This paper aims to predict the heat transfer and pressure drop for an in-line flat tubes configuration in a cross-flow using an artificial neural network. The numerical study of a two-dimensional steady state and incompressible laminar flow for an in-line flat tube configuration in a cross-flow is also considered in this study. The Reynolds number varies from 10 to 320. Heat transfer coefficient and pressure drop results are presented for tube configurations at three transverse pitches of 2.5, 3.0, and 4.5 with two longitudinal pitches of 3.0 and 6.0. The predicted results for the average Nusselt number and dimensionless pressure show good agreement with previous work. The accuracy between the actual values and the neural network approach model results was obtained with a mean absolute relative error less than 4.1%, 4.8%, and 3.8% for the average Nusselt number, dimensionless pressure drop and average friction factor, respectively.

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Section: Methodsmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Heat Transfer and Pressure Drop Prediction in an in-Line Flat Tube Bundle by Radial Basis Function Network

Tahseen

Rahman²

2014

Int. J. Automot. Mech. Eng.

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“…In developing the model, the following assumptions were made: (i) the physical properties of air flow are constant; [15] the air flow is incompressible and laminar flow; and (iii) the steady states flow and heat transfer. The governing equations for two-dimensional continuity, Naviere-Stokes for momentum and energy equation can be written as follows [24]:…”

Section: S S D  mentioning

confidence: 99%

Effect of tube spacing, fin density and Reynolds number on overall heat transfer rate for in-line configuration

Tahseen¹,

Rahman²,

Ishak

2015

Int. J. Automot. Mech. Eng.

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This paper presents variation of overall dimensionless heat transfer rate against dimensionless tubes spacing, fins density, Reynolds number for in-line configuration. Influence of Reynolds number on the predicted and experimental results of dimensionless pumping power minimization for with respect to the dimensionless fins density for several tube-to-tube spacing also investigated. The experiments were conducted at a flat tube in the flow direction, and Reynolds number based on the hydraulic diameter (ReDh) was considered. The dimensionless overall heat transfer rate is always increases with an increase of the Reynolds number. The dimensionless fins spacing effect on the dimensionless overall heat transfer rate increase is noticeable at high Reynolds numbers. The dimensionless pumping power increases with increasing response Reynolds numbers for dimensionless tubes spacing and dimensionless fins density. The dimensionless pumping power decreased with increase of dimensionless fin density.

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“…The density of small heat exchangers' surface is very high, and it can be as much as 1800 m 2 m 3 . Due to its high heat transfer rate, plate fin heat exchangers are highly important now, and are widely used [3].…”

Section: Introductionmentioning

confidence: 99%

Numerical analysis of the surface and geometry of plate fin heat exchangers for increasing heat transfer rate

Shahdad

Fazelpour

2018

Int J Energy Environ Eng

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This paper investigates the flow field and turbulent flow heat transfer around an array of plain and perforated fin using Fluent software within the range of 20,000-50,000 Reynolds. Regarding the turbulent flow, the k-ε RNG turbulence model was implemented, and SIMPLE algorithm was used for solving the equations of three-dimensional, steady, and incompressible flow. In the simulation process, air was considered as the working fluid with consistent physical properties. The results revealed that perforated fins increase the heat transfer coefficient as well as Nusselt number. The highest heat transfer coefficient and Nusselt number was achieved for perforated fins with two square holes. Moreover, it was concluded that increase of Reynolds number notably increases the heat transfer coefficient and Nusselt number. The total drag force imposed to plain fins was higher than the force imposed to perforated fins. As a result, by changing plain fins into perforated fins, the pressure decreases due to passage of the flow through the pins, and accordingly, the total drag force imposed to the fins decreases. Finally, it was revealed that attaching some pins on the plain fins along the passing flow will decrease the pressure, while notably increase the heat transfer. Furthermore, it can reduce the fins' weight and price.

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Performance predictions of laminar heat transfer and pressure drop in an in-line flat tube bundle using an adaptive neuro-fuzzy inference system (ANFIS) model

Cited by 35 publications

References 27 publications

Heat Transfer and Pressure Drop Prediction in an in-Line Flat Tube Bundle by Radial Basis Function Network

Heat Transfer and Pressure Drop Prediction in an in-Line Flat Tube Bundle by Radial Basis Function Network

Effect of tube spacing, fin density and Reynolds number on overall heat transfer rate for in-line configuration

Numerical analysis of the surface and geometry of plate fin heat exchangers for increasing heat transfer rate

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