Prediction of thermal and fluid flow characteristics in helically coiled tubes using ANFIS and GA based correlations

Beigzadeh, Reza; Rahimi, Masoud

doi:10.1016/j.icheatmasstransfer.2012.10.011

Cited by 43 publications

(7 citation statements)

References 19 publications

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“…7A compares the experimental data reported by Mokhtari et al [38], Wu et al [39], Jayakumar et al [40], and Fig. 7B Pawar et al [41], Janssen & Hoogendoorn [42], and Beigzadeh & Rahimi [43] to the numerical results obtained in the present study in terms of the internal Nusselt Number. There is substantial agreement between the current research and the conclusions published in [38][39][40][41][42][43].…”

Section: Verificationsupporting

confidence: 73%

“…7B Pawar et al [41], Janssen & Hoogendoorn [42], and Beigzadeh & Rahimi [43] to the numerical results obtained in the present study in terms of the internal Nusselt Number. There is substantial agreement between the current research and the conclusions published in [38][39][40][41][42][43]. Here, we address the findings of both numerical and experimental investigations.…”

Section: Verificationsupporting

confidence: 70%

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Experimental and Numerical Investigations on the Heat Transfer of a Helical Coil Heat Exchanger Utilized α-Al2 O3 nanofluid

Abdullah,

Hussein

2023

DJES

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This study focused on investigating the impact of α-Al2O3 nanoparticles on heat transfer in shell and helical coiled tube heat exchangers. The objective was to analyze the influence of various geometrical characteristics, specifically the coil pitch, on the Nusselt numbers of both sides using a combination of numerical simulations and experimental methods. The working fluid for the hot side was water. The research encompassed an examination of the friction factor for three different coils, exploring the effects of pitch spacing on heat transfer, and assessing the influence of nanoparticles on heat transfer on the inner side of the coil. The findings of the current work indicated significant improvements in heat transfer parameters when employing water-α-Al2O3 nanoparticles as the cold fluid. Comparing this heat exchanger to one without the inclusion of α-Al2O3 nanoparticles revealed a remarkable efficiency enhancement of 7.68 percent. This increase strongly suggests a notable acceleration in the rate of heat transmission within the heat exchanger. Overall, this study provides valuable insights into the utilization of α-Al2O3 nanoparticles in enhancing heat transfer in shell and helical coiled tube heat exchangers. The results highlight the potential benefits of incorporating nanoparticles into such systems, leading to improved performance and more efficient heat exchange processes.

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Section: Verificationsupporting

confidence: 73%

Section: Verificationsupporting

confidence: 70%

Experimental and Numerical Investigations on the Heat Transfer of a Helical Coil Heat Exchanger Utilized α-Al2 O3 nanofluid

Abdullah,

Hussein

2023

DJES

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“…ey found that ANFIS is the best predictive model, whereas RSM is the least in the adsorption of EBT dye. Mehrabi et al [25] and Esen et al [26] used ANFIS, whereas Beigzadeh and Rahimi [27,28] used ANFIS and GA for modeling the influence of the essential parameters of the heat exchangers. Esen and Inalli [29], Hayati et al [30], and Esen et al [31] predicted Nu using novel geometry in a heat exchanger using ANFIS.…”

Section: Introductionmentioning

confidence: 99%

Analysis and Implementation of Thermal Heat Exchanger Tube Performance with Helically Pierced Twisted Tape Inserts Using ANFIS Model

Altarazi

Kumar

Gupta

et al. 2021

Mathematical Problems in Engineering

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The present work used ANFIS, an adaptive neuro-fuzzy inference system modeling to analyze the effect of the variable parameters of helically pierced twisted tape inserts on the Nusselt number, friction factor, and thermo-hydraulic heat exchanger tube performance. The experimental data utilized for ANFIS modeling considered a diameter ratio ranging from 0.57 to 0.80, a relative pitch ratio ranging from 0.046 to 0.107, a perforation index ranging from 5% to 20% as variable twisted tape parameters and flow parameters. The Reynolds number varies from 4000 to 30000. The analysis showed that the maximum thermo-hydraulic performance was obtained at a diameter ratio of 0.65, a relative pitch ratio of 0.085, and a perforation index equal to 10%. The result predicts that the ANFIS model and experimental results are in good agreement as they have only ±0.53% deviations.

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“…Each parameter is equally important to achieve a high‐quality biodiesel which meets the regulatory standards. Computational intelligence (CI) methods such as artificial neural network (ANN) and adaptive neuro‐fuzzy inference system (ANFIS) have been used to estimate physical and chemical data in many studies recently . Reviewing the literature revealed that no study has been published to discuss the application of ANN and ANFIS in predicting the biodiesel conversion under several conditions.…”

Section: Introductionmentioning

confidence: 99%

Biodiesel conversion modeling under several conditions using computational intelligence methods

Mohadesi

Rezaei

2017

Env Prog and Sustain Energy

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The computational intelligence (CI) methods such as artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) have many applications in chemistry, oil and gas, electronics, financial, telecommunications, and many others. In this article, ANN and ANFIS are used to model and predict the biodiesel conversion under several conditions. The inputs of the proposed CI models are oil type, catalyst type, calcination temperature, catalyst concentration, methanol-to-oil ratio, n-hexane-to-oil volume ratio, reaction time, and reaction temperature and the output is biodiesel conversion. Experimental data of available literature are used to train and test the CI models in MATLAB 7.0.4 software. Comparison between the proposed ANN and ANFIS models and the experimental data show that the proposed CI models are very efficient and fast tools, and there is a good agreement between them and the experimental data with a minimum error. Also, it can be found that the introduced ANN model is more accurate than the ANFIS model. The proposed ANN model has overall MRE% (mean relative error percentage) <1.5%, RMSE (root mean square error) <1.34, R (correlation coefficient) >0.9995, and MAE (mean absolute error) <0.9.

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Prediction of thermal and fluid flow characteristics in helically coiled tubes using ANFIS and GA based correlations

Cited by 43 publications

References 19 publications

Experimental and Numerical Investigations on the Heat Transfer of a Helical Coil Heat Exchanger Utilized α-Al2 O3 nanofluid

Experimental and Numerical Investigations on the Heat Transfer of a Helical Coil Heat Exchanger Utilized α-Al2 O3 nanofluid

Analysis and Implementation of Thermal Heat Exchanger Tube Performance with Helically Pierced Twisted Tape Inserts Using ANFIS Model

Biodiesel conversion modeling under several conditions using computational intelligence methods

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