Filtration of Radiating and Reacting SWCNT–MWCNT/Water Hybrid Flow with the Significance of Darcy–Forchheimer Porous Medium

Suganya, S.; Muthtamilselvan, M.; Al-Amri, Fahad; Abdalla, Bahaaeldin; Doh, Deog-Hee

doi:10.1007/s13369-020-04967-9

Cited by 12 publications

(5 citation statements)

References 33 publications

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“…Ghadikolaei and Gholinia [27] worked on heat transport and magnetonatural convection flow of hybrid nanofluid over a vertical porous stretchable surface. Suganya et al [28] evaluated the hybrid nanofluid flow the effect of Darcy-Forchheimer porous medium.…”

Section: Introductionmentioning

confidence: 99%

Homogeneous–Heterogeneous Chemical Reactions of Radiation Hybrid Nanofluid Flow on a Cylinder with Joule Heating: Nanoparticles Shape Impact

2021

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The current analysis aims to exhibit the nanoparticles of Al2O3 + Cu-water hybrid nanofluid flow for Darcy–Forchheimer with heterogeneous–homogeneous chemical reactions and magnetic field aspects past a stretching or shrinking cylinder with Joule heating. This paper performed not only with the hybrid nanofluid but also the shape of Al2O3 and Cu nanoparticles. The model of single-phase hybrid nanofluid due to thermophysical features is utilized for the mathematical formulation. In the present exploration equal diffusions factors for reactants and auto catalyst are instituted. The system of governing equations has been simplified by invoking the similarity transformation. The numerical computations are invoked due to the function bvp4c of Matlab, with high non-linearity. Numerical outcomes illustrated that; sphere shape nanoparticles presented dramatic performance on heat transfer of hybrid nanofluid movement; an opposite behavior is noticed with lamina shape. The local Nusselt number strengthens as the transverse curvature factor becomes larger. In addition, the homogeneous–heterogeneous reactions factors lead to weaken concentration fluctuation.

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Section: Introductionmentioning

confidence: 99%

Homogeneous–Heterogeneous Chemical Reactions of Radiation Hybrid Nanofluid Flow on a Cylinder with Joule Heating: Nanoparticles Shape Impact

2021

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“…A comparative analysis is showcased between SWCNTs and MWCNTs suspended in a base liquid 27,28 . Some researchers 29,30,31 discussed the Single-wall carbon nanotubes (SWCNT) and multiwall carbon nanotubes (MWCNT), the two primary forms of water-based carbon nanotubes. A three-dimensional CNT nanofluid flow model with an unsteady flow and a uniform magnetic field between two rotating discs and also a Darcy-Forchheimer scheme with convective boundary conditions, suction/injection, and thermal radiation are considered 32 .…”

Section: Introductionmentioning

confidence: 99%

Enhanced Heat Transfer in Vertical Channels via Non-Darcy Flow with SWCNTs and MWCNTs: Arrhenius Activation and Viscosity Variations

Khatua,

Koneti,

Haripriya

et al. 2024

Preprint

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The effective management of heat transfer processes is critical in various engineering applications, including thermal management systems and advanced cooling technology. Nanofluids, with their distinct thermal properties, present significant opportunities for improving heat transmission. The Arrhenius activation model is used to investigate the thermal behaviour of nanofluids. This study investigates the joint effect of Arrhenius activation on the non-darcy hydromagnetic convective heat and mass transfer of a nanofluid in a vertical channel using both SWCNT and MWCNT. The Runge-Kutta fourth-order shooting technique was used to analyze the coupled nonlinear equations governing flow heat transfer. Graphical research has been conducted to investigate the impact of different activation energies, viscosity levels, heat sources, and chemical reactions on flow characteristics. A numerical analysis is performed to determine the skin friction, the rate of heat transfer, and the mass transfer for various parametric variables.

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“…Du et al 5 investigated the natural convection of ventilation cavity filling with the hybrid nanofluid (NF) of Cu–Al 2 O 3 /H 2 O and under the magnetic field. There are also a large number of studies that utilized a porous system in various conditions for improving heat transfer 6 – 9 . For example, an investigation indicated the improved heat transfer by reducing porosity 6 .…”

Section: Introductionmentioning

confidence: 99%

“…There are also a large number of studies that utilized a porous system in various conditions for improving heat transfer 6 – 9 . For example, an investigation indicated the improved heat transfer by reducing porosity 6 . The other study reported the enhancement of convective heat transfer by reducing porosity and increasing pore density 7 .…”

Section: Introductionmentioning

confidence: 99%

Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results

Babanezhad

Behroyan

Marjani

et al. 2021

Sci Rep

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Utilizing artificial intelligence algorithm of adaptive network-based fuzzy inference system (ANFIS) in combination with the computational lfuid dynamics (CFD) has recently revealed great potential as an auxiliary method for simulating challenging fluid mechnics problems. This research area is at the beginning, and needs sophisticated algorithms to be developed. No studies are available to consider the efficiency of the other trainers like differential evolution (DE) integrating with the FIS for capturing the pattern of the simulation results generated by CFD technique. Besides, the adjustment of the tuning parameters of the artificial intelligence (AI) algorithm for finding the highest level of intelligence is unavailable. The performance of AI algorithms in the meshing process has not been considered yet. Therfore, herein the Al2O3/water nanofluid flow in a porous pipe is simulated by a sophisticated hybrid approach combining mechnsitic model (CFD) and AI. The finite volume method (FVM) is employed as the CFD approach. Also, the differential evolution-based fuzzy inference system (DEFIS) is used for learning the CFD results. The DEFIS learns the nanofluid velocity in the y-direction, as output, and the nodes coordinates (i.e., x, y, and z), as inputs. The intelligence of the DEFIS is assessed by adjusting the methd’s variables including input number, population number, and crossover. It was found that the DEFIS intelligence is related to the input number of 3, the crossover of 0.8, and the population number of 120. In addition, the nodes increment from 4833 to 774,468 was done by the DEFIS. The DEFIS predicted the velocity for the new dense mesh without using the CFD data. Finally, all CFD results were covered with the new predictions of the DEFIS.

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Filtration of Radiating and Reacting SWCNT–MWCNT/Water Hybrid Flow with the Significance of Darcy–Forchheimer Porous Medium

Cited by 12 publications

References 33 publications

Homogeneous–Heterogeneous Chemical Reactions of Radiation Hybrid Nanofluid Flow on a Cylinder with Joule Heating: Nanoparticles Shape Impact

Homogeneous–Heterogeneous Chemical Reactions of Radiation Hybrid Nanofluid Flow on a Cylinder with Joule Heating: Nanoparticles Shape Impact

Enhanced Heat Transfer in Vertical Channels via Non-Darcy Flow with SWCNTs and MWCNTs: Arrhenius Activation and Viscosity Variations

Velocity prediction of nanofluid in a heated porous pipe: DEFIS learning of CFD results

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