This study explores the complex dynamics of unsteady convective flow in micropolar nanofluids over a rough conical surface, with a focus on the effects of triple diffusive transport and Arrhenius activation energy. Th...This study explores the complex dynamics of unsteady convective flow in micropolar nanofluids over a rough conical surface, with a focus on the effects of triple diffusive transport and Arrhenius activation energy. The primary objective is to understand the interplay among nonlinear convection, micro-rotation effects, and species diffusion under the influence of thermal and electromagnetic forces. The analysis is motivated by practical applications of cryogenic fluids, specifically liquid hydrogen and liquid nitrogen,where precise control of heat and mass transport is critical. The conical surface roughness is mathematically modeled as a high-frequency, small-amplitude sinusoidal waveform.To address the non-similar nature of the boundary-layer equations, Mangler's transformations are employed, followed by the implementation of a finite difference scheme for numerical solutions. The methodology further integrates a machine learning-based neural network to predict the skin friction under the influence of roughness-induced perturbations, ensuring computational efficiency and improved generalization. The study yields several novel findings. Notably, the presence of surface roughness introduces the wavelike modulations in the local skin friction coefficient. It is also observed that nonlinear convective interactions, enhanced by temperature gradients and vortex viscosity parameters, significantly intensify near-wall velocity gradients. Moreover, key physical quantities are correlated with governing parameters using power-law relationships, providing generalized predictive models. The validation of the numerical results is achieved through consistency checks with the existing limiting solutions and convergence analysis, ensuring the reliability of the proposed computational framework.展开更多
In this paper,numerical investigations for peristaltic motion of dusty nanofluids in a curved channel are performed.Two systems of partial differential equations are presented for the nanofluid and dusty phases and th...In this paper,numerical investigations for peristaltic motion of dusty nanofluids in a curved channel are performed.Two systems of partial differential equations are presented for the nanofluid and dusty phases and then the approximations of the long wave length and low Reynolds number are applied.The physical domain is transformed to a rectangular computational model using suitable grid transformations.The resulting systems are solved numerically using shooting method and mathematical forms for the pressure distributions are introduced.The controlling parameters in this study are the thermal buoyancy parameter G_(r),the concentration buoyancy parameter Gc,the amplitude ratio,the Eckert number Ec,the thermophoresis parameter N_(t) and the Brownian motion parameter Nb and the dusty parameters D_(s);α_(s).The obtained results revealed that an increase in the Eckert number enhances the temperature of the fluid and dusty particles while the nanoparticle volume fraction is reduced.Also,both of the temperature and nanoparticles volume fraction are supported by the growing of the Brownian motion parameter.展开更多
An implicit finite difference(FD)and artificial neural network(ANN)tech-niques are applied to study the triple diffusion and non-linear mixed convection flow around a vertical cone.The forced flow is due to an impulsi...An implicit finite difference(FD)and artificial neural network(ANN)tech-niques are applied to study the triple diffusion and non-linear mixed convection flow around a vertical cone.The forced flow is due to an impulsive motion of a micropolar nanofluid while the buoyancy-driven flow is obtained using the quadratic form of Boussinesq approx-imation.Two governing equations are introduced for the species concentrations;those include non-linear chemical reactions.It is focused on the cases of the weak concentration of microelements,opposing and assisting flow,and the roles of the magnetic field,viscous dissipation,and convective boundary conditions are examined.The solution methodology is based on Mangler’s transformations.At the same time,the effective ANN is used to predict some important physical quantities such as heat transfer rate against some key factors such as Biot number,Eckert number,and magnetic coefficient.Remarkably,the flow rate in the assisting flow is up to 0.95%higher than in the opposing flow.Across all cases,an increase in the vortex parameter(K Z 0:1-1:2)enhances fluid friction near the cone surface by 63.1%.These findings are particularly relevant for industrial applications involving heat and mass transfer in nanofluid systems,such as microreactors,biomedical engineering,and thermal energy storage.展开更多
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/111/46
文摘This study explores the complex dynamics of unsteady convective flow in micropolar nanofluids over a rough conical surface, with a focus on the effects of triple diffusive transport and Arrhenius activation energy. The primary objective is to understand the interplay among nonlinear convection, micro-rotation effects, and species diffusion under the influence of thermal and electromagnetic forces. The analysis is motivated by practical applications of cryogenic fluids, specifically liquid hydrogen and liquid nitrogen,where precise control of heat and mass transport is critical. The conical surface roughness is mathematically modeled as a high-frequency, small-amplitude sinusoidal waveform.To address the non-similar nature of the boundary-layer equations, Mangler's transformations are employed, followed by the implementation of a finite difference scheme for numerical solutions. The methodology further integrates a machine learning-based neural network to predict the skin friction under the influence of roughness-induced perturbations, ensuring computational efficiency and improved generalization. The study yields several novel findings. Notably, the presence of surface roughness introduces the wavelike modulations in the local skin friction coefficient. It is also observed that nonlinear convective interactions, enhanced by temperature gradients and vortex viscosity parameters, significantly intensify near-wall velocity gradients. Moreover, key physical quantities are correlated with governing parameters using power-law relationships, providing generalized predictive models. The validation of the numerical results is achieved through consistency checks with the existing limiting solutions and convergence analysis, ensuring the reliability of the proposed computational framework.
基金the Deanship of Scientific Research atKing Khalid University for funding this work through research groups program under Grant Number(R.G.P2/72/41).
文摘In this paper,numerical investigations for peristaltic motion of dusty nanofluids in a curved channel are performed.Two systems of partial differential equations are presented for the nanofluid and dusty phases and then the approximations of the long wave length and low Reynolds number are applied.The physical domain is transformed to a rectangular computational model using suitable grid transformations.The resulting systems are solved numerically using shooting method and mathematical forms for the pressure distributions are introduced.The controlling parameters in this study are the thermal buoyancy parameter G_(r),the concentration buoyancy parameter Gc,the amplitude ratio,the Eckert number Ec,the thermophoresis parameter N_(t) and the Brownian motion parameter Nb and the dusty parameters D_(s);α_(s).The obtained results revealed that an increase in the Eckert number enhances the temperature of the fluid and dusty particles while the nanoparticle volume fraction is reduced.Also,both of the temperature and nanoparticles volume fraction are supported by the growing of the Brownian motion parameter.
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Research Project under grant number RGP2/111/46.
文摘An implicit finite difference(FD)and artificial neural network(ANN)tech-niques are applied to study the triple diffusion and non-linear mixed convection flow around a vertical cone.The forced flow is due to an impulsive motion of a micropolar nanofluid while the buoyancy-driven flow is obtained using the quadratic form of Boussinesq approx-imation.Two governing equations are introduced for the species concentrations;those include non-linear chemical reactions.It is focused on the cases of the weak concentration of microelements,opposing and assisting flow,and the roles of the magnetic field,viscous dissipation,and convective boundary conditions are examined.The solution methodology is based on Mangler’s transformations.At the same time,the effective ANN is used to predict some important physical quantities such as heat transfer rate against some key factors such as Biot number,Eckert number,and magnetic coefficient.Remarkably,the flow rate in the assisting flow is up to 0.95%higher than in the opposing flow.Across all cases,an increase in the vortex parameter(K Z 0:1-1:2)enhances fluid friction near the cone surface by 63.1%.These findings are particularly relevant for industrial applications involving heat and mass transfer in nanofluid systems,such as microreactors,biomedical engineering,and thermal energy storage.
