Thermal radiation impact on heat and mass transfer analysis of nanofluids inside a cavity with gyrotactic microorganisms

Reddy, P. Sudarsana; Sreedevi, Paluru

doi:10.1108/hff-04-2023-0191

Cited by 17 publications

(1 citation statement)

References 59 publications

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“…The steady natural convection heat transfer of a hybrid nanosuspension within a partially heated/cooled trapezoidal zone under the influence of uniform Lorentz force was studied by Roşca et al (2023). The effects of thermal radiation on the appearance of heat transmission inside an enclosure containing gyrotactic bacteria were analyzed by Reddy and Sreedevi (2023).…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of radiative solar trough collectors for MHD Jeffrey hybrid nanofluid flow with gyrotactic microorganism: entropy generation optimization

Kumar,

Sharma,

Bin-Mohsen

et al. 2024

HFF

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Purpose A parabolic trough solar collector is an advanced concentrated solar power technology that significantly captures radiant energy. Solar power will help different sectors reach their energy needs in areas where traditional fuels are in use. This study aims to examine the sensitivity analysis for optimizing the heat transfer and entropy generation in the Jeffrey magnetohydrodynamic hybrid nanofluid flow under the influence of motile gyrotactic microorganisms with solar radiation in the parabolic trough solar collectors. The influences of viscous dissipation and Ohmic heating are also considered in this investigation. Design/methodology/approach Governing partial differential equations are derived via boundary layer assumptions and nondimensionalized with the help of suitable similarity transformations. The resulting higher-order coupled ordinary differential equations are numerically investigated using the Runga-Kutta fourth-order numerical approach with the shooting technique in the computational MATLAB tool. Findings The numerical outcomes of influential parameters are presented graphically for velocity, temperature, entropy generation, Bejan number, drag coefficient and Nusselt number. It is observed that escalating the values of melting heat parameter and the Prandl number enhances the Nusselt number, while reverse effect is observed with an enhancement in the magnetic field parameter and bioconvection Lewis number. Increasing the magnetic field and bioconvection diffusion parameter improves the entropy and Bejan number. Originality/value Nanotechnology has captured the interest of researchers due to its engrossing performance and wide range of applications in heat transfer and solar energy storage. There are numerous advantages of hybrid nanofluids over traditional heat transfer fluids. In addition, the upswing suspension of the motile gyrotactic microorganisms improves the hybrid nanofluid stability, enhancing the performance of the solar collector. The use of solar energy reduces the industry’s dependency on fossil fuels.

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Section: Introductionmentioning

confidence: 99%

Statistical analysis of radiative solar trough collectors for MHD Jeffrey hybrid nanofluid flow with gyrotactic microorganism: entropy generation optimization

Kumar,

Sharma,

Bin-Mohsen

et al. 2024

HFF

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Bioconvective magnetized flow analysis of a tangent hyperbolic nanofluid with nonuniform source/sink over an exponentially porous stretching sheet

Ahsan,

Khan,

Alqurashi

et al. 2024

Z Angew Math Mech

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This study use numerical method to investigate the transportation of a bioconvective magnetized tangent hyperbolic nanofluid across an exponentially porous stretched sheet. The flow model takes into account the important contributions of Joule heating, thermal radiation, activation energy, and nonuniform heat source/sink. Moreover, slip boundary conditions with gyrotactic microorganisms are taken into flow analysis. The flow model is converted into a system of nonlinear ordinary differential equations (ODEs) using suitable similarity variables. The bvp4c approach in MATLAB is used to get numerical solutions for this system. The study evaluates the influence of different parameters on the velocity, temperature, concentration, and microorganism profile via graphical representations. The results indicate that increasing the mixed convection parameters accelerates the velocity profile, whereas raising the magnetic parameter slows it down. The temperature profile increases with higher values of radiation parameter, however, it decreases as the Prandtl number and thermal slip parameter increase. Furthermore, the microorganism profile decreases as the bioconvection Lewis number and microorganism slip parameter increase.

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A novel design of recurrent neural network to investigate the heat transmission of radiative Casson nanofluid flow consisting of carbon nanotubes (CNTs) across a curved stretchable surface

Shahbaz,

Ahmad,

Raja

et al. 2024

Z Angew Math Mech

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This study aims to develop a supervised learning artificial recurrent neural network algorithm supported by Bayesian regularization called (ARNN‐BR) to analyze the impact of physical parameters, including radius of curvature (), Casson parameter (), heat generation parameter () and radiation parameter () on velocity fʹ(η), and temperature profiles θ(η) in Casson nanofluid consisting of carbon nanotubes (CNTs‐CNF) model for single and multiwalled CNTs across a curved stretched surface. The numerical dataset of the proposed model has been constructed by varying various parameters for five scenarios that are used in a Bayesian regularization‐based intelligent computing method to build networks for approximating the numerical solutions of CNTs‐CNF model. It is observed that increment in the dimensionless radius of curvature () causes to rise an increase in the velocity profile fʹ(η) for both SWCNTs and MWCNTs. However, a contrasting trend is observed when the Casson parameter () is increased to higher values. The temperature θ(η) of fluid increases as the heat generation parameter () and radiation parameter () increase. However, an opposite behavior is noticed when the dimensionless radius of curvature () varies. The effectiveness and significance of designed Bayesian regularization based artificial recurrent neural networks (ARNN‐BR) is demonstrated through regression index measurements, error histogram studies, auto‐correlation analysis and convergence curves showing a minimal level of mean square error (E‐11 to E‐04) for the comprehensive simulations of CNTs‐CNF model. The designed ARNN‐BR algorithm is employed in many domains such as voice recognition, machine translation, identification of neurological brain illnesses as well as for automated translation of texts across different languages.

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Thermal radiation impact on heat and mass transfer analysis of nanofluids inside a cavity with gyrotactic microorganisms

Cited by 17 publications

References 59 publications

Statistical analysis of radiative solar trough collectors for MHD Jeffrey hybrid nanofluid flow with gyrotactic microorganism: entropy generation optimization

Statistical analysis of radiative solar trough collectors for MHD Jeffrey hybrid nanofluid flow with gyrotactic microorganism: entropy generation optimization

Bioconvective magnetized flow analysis of a tangent hyperbolic nanofluid with nonuniform source/sink over an exponentially porous stretching sheet

A novel design of recurrent neural network to investigate the heat transmission of radiative Casson nanofluid flow consisting of carbon nanotubes (CNTs) across a curved stretchable surface

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