Prediction of Dynamic Viscosity of a New Non-Newtonian Hybrid Nanofluid Using Experimental and Artificial Neural Network (Ann) Methods

Toghraie, Davood; Sina, Nima; Mozafarifard, Milad; Alizadeh, As’ad; Soltani, Farid; Fazilati, Mohamad

doi:10.1615/heattransres.2020034645

Cited by 20 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The application of conventional heat transfer fluids such as water, methanol, ethylene glycol, and engine oil, due to their low thermal conductivities is limited [1]. To upgrade the energy efficiency of systems in engineering applications the conventional heat transfer fluids were replaced with nanofluids (NFs) [2]. nanofluids are suspension of small solid particles of metallic or non-metallic, called nanoparticle in the aforementioned base fluids [3].…”

Section: Introductionmentioning

confidence: 99%

Numerical Solution of Heat Transfer Flow of Casson Hybrid Nanofluid over Vertical Stretching Sheet with Magnetic Field Effect

Alkasasbeh¹

2022

CFDL

View full text Add to dashboard Cite

In this study, heat transfer flow of Casson hybrid nanofluid over vertical stretching sheet with magnetic field effect is studied. Copper oxide and graphite oxide in methanol are considered as the hybrid nanoparticles. This analysis of study points out several aspects of flow physically which is related to transportation of heat. The conservation of partial differential equations (PDEs) are taken place into non-linear coupled ordinary differential equations (ODEs) by using transformation of dimensionless variables, and solution of these ODEs is performed using Chebyshev differential quadrature method (CDQM). Effects of many involved parameters like M, , , a/c, denotes the magnetic parameter, Casson parameter, the nanoparticles volume fraction and ratio parameter are shown through tables for skin friction and Nusselt number. Moreover, it is presented as sketching on temperature and velocity profiles briefly. Results revealed that the Casson hybrid nanofluid has highest value when <1 for Nusselt number, skin friction, and temperature profile. Moreover, it is lowest value when >1 for velocity profile and Nusselt number. It also is observed that, when the magnetic field and Cason parameter increase, this leads to an increase in the temperature profile and a decrease in the Nusselt number and the velocity profile. A comparison study is also made which compares the present result with published data is executed and findings meet with them.

show abstract

Section: Introductionmentioning

confidence: 99%

Numerical Solution of Heat Transfer Flow of Casson Hybrid Nanofluid over Vertical Stretching Sheet with Magnetic Field Effect

Alkasasbeh¹

2022

CFDL

View full text Add to dashboard Cite

show abstract

“…Complex challenges, particularly nonanalytical, nonlinear, and nonstationary, may be solved with ANNs because of their ability to ease high-level programming in their crude mimicry of a biological network, which can be used to handle a wide range of problems. The experimental study is carried out using artificial neural networks strategies, to simulate energy and exergy of the evacuated solar-tubes is performed in the study by Sadeghi et al (2020), thermal conductivity model nanofluid-based study is carried out in the study by Pare and Ghosh (2021), non-Newtonian hybrid nanofluid is taken to predict the dynamic viscosity (Toghraie et al, 2020), and statistical tools applied for thermos-physical properties of nanofluid (Esfe et al, 2022). The third-grade nanofluidic model along with convective conditions has been taken through a stretchable surface which is further solved by neural technique in the study by Shoaib et al (2021).…”

Section: Introductionmentioning

confidence: 99%

MHD williamson nanofluid flow in the rheology of thermal radiation, joule heating, and chemical reaction using the Levenberg–Marquardt neural network algorithm

et al. 2022

View full text Add to dashboard Cite

Various studies have been conducted on the topic of predicting the thermal conductivity of nanofluids. Here, the thermal conductivity of nanofluids is determined using artificial neural networks since this approach is rapid and accurate, as well as cost-effective. To forecast the thermal conductivity of magnetohydrodynamic Williamson nanofluids flow through a vertical sheet, a feed-forward neural network with various numbers of neurons has been evaluated, and the best network based on the performance is selected. The fluid model incorporates the effects of Joule heating, heat generation absorption, thermal radiation, and a chemical reaction (MHD-WNF-HGA). A combination of heat radiation and reactive species improves the energy and solute profiles. The magnetic Reynolds number is assumed to be so small; therefore, the generated magnetic field has no effect. A postulate of similarity variables is used to convert the physical model in the form of nonlinear partial differential equations to an ordinary differential equation system. A supervised Levenberg–Marquardt backpropagation algorithm possesses a multilayer perceptron that is used for training the network, which is one of the top algorithms in machine learning. The bvp4c numerical technique is adopted to build the datasets for the construction of continuous neural network mapping. Flow, energy, and concentration profiles of the fluidic flow are constructed by adjusting several physical quantities such as the Williamson parameter, thermal radiation parameter, magnetic parameter, Eckert number, Darcy number, Brownian motion, and thermophoresis parameter. Analytical techniques such as error histogram graphs and regression-based statistical graphs are used to examine the accuracy of a suggested method. It has been found that the Levenberg–Marquardt backpropagation neural network mappings’ derivation, convergence, authentication, and consistency have been proven. Furthermore, thermal radiation assists the energy distribution to increase smoothly. Fluid velocity drops with the Williamson parameter, whereas thermophoresis impact enhances the strength of the nanofluid density.

show abstract

“…In this article, an analysis was performed on the convection of a Newtonian fluid flow between two asymmetric parallel surfaces in a porous medium. Mondal [17] researched irreversibility minimization in a viscoelastic liquid flow [18–49]. The viscoelastic parameters, the Peclet number, and the temperature gradient were studied in this paper.…”

Section: Introductionmentioning

confidence: 99%

Hybrid surveying of radiation and magnetic impacts on Maxwell fluid with MWCNT nanotube influence of two wire loops

et al. 2022

View full text Add to dashboard Cite

In this paper, the effects of Maxwell nanoliquid transmission with MWCNT nanotube are investigated over two circular wires with opposite currents and a stretching sheet. The innovation of this paper is the examination of MWCNT nanotube temperature and nanofluid velocity and also magnetic potential and magnetic effect in the three different places of two circular wires on the sheet. The finite element method is utilized for calculating differential equations. By increasing the distance from two wires on the stretching sheet, the amount of MWCNT nanotube temperature increased, and a large vortex forms at the bottom of the sheet. Overall, the MWCNT nanotube velocity around the two circular wires at the beginning of the sheet is 46.09% higher than the middle of the sheet and 20% higher than the end of the sheet, also the MWCNT nanotube magnetic potential around the two circular wires at the beginning of the sheet is 13.40% higher than the middle of the sheet and 23% higher than the end of the sheet. The second part of the essay relates to the using response surface methodology (RSM) method in the Design‐Expert software. Based on the results obtained from the MWCNT nanotube velocity graphs and the heat transfer cantors, and magnetism effects in the Design‐Expert software, the best optimization occurred for u = 0.07, T = 1.15, and H = 4.531.

show abstract

Prediction of Dynamic Viscosity of a New Non-Newtonian Hybrid Nanofluid Using Experimental and Artificial Neural Network (Ann) Methods

Cited by 20 publications

References 0 publications

Numerical Solution of Heat Transfer Flow of Casson Hybrid Nanofluid over Vertical Stretching Sheet with Magnetic Field Effect

Numerical Solution of Heat Transfer Flow of Casson Hybrid Nanofluid over Vertical Stretching Sheet with Magnetic Field Effect

MHD williamson nanofluid flow in the rheology of thermal radiation, joule heating, and chemical reaction using the Levenberg–Marquardt neural network algorithm

Hybrid surveying of radiation and magnetic impacts on Maxwell fluid with MWCNT nanotube influence of two wire loops

Contact Info

Product

Resources

About