This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and sol...This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and solved using the finite volume method.The simulations examine the impact of key parameters such as heat source length and position,Peclet number,porosity,and heat generation/absorption on flow patterns,temperature distribution,concentration profiles,and microorganism rotation.Results indicate that extending the heat source length enhances convective currents and heat transfer efficiency,while optimizing the heat source position reduces entropy generation.Higher Peclet numbers amplify convective currents and microorganism distribution complexity.Variations in porosity and heat generation/absorption significantly influence flow dynamics.Additionally,the artificial neural network model reliably predicts the mean Nusselt and Sherwood numbers(Nu&Sh),demonstrating its effectiveness for such analyses.The simulation results reveal that increasing the heat source length significantly enhances heat transfer,as evidenced by a 15%increase in the mean Nusselt number.展开更多
The magnetic impacts upon the transport of heat and mass of an electrically conducting nanofluid within an annulus among an inner rhombus with convex and outer cavity with periodic temperature/concentration profiles o...The magnetic impacts upon the transport of heat and mass of an electrically conducting nanofluid within an annulus among an inner rhombus with convex and outer cavity with periodic temperature/concentration profiles on its left wall are assessed by the ISPH method.The right wall has Tcand C,cflat walls are adiabatic,and the temperature and concentration of the left wall are altered sinusoidally with time.The features of the heat and mass transfer and fluid flow through an annulus are assessed across a wide scale of Hartmann number Ha,Soret number Sr,oscillation amplitude A,Dufour number Du,nanoparticles parameterΦ,oscillation frequency f,Rayleigh number Ra,and radius of a superellipse a at Lewis number Le=20,magnetic field’s angle g=45°,Prandtl number Pr=6.2,a superellipse coefficient n=3/2,and buoyancy parameter N=1.The results reveal that the velocity’s maximum reduces by 70.93%as Ha boosts from 0 to 50,and by 66.24%as coefficient a boosts from 0.1 to 0.4.Whilst the velocity’s maximum augments by 83.04%as Sr increases from 0.6 to 2 plus a decrease in Du from 1 to0.03.The oscillation amplitude A,and frequency f are significantly affecting the nanofluid speed,and heat and mass transfer inside an annulus.Increasing the parameters A and f is augmenting the values of mean Nusselt number-(Sh)and mean Sherwood number^-(Nu).Increasing the radius of a superellipse a enhances the values of^(Nu)and^(Sh).展开更多
The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological...The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological factors is investigated.The analysis combines a traditional numerical approach with machine learning techniques.Mathematical equations describing the system are transformed into a dimensionless form and then solved using computational methods.The artificial neural network(ANN)model,trained with the Levenberg-Marquardt method,accurately predicts(Nu)values,showing high correlation(R=1),low mean squared error(MSE),and minimal error clustering.Parametric analysis reveals significant effects of parameters,length and location of source(B),(D),heat generation/absorption coefficient(Q),and porosity parameter(ε).Increasing the cooling area length(B)reduces streamline intensity and local Nusselt and Sherwood numbers,while decreasing isotherms,isoconcentrations,and micro-rotation.The Bejan number(Be+)decreases with increasing(B),whereas(Be+++),and global entropy(e+++)increase.Variations in(Q)slightly affect streamlines but reduce isotherm intensity and average Nusselt numbers.Higher(D)significantly impacts isotherms,iso-concentrations,andmicro-rotation,altering streamline contours and local Bejan number distribution.Increased(ε)enhances streamline strength and local Nusselt number profiles but has mixed effects on average Nusselt numbers.These findings highlight the complex interactions between cooling area length,fluid flow,and heat transfer properties.By combining finite volume method(FVM)with machine learning technique,this study provides valuable insights into the complex interactions between key parameters and heat transfer,contributing to the development of more efficient designs in applications such as cooling systems,energy storage,and bioengineering.展开更多
基金Dean ship of Scientific Research at King Khalid University,Abha,Saudi Arabia,for funding this work through the Research Group Project(Grant No.RGP.2/610/45)funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project(Grant No.PNURSP2024R102),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘This study numerically investigates inclined magneto-hydrodynamic natural convection in a porous cavity filled with nanofluid containing gyrotactic microorganisms.The governing equations are nondimensionalized and solved using the finite volume method.The simulations examine the impact of key parameters such as heat source length and position,Peclet number,porosity,and heat generation/absorption on flow patterns,temperature distribution,concentration profiles,and microorganism rotation.Results indicate that extending the heat source length enhances convective currents and heat transfer efficiency,while optimizing the heat source position reduces entropy generation.Higher Peclet numbers amplify convective currents and microorganism distribution complexity.Variations in porosity and heat generation/absorption significantly influence flow dynamics.Additionally,the artificial neural network model reliably predicts the mean Nusselt and Sherwood numbers(Nu&Sh),demonstrating its effectiveness for such analyses.The simulation results reveal that increasing the heat source length significantly enhances heat transfer,as evidenced by a 15%increase in the mean Nusselt number.
基金the Deanship of Scientific Research at King Khalid University,Abha,Saudi Arabia,for funding this work through the Research Group Project under Grant Number(RGP.2/144/42)funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program。
文摘The magnetic impacts upon the transport of heat and mass of an electrically conducting nanofluid within an annulus among an inner rhombus with convex and outer cavity with periodic temperature/concentration profiles on its left wall are assessed by the ISPH method.The right wall has Tcand C,cflat walls are adiabatic,and the temperature and concentration of the left wall are altered sinusoidally with time.The features of the heat and mass transfer and fluid flow through an annulus are assessed across a wide scale of Hartmann number Ha,Soret number Sr,oscillation amplitude A,Dufour number Du,nanoparticles parameterΦ,oscillation frequency f,Rayleigh number Ra,and radius of a superellipse a at Lewis number Le=20,magnetic field’s angle g=45°,Prandtl number Pr=6.2,a superellipse coefficient n=3/2,and buoyancy parameter N=1.The results reveal that the velocity’s maximum reduces by 70.93%as Ha boosts from 0 to 50,and by 66.24%as coefficient a boosts from 0.1 to 0.4.Whilst the velocity’s maximum augments by 83.04%as Sr increases from 0.6 to 2 plus a decrease in Du from 1 to0.03.The oscillation amplitude A,and frequency f are significantly affecting the nanofluid speed,and heat and mass transfer inside an annulus.Increasing the parameters A and f is augmenting the values of mean Nusselt number-(Sh)and mean Sherwood number^-(Nu).Increasing the radius of a superellipse a enhances the values of^(Nu)and^(Sh).
基金Deanship of Scientific Research at King Khalid University,Abha,Saudi Arabia,for funding this work through theResearch Group Project underGrant Number(RGP.2/610/45)funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R102)PrincessNourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The research examines fluid behavior in a porous box-shaped enclosure.The fluid contains nanoscale particles and swimming microbes and is subject to magnetic forces at an angle.Natural circulation driven by biological factors is investigated.The analysis combines a traditional numerical approach with machine learning techniques.Mathematical equations describing the system are transformed into a dimensionless form and then solved using computational methods.The artificial neural network(ANN)model,trained with the Levenberg-Marquardt method,accurately predicts(Nu)values,showing high correlation(R=1),low mean squared error(MSE),and minimal error clustering.Parametric analysis reveals significant effects of parameters,length and location of source(B),(D),heat generation/absorption coefficient(Q),and porosity parameter(ε).Increasing the cooling area length(B)reduces streamline intensity and local Nusselt and Sherwood numbers,while decreasing isotherms,isoconcentrations,and micro-rotation.The Bejan number(Be+)decreases with increasing(B),whereas(Be+++),and global entropy(e+++)increase.Variations in(Q)slightly affect streamlines but reduce isotherm intensity and average Nusselt numbers.Higher(D)significantly impacts isotherms,iso-concentrations,andmicro-rotation,altering streamline contours and local Bejan number distribution.Increased(ε)enhances streamline strength and local Nusselt number profiles but has mixed effects on average Nusselt numbers.These findings highlight the complex interactions between cooling area length,fluid flow,and heat transfer properties.By combining finite volume method(FVM)with machine learning technique,this study provides valuable insights into the complex interactions between key parameters and heat transfer,contributing to the development of more efficient designs in applications such as cooling systems,energy storage,and bioengineering.
