Modeling of heat transfer enhancement by wire coil inserts using artificial neural network analysis

Nasr, Mohammad Reza Jafari; Khalaj, Ali Habibi; Mozaffari, S.H.

doi:10.1016/j.applthermaleng.2009.07.014

Cited by 51 publications

(9 citation statements)

References 15 publications

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“…In that work, the authors performed an experimental investigation on heat transfer in helically coiled tubes and proposed the following correlation: Eq. (19) and the ANN model. As can be seen in the figure, the ANN has superior performance for predicting the Nusselt number.…”

Section: Resultsmentioning

confidence: 99%

“…Bélanger and Gosselin [18] studied application of neural networks to evaluate the design space of the heat transfer problems with a number of choices of materials. Jafari Nasr et al [19] employed ANNs to predict Nusselt number and friction factor inside tube with wire coil inserts. They compared their results with corresponding power-law regressions and a superior performance of the ANN compared with those methods was found.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of heat transfer and flow characteristics in helically coiled tubes using artificial neural networks

Beigzadeh

Rahimi

2012

International Communications in Heat and Mass Transfer

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of heat transfer and flow characteristics in helically coiled tubes using artificial neural networks

Beigzadeh

Rahimi

2012

International Communications in Heat and Mass Transfer

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“…Several works are summarized hereafter. The passive heat transfer enhancement methods that are still used by the majority of researchers are swirl devices in the form of twisted tape [3][4][5], wire coil [6,7], helical screw [8,9], and vortex generators in the form of a winglet [10][11][12]. These were investigated under various parameters to determine the important factors that affect the performance of the heat exchanger.…”

Section: Introductionmentioning

confidence: 99%

Numerical Study of Heat Transfer Enhancement of Internal Flow Using Double-Sided Delta-Winglet Tape Insert

et al. 2018

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A numerical study was performed to investigate the thermal performance characteristics of an enhanced tube heat exchanger fitted with punched delta-winglet vortex generators. Computational fluid dynamics modeling was applied using the k–ε renormalized group turbulence model. Benchmarking was performed using the results of the experimental study for a similar geometry. Attack angles of 30°, 50°, and 70° were used to investigate the heat transfer and pressure drop characteristics of the enhanced tube. Flow conditions were considered in the turbulent region in the Reynolds number range of 9100 to 17,400. A smooth tube was employed for evaluating the increment in the Nusselt number and the friction factor characteristics of the enhanced tube. The results show that the Nusselt number, friction factor, and thermal performance factor have a similar tendency. The presence of this insert offers a higher thermal performance factor as compared to that obtained with a plain tube. Vortex development in the flow structure aids in generating a vortex flow, which increases convective heat transfer. In addition, as the angle is varied, it is observed that the largest attack angle provides the highest thermal performance factor. The greatest increase in the Nusselt number and friction factor, respectively, was found to be approximately 3.7 and 10 times greater than those of a smooth tube. Through numerical simulations with the present simulation condition, it is revealed that the thermal performance factor approaches the value of 1.1. Moreover, the numerical and experimental values agree well although they tend to be different at high Reynolds number conditions. The numerical and experimental values both show similar trends in the Nusselt number, friction factor, and thermal performance factor.

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“…For this reason, thirty various trained network, was tested and validated and the best performance among trained network for each topology is listed in Table 6 . So, theoretically the optimal ANN configuration selected is based on the least prediction of validation errors 24 , 49 .…”

Section: Resultsmentioning

confidence: 99%

Analysis and estimation of cross-flow heat exchanger fouling in phosphoric acid concentration plant using response surface methodology (RSM) and artificial neural network (ANN)

Jradi

Marvillet

Jeday

2022

Sci Rep

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The production of phosphoric acid by dehydrated process leads to the precipitation of unwanted insoluble salts promoting thus the crystallization fouling build-up on heat transfer surfaces of the exchangers. During the acid concentration operation, the presence of fouling in heat exchangers results in reducing the performance of this equipment, in terms of heat transfer, while increasing energy losses and damaging the apparatus. To mitigate these adverse effects of fouling, it is necessary to forecast the thermal resistance of fouling to schedule and perform exchanger cleaning. In this context, artificial neural network and response surface methodology were used to estimate thermal resistance of fouling in a cross-flow heat exchanger by using the operating data of the concentration loop. The absolute average relative deviations, mean squared errors, root mean squared errors and correlation coefficients were used as indicators error between the experimental and estimated values for both methods. The best fitted model derived from response surface methodology method was second order polynomial while the best architecture topology, for the artificial neural network method, consists of three layers: input layer with six input variables, hidden layer with six hidden neurons and an output layer with single output variable. The interactive influences of operating parameters which have significant effects on the fouling resistance were illustrated in detail. The value of correlation coefficient for the output parameter from the response surface methodology is 0.9976, indicating that the response surface methodology as an assessment methodology in estimating fouling resistance is more feasible compared with the artificial neural network approach.

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Modeling of heat transfer enhancement by wire coil inserts using artificial neural network analysis

Cited by 51 publications

References 15 publications

Prediction of heat transfer and flow characteristics in helically coiled tubes using artificial neural networks

Prediction of heat transfer and flow characteristics in helically coiled tubes using artificial neural networks

Numerical Study of Heat Transfer Enhancement of Internal Flow Using Double-Sided Delta-Winglet Tape Insert

Analysis and estimation of cross-flow heat exchanger fouling in phosphoric acid concentration plant using response surface methodology (RSM) and artificial neural network (ANN)

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