Numerical study of the fluid dynamics and heat transfer for shear-thinning nanofluids in a micro pin-fin heat sink

González, Amaru; Ruz, O.; Castillo, Ernesto

doi:10.1016/j.csite.2021.101635

Cited by 18 publications

(1 citation statement)

References 55 publications

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“…It is also used to control the temperature of car radiators and electrical equipment (Zhao et al, 2016). Heat transfer is used by solar thermal collectors (González et al, 2021) and thermal control components (Okonkwo et al, 2021) to convert solar energy into heat and power. Several of these components must quickly distribute heat in order for the system to work at its maximum effectiveness and efficiency (Okonkwo et al, 2018).…”

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

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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.

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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

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Analysis of a Stabilised Finite Element Method for Power-Law Fluids

Barrenechea

Süli

2022

Constr Approx

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A low-order finite element method is constructed and analysed for an incompressible non-Newtonian flow problem with power-law rheology. The method is based on a continuous piecewise linear approximation of the velocity field and piecewise constant approximation of the pressure. Stabilisation, in the form of pressure jumps, is added to the formulation to compensate for the failure of the inf-sup condition, and using an appropriate lifting of the pressure jumps a divergence-free approximation to the velocity field is built and included in the discretisation of the convection term. This construction allows us to prove the convergence of the resulting finite element method for the entire range $$r>\frac{2 d}{d+2}$$ r > 2 d d + 2 of the power-law index r for which weak solutions to the model are known to exist in d space dimensions, $$d \in \{2,3\}$$ d ∈ { 2 , 3 } .

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Comparative study of various solar power generation systems integrated with nanofluid-flat heat pipe

Gao

Chen

et al. 2022

Applied Energy

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Numerical study of the fluid dynamics and heat transfer for shear-thinning nanofluids in a micro pin-fin heat sink

Cited by 18 publications

References 55 publications

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

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

Analysis of a Stabilised Finite Element Method for Power-Law Fluids

Comparative study of various solar power generation systems integrated with nanofluid-flat heat pipe

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