Significance of EMHD graphene oxide (GO) water ethylene glycol nanofluid flow in a Darcy–Forchheimer medium by machine learning algorithm

The low heat efficiency of base fluids is a key problem among investigators. To address this issue, investigators utilize tiny-sized (1–100 nm) metal solid material inside the base fluids to boost thermal performance of base solvents. A numerical investigation on the thermal application functioning...

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Bibliographic Details
Published in:European physical journal plus Vol. 138; no. 3; p. 213
Main Authors: Shafiq, Anum, Çolak, Andaç Batur, Sindhu, Tabassum Naz
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 07.03.2023
Springer Nature B.V
Subjects:
Algorithms
Applied and Technical Physics
Artificial intelligence
Artificial neural networks
Atomic
Biot number
Boundary conditions
Chemical reactions
Coefficient of friction
Complex Systems
Condensed Matter Physics
Datasets
Entropy
Ethylene glycol
Fluid flow
Friction
Graphene
Heat
Investigations
Machine learning
Mathematical and Computational Physics
Molecular
Nanofluids
Nanomaterials
Nanoparticles
Oil recovery
Optical and Plasma Physics
Physics
Physics and Astronomy
Radiation
Regular Article
Runge-Kutta method
Skin friction
Theoretical
Thermal radiation
ISSN:2190-5444, 2190-5444
Online Access:Get full text
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Summary:The low heat efficiency of base fluids is a key problem among investigators. To address this issue, investigators utilize tiny-sized (1–100 nm) metal solid material inside the base fluids to boost thermal performance of base solvents. A numerical investigation on the thermal application functioning of graphene oxide water/ethylene glycol-based nanofluids under the influence of the electromagnetohydrodynamic and Darcy–Forchheimer medium has been compiled in the present study via a machine learning algorithm. In the study of nanofluid flow, thermal radiation and a convective boundary condition are also used. The Runge–Kutta fourth-order shooting method was utilized to calculate the system of equations. The skin friction coefficient and Nusselt parameter were simulated with various variables, and two distinct artificial neural networks have been developed based on the findings. It is beneficial to estimate the fluid temperature with a large Biot number. R value above 0.99 was obtained for the developed artificial neural networks. The deviation rate was also calculated at very low values. The outcomes show that the proposed artificial neural network models can accurately predict the skin friction coefficient and Nusselt number.
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ISSN:2190-5444
2190-5444
DOI:10.1140/epjp/s13360-023-03798-5