A good contribution of computational fluid dynamics (CFD) and GA-ANN methods to find the best type of helical wire inserted tube in heat exchangers

Sharifi, Khashayar; Sabeti, Morteza; Rafiei, Mehdi; Mohammadi, Amir H.; Ghaffari, Ali; Asl, M. Haghighi; Halladj, Rouein

doi:10.1016/j.ijthermalsci.2020.106398

Cited by 35 publications

(9 citation statements)

References 27 publications

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“…GA works efficiently if the input variables, namely, i , j , and k , are introduced as discrete values with specific ranges. [ 26 ] Therefore, the search ranges of the three variables were defined as

0.500 < i < 3.000

0.200 < j < 1.000

, and

0.200 < k < 1.000

. As can be seen, a three‐decimals precision was employed for each variable.…”

Section: Modellingmentioning

confidence: 99%

A new analytical model to predict heavy oil production rate in the SAGD process

et al. 2022

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An analytical model for predicting the oil production rate in the steam-assisted gravity drainage (SAGD) process is presented in this article. The suggested correlation is found based on Butler's original work. It considers the most effective parameters of the process that emphasize the influence of gravity drainage and that are grouped together in the form of the Rayleigh's number. The present model introduces three coefficients (i, j, and k) into the equation, which are determined by minimizing an objective function based on the difference between the six experimental SAGD datasets and the calculated results. The tool chosen for the minimization is the genetic algorithm (GA). After the initial evaluation, the same approach is used for other reservoir characteristics to ensure the robustness of the new equation. Having considered various simulation outcomes with an average error of 8.9% makes this model a credible one for predicting the SAGD production rates. The novelty of the new predictive model lies within its unique approach, making it quite fast and applicable to a wide range of reservoirs with low associated estimation inaccuracies.

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0.500 < i < 3.000

0.200 < j < 1.000

, and

0.200 < k < 1.000

. As can be seen, a three‐decimals precision was employed for each variable.…”

Section: Modellingmentioning

confidence: 99%

A new analytical model to predict heavy oil production rate in the SAGD process

et al. 2022

Self Cite

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“…More recently, Akhavan-Behabadi [1] and Roy and Saha [24] proposed their own friction factor correlations. The correlation proposed by Akhavan-Behabadi was established for 2 mm thickness wire coils and was just a function of Reynolds numbers from [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. Roy and Saha presented a generalized correlation as a function of the helix wire-coil angle and the dimensionless thickness for Re but they only tested three wirecoils.…”

Section: Introductionmentioning

confidence: 99%

“…For twisted-tapes the use of response surface method (RSM), genetic algorithm, and Taguchi approach has also been applied [16]. To find the most adequate wire-coil insert to enhance a heat exchanger, Sharifi et al [31] employed 3D numerical simulations, artificial neural network (ANN) and genetic algorithms (GA) and Subasi and Erdem [33] introduced as an integrated optimization methodology, a hybrid approach that combines Multi-objective Optimization and Multiple-criteria Decision-making techniques.…”

Section: Introductionmentioning

confidence: 99%

Validation of a new methodological approach for the selection of wire-coil inserts in thermal equipment

Molina‐García

Herrero-Martín

Pérez-García

et al. 2023

Applied Thermal Engineering

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“…Their results showed that LSSVM approach has the best performance. Most recently, Sharifi et al [45] performed a three dimensional numerical simulation to investigate the effect of coiled wire inserts with various arrangements on friction coefficient and heat transfer rates for the helical wire inserted tube in heat exchangers. They stated that, with the assist ANN, the heat transfer rates and the friction coefficients could be accurately estimated within the ranges of input variables.…”

Section: Introductionmentioning

confidence: 99%