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. 2024 Feb 10;10(4):e26279.
doi: 10.1016/j.heliyon.2024.e26279. eCollection 2024 Feb 29.

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

Mohammadreza Baghoolizadeh  1 , Dheyaa J Jasim  2 , S Mohammad Sajadi  3 , Reza Rostamzadeh- Renani  4 , Mohammad Rostamzadeh- Renani  4 , Maboud Hekmatifar  5
Affiliations

Using of artificial neural networks and different evolutionary algorithms to predict the viscosity and thermal conductivity of silica-alumina-MWCN/water nanofluid

Mohammadreza Baghoolizadeh et al. Heliyon. .

Retraction in

Abstract

This study predicts the parameters such as viscosity and thermal conductivity in silica-alumina-MWCN/water nanofluid using the artificial intelligence method and using design variables such as solid volume fraction and temperature. In this study, 6 optimization algorithms were used to predict and numerically model the μnf and TC of silica-alumina-MWCNT/water-NF. In this study, six measurement criteria were used to evaluate the estimates obtained from the coupling process of GMDH ANN with each of these 6 optimization algorithms. The results reveal that the influence of the φ is notably higher on both μnf and TC with values of 0.83 for μnf and 0.92 for TC, while Temp has a relatively weaker impact with -0.5 for μnf and 0.38 for TC. Among various algorithms, the coupling of the evolutionary algorithm NSGA II with ANN and GMDH performs best in predicting μnf and TC for the NF, with a maximum margin of deviation of -0.108 and an R2 evaluation criterion of 0.99996 for μnf and 1 for TC, indicating exceptional model accuracy. In the subsequent phase, a meta-heuristic Genetic Algorithm minimizes μnf and TC values. Four points (A, B, C, and D) along the Pareto front are selected, with point A representing the optimal state characterized by low values of φ and Temp (0.0002 and 50.8772, respectively) and corresponding target function values of 0.9988 for μnf and 0.6344 for TC. In contrast, point D represents the highest values of φ and Temp (0.49986 and 59.9775, respectively) and yields target function values of 2.382 for μnf and 0.8517 for TC. This analysis aids in identifying the optimal operating conditions for maximizing NF performance.

Keywords: Correlation coefficient; Meta-heuristic; NSGA II; Nanofluid; Pareto front.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Correlation value between input variables and objective functions.
Fig. 2
Fig. 2
Correlation value between input variables and objective functions.
Fig. 3
Fig. 3
Correlation value between input variables and objective functions.
Fig. 4
Fig. 4
(a) Spiraling up (b) Double round and (c) Random movement.
Fig. 5
Fig. 5
MOD for μnf of silica-alumina-MWCN/water nanofluid.
Fig. 6
Fig. 6
MOD for TC of silica-alumina-MWCN/water nanofluid.
Fig. 7
Fig. 7
Schematic of ANN neurons.
Fig. 8
Fig. 8
Comparison of μnf predicted by ANN with experimental data.
Fig. 9
Fig. 9
Comparison of predicted TC by ANN with experimental data.
Fig. 10
Fig. 10
Obtained Pareto front by six evolutionary algorithms.
Fig. 11
Fig. 11
Pareto front created by NSGA II algorithm.
Fig. 12
Fig. 12
Optimal points of the Pareto front.
Fig. 13
Fig. 13
Surface plot of inputs (φ, T) and μnf.
Fig. 14
Fig. 14
Surface plot of inputs (φ, T) and TC.
See this image and copyright information in PMC

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