Numerical investigation of nanofluid flow using CFD and fuzzy-based particle swarm optimization.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
25 10 2021
25 10 2021
Historique:
received:
26
07
2021
accepted:
29
09
2021
entrez:
26
10
2021
pubmed:
27
10
2021
medline:
27
10
2021
Statut:
epublish
Résumé
This paper is focused on the application and performance of artificial intelligence in the numerical modeling of nanofluid flows. Suspension of metallic nanoparticles in the fluids has shown potential in heat transfer enhancement of the based fluids. There are many numerical studies for the investigation of thermal and hydrodynamic characteristics of nanofluids. However, the optimization of the computational fluid dynamics (CFD) modeling by an artificial intelligence (AI) algorithm is not considered in any study. The CFD is a powerful technique from an accuracy point of view. However, it could be time and cost-consuming, especially in large-scale and complicated problems. It is expected that the machine learning technique of the AI algorithms could improve such CFD drawbacks by patterning the CFD data. Once the AI finds the CFD pattern intelligently, there is no need for CFD calculations. The particle swarm optimization-based fuzzy inference system (PSOFIS) is considered in this study to predict the velocity profile of Al
Identifiants
pubmed: 34697333
doi: 10.1038/s41598-021-00279-6
pii: 10.1038/s41598-021-00279-6
pmc: PMC8545973
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
20973Commentaires et corrections
Type : ExpressionOfConcernIn
Informations de copyright
© 2021. The Author(s).
Références
Choi, S. U. & Eastman, J. A. Enhancing Thermal Conductivity of Fluids with Nanoparticles (Argonne National Lab, 1995).
Song, Y.-Q. et al. Solar energy aspects of gyrotactic mixed bioconvection flow of nanofluid past a vertical thin moving needle influenced by variable Prandtl number. Chaos Solitons Fractals. 151, 111244 (2021).
doi: 10.1016/j.chaos.2021.111244
Gowda, R. P. et al. Exploring magnetic dipole contribution on ferromagnetic nanofluid flow over a stretching sheet: An application of Stefan blowing. J. Mol. Liquids. 335, 116215 (2021).
doi: 10.1016/j.molliq.2021.116215
Song, Y.-Q. et al. Physical impact of thermo-diffusion and diffusion-thermo on Marangoni convective flow of hybrid nanofluid (MnZiFe
doi: 10.1142/S0217984921410062
Madhukesh, J. K. et al. Bio-Marangoni convection flow of Casson nanoliquid through a porous medium in the presence of chemically reactive activation energy. Appl. Math. Mech. 42(8), 1191–1204 (2021).
doi: 10.1007/s10483-021-2753-7
Azizifar, S., Ameri, M. & Behroyan, I. Experimental investigation of the subcooled flow boiling heat transfer of water and nanofluids in a horizontal metal foam tube. Heat Mass Transf. 57, 1499–1511. https://doi.org/10.1007/s00231-021-03042-9 (2021).
doi: 10.1007/s00231-021-03042-9
Darzi, A. R., Farhadi, M. & Sedighi, K. Heat transfer and flow characteristics of Al
doi: 10.1016/j.icheatmasstransfer.2013.06.003
Kumar, R. R., Sridhar, K. & Narasimha, M. Heat transfer performance in heat pipe using Al
Qu, J., Wu, H.-Y. & Cheng, P. Thermal performance of an oscillating heat pipe with Al
doi: 10.1016/j.icheatmasstransfer.2009.10.001
Qu, J. & Wu, H. Thermal performance comparison of oscillating heat pipes with SiO
doi: 10.1016/j.ijthermalsci.2011.04.004
Heris, S. Z., Esfahany, M. N. & Etemad, S. G. Experimental investigation of convective heat transfer of Al
doi: 10.1016/j.ijheatfluidflow.2006.05.001
Xuan, Y. & Li, Q. Investigation on convective heat transfer and flow features of nanofluids. J. Heat transfer 125(1), 151–155 (2003).
doi: 10.1115/1.1532008
Jahanshahi, M. et al. Numerical simulation of free convection based on experimental measured conductivity in a square cavity using Water/SiO
doi: 10.1016/j.icheatmasstransfer.2010.03.010
Namburu, P. K. et al. Numerical study of turbulent flow and heat transfer characteristics of nanofluids considering variable properties. Int. J. Therm. Sci. 48(2), 290 (2009).
doi: 10.1016/j.ijthermalsci.2008.01.001
Behroyan, I. et al. CFD models comparative study on nanofluids subcooled flow boiling in a vertical pipe. Numer. Heat Transfer Part A Appl. 73(1), 55–74 (2018).
doi: 10.1080/10407782.2017.1420299
Ganesan, P. et al. Turbulent forced convection of Cu–water nanofluid in a heated tube: Improvement of the two-phase model. Numer. Heat Transfer Part A Appl. 69(4), 401–420 (2016).
doi: 10.1080/10407782.2015.1081019
Behroyan, I. et al. A comprehensive comparison of various CFD models for convective heat transfer of Al
doi: 10.1016/j.icheatmasstransfer.2015.11.001
Behroyan, I. et al. Turbulent forced convection of Cu–water nanofluid: CFD model comparison. Int. Commun. Heat Mass Transfer 67, 163–172 (2015).
doi: 10.1016/j.icheatmasstransfer.2015.07.014
Madhukesh, J. et al. Numerical simulation of AA7072–AA7075/water-based hybrid nanofluid flow over a curved stretching sheet with Newtonian heating: A non-Fourier heat flux model approach. J. Mol. Liquids. 335, 116103 (2021).
doi: 10.1016/j.molliq.2021.116103
Prasannakumara, B. C. Numerical simulation of heat transport in Maxwell nanofluid flow over stretching sheet considering magnetic dipole effect. Partial Differ. Eqn. Appl. Math. 4, 100064 (2021).
doi: 10.1016/j.padiff.2021.100064
Kumar, R. N. et al. Inspection of convective heat transfer and KKL correlation for simulation of nanofluid flow over a curved stretching sheet. Int. Commun. Heat Mass Transfer. 126, 105445 (2021).
doi: 10.1016/j.icheatmasstransfer.2021.105445
Nguyen, Q. et al. Fluid velocity prediction inside bubble column reactor using ANFIS algorithm based on CFD input data. Arab. J. Sci. Eng. 45, 7487–7498 (2020).
doi: 10.1007/s13369-020-04611-6
Zhou, J. et al. Performance evaluation of hybrid FFA-ANFIS and GA-ANFIS models to predict particle size distribution of a muck-pile after blasting. Eng. Comput. 37(1), 265–274 (2021).
doi: 10.1007/s00366-019-00822-0
Xu, P. et al. Flow visualization and analysis of thermal distribution for the nanofluid by the integration of fuzzy c-means clustering ANFIS structure and CFD methods. J. Visual. 23(1), 97–110 (2020).
doi: 10.1007/s12650-019-00614-0
Selimefendigil, F. & Öztop, H. F. Numerical analysis and ANFIS modeling for mixed convection of CNT–water nanofluid filled branching channel with an annulus and a rotating inner surface at the junction. Int. J. Heat Mass Transf. 127, 583–599 (2018).
doi: 10.1016/j.ijheatmasstransfer.2018.07.038
Pourtousi, M. et al. Prediction of multiphase flow pattern inside a 3D bubble column reactor using a combination of CFD and ANFIS. RSC Adv. 5(104), 85652–85672 (2015).
doi: 10.1039/C5RA11583C
Pourtousi, M. et al. A combination of computational fluid dynamics (CFD) and adaptive neuro-fuzzy system (ANFIS) for prediction of the bubble column hydrodynamics. Powder Technol. 274, 466–481 (2015).
doi: 10.1016/j.powtec.2015.01.038
Babanezhad, M. et al. Performance and application analysis of ANFIS artificial intelligence for pressure prediction of nanofluid convective flow in a heated pipe. Sci. Rep. 11(1), 1–18 (2021).
doi: 10.1038/s41598-020-79139-8
Shih, T. M. Numerical Heat Transfer (CRC Press, 1984).
Fluent, A. 14.5. User’s and Theory Guide (ANSYS, 2014).
Aly, W. I. Numerical study on turbulent heat transfer and pressure drop of nanofluid in coiled tube-in-tube heat exchangers. Energy Convers. Manage. 79, 304–316 (2014).
doi: 10.1016/j.enconman.2013.12.031
Takagi, T. & Sugeno, M. Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 1, 116–132 (1985).
doi: 10.1109/TSMC.1985.6313399
Babanezhad, M. et al. Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution algorithm. Sci. Rep. 11(1), 1–14 (2021).
doi: 10.1038/s41598-020-79139-8
Ciano, T. et al. Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm. Sci. Rep. 11(1), 1–12 (2021).
doi: 10.1038/s41598-020-79139-8