Performance evaluation of a U-shaped heat exchanger containing hybrid Cu/CNTs nanofluids: experimental data and modeling using regression and artificial neural network

Maddah, Heydar; Ghazvini, Mahyar; Ahmadi, Mohammad Hossein; Bui, Dieu Tien; Filho, Enio Pedone Bandarr

doi:10.1007/s10973-020-09882-7

Cited by 8 publications

(5 citation statements)

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“…The results showed that the non-Newtonian hybrid nanofluid always had a higher heat transfer rate, overall heat transfer coefficient and effectiveness than those of the Newtonian hybrid nanofluid. Maddah et al [21] investigated viscosity and thermal conductivity of hybrid Cu/CNT water-based nanofluids at various concentrations of nanofluid and temperatures. The results demonstrated that although increased concentration resulted in enhancement of the thermal conduction coefficient and viscosity, the increase in temperature followed the expected results of increasing thermal conductivity and decreasing viscosity.…”

Section: Introductionmentioning

confidence: 99%

Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

et al. 2021

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This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of 150–350, temperature in the range of 70–90 K, and nanoparticle concentration in the range of 2–4% were selected as network input variables, while the corresponding Nusselt number and energy consumption were considered as the network target. The network has 3 inputs, 1 hidden layer with 22 neurons and an output layer. The SOM neural network was also used to determine the number of winner neurons. The advanced optimal artificial neural network model shows a reasonable agreement in predicting experimental data with mean square errors of 0.0023357 and 0.00011465 and correlation coefficients of 0.9994 and 0.9993 for the Nusselt number and energy consumption data set. The obtained values of eMAX for the Nusselt number and energy consumption are 0.1114, and 0.02, respectively. Desirable results obtained for the two factors of correlation coefficient and mean square error indicate the successful prediction by artificial neural network with a topology of 3-22-2.

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Section: Introductionmentioning

confidence: 99%

Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

et al. 2021

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“…6. In recent years, the radial basis transfer function was used to train ANN models, some examples are shown in Maddah et al 25 and Zolghadri et al 26 This article aims to introduce a new perspective, called the ANN, which allows us to calculate the heat transfer area considering the assumption of variable overall heat transfer coefficient.…”

Section: Introductionmentioning

confidence: 99%

“…The research reports that the overall heat transfer coefficient is a function of nanoparticle concentration, rib pitch, and rib height. Then Maddah, 25 used a different strategy, the authors uses an ANN model to predict the exergetic efficiency of a U‐shaped heat exchanger, the Levenberg–Marquardt method is used to optimize the adjustment parameters in the hidden layer considering seven neurons; for the evaluation, the use of convective coefficients was not reported, special emphasis was placed on the thermal‐physical properties of the hybrid Cu/CNTs water‐based nanofluids. The Reynolds number, temperature, and nanoparticle concentration were selected to train an ANN that considers 22 neurons in the hidden layer to predict the energy consumption and Nusselt number in shell and tube heat exchanger 26 …”

Section: Introductionmentioning

confidence: 99%

“…However, the impact of this prediction on the calculation of transfer area under conditions of variable U has not yet been considered. In recent years, the radial basis transfer function was used to train ANN models, some examples are shown in Maddah et al 25 and Zolghadri et al 26 …”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the radial basis transfer function was used to train ANN models, some examples are shown in Maddah et al 25 and Zolghadri et al 26 …”

Section: Introductionmentioning

confidence: 99%

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Heat exchanger design considering variable overall heat transfer coefficient: An artificial neural network approach

et al. 2021

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This study presents an artificial neural network approach in combination with numerical methods to calculate the heat transfer area assuming a nonlinear variation of the global heat transfer coefficient as a consequence of the thermophysical properties of the fluids, the geometry of the surfaces, and other factors. The development of the article is presented in two applications. The first application takes up the database described by Allan P. Colburn, four possibilities are proposed using functions from the field of artificial neural networks to create several approaches. The second application is presented to verify the goodness of the proposed methodology, the artificial neural network model is applied in an experimental data set of double‐pipe vertical heat exchangers, the comparison between the calculated and experimental heat transfer area shows a relative percentage error smaller than 2.8%. The results in the applications are evidence of the competitiveness of the artificial neural network for the prediction of the heat transfer area considering a variable overall heat transfer coefficient.

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Investigation of the thermal conductivity of soil subjected to freeze–thaw cycles using the artificial neural network model

FIRAT

Atila

2021

J Therm Anal Calorim

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Performance evaluation of a U-shaped heat exchanger containing hybrid Cu/CNTs nanofluids: experimental data and modeling using regression and artificial neural network

Cited by 8 publications

References 62 publications

Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

Heat exchanger design considering variable overall heat transfer coefficient: An artificial neural network approach

Investigation of the thermal conductivity of soil subjected to freeze–thaw cycles using the artificial neural network model

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