A high-order hybrid numerical framework is developed by coupling a three-stage exponential time integrator with a Runge–Kutta scheme for the efficient solution of partial differential equations involving first-order ...A high-order hybrid numerical framework is developed by coupling a three-stage exponential time integrator with a Runge–Kutta scheme for the efficient solution of partial differential equations involving first-order time derivatives.The proposed scheme attains third-order temporal accuracy and is rigorously validated through stability and convergence analyses for both scalar and coupled systems.Its effectiveness is demonstrated by simulating unsteady Eyring-Prandtl non-Newtonian nanofluid flow over a Riga plate with coupled heat and mass transfer under electromagnetic actuation.The physical model accounts for Brownian motion and thermophoresis,and the nanofluid considered is a Prandtl-type non-Newtonian base fluid containing suspended nanoparticles,with heat and mass transport governed by coupled momentum,energy,and concentration equations.Numerical simulations are performed over practically relevant parameter ranges,with the Reynolds number fixed at Re=5 and the Prandtl number set to Pr=3 to represent moderate inertial and thermal diffusion effects typical of nanofluid transport systems.To enhance computational efficiency,an artificial neural network(ANN)-based surrogate model is developed to predict the skin friction coefficient and local Sherwood number as functions of Reynolds number,Prandtl number,Schmidt number,Brownian motion,and thermophoresis parameters.The training dataset is generated entirely from high-fidelity numerical simulations produced by the proposed hybrid scheme.The data are systematically partitioned into 70%for training,15%for validation,and 15%for testing,ensuring reliable generalization.Regression analysis yields a near-unity correlation coefficient(R≈0.99),while error histograms exhibit tightly clustered residuals around zero,confirming high predictive accuracy.Furthermore,a benchmark convergence study using Stokes’first problem demonstrates that the proposed scheme consistently achieves lower global error norms than the classical Runge–Kutta method for identical spatial and temporal resolutions.Overall,this study introduces a novel computational intelligence framework that integrates high-order numerical solvers with machine learning,offering a robust and time-efficient tool for advanced modeling and real-time prediction of non-Newtonian nanofluid transport phenomena under electromagnetic flow control.展开更多
Hybrid nanofluids have gained significant attention for their superior thermal and rheological characteristics,offering immense potential in energy conversion,biomedical transport,and electromagnetic flow control syst...Hybrid nanofluids have gained significant attention for their superior thermal and rheological characteristics,offering immense potential in energy conversion,biomedical transport,and electromagnetic flow control systems.Understanding their dynamic behavior under coupled magnetic,rotational,and reactive effects is crucial for the development of efficient thermal management technologies.This study develops a neuro-fuzzy computational framework to examine the dynamics of a reactive Cu–TiO_(2)–H_(2)Ohybrid nanofluid flowing through a squarely elevated Riga tunnel.The governing model incorporates Hall and ion-slip effects,thermal radiation,and first-order chemical reactions under ramped thermo-solutal boundary conditions and rotational electromagnetic forces.Closed-form analytical solutions are derived via the Laplace transform method to describe the transient velocity,temperature,and concentration fields.To complement and validate the analytical model,an artificial neural network(ANN)optimized using the Levenberg–Marquardt backpropagation algorithm(ANN-LMBPA)is trained on datasets generated in Mathematica.Regression and error analyses confirm the model’s predictive robustness,with mean squared errors ranging between 10^(-4) and 10^(-9).In addition,an Adaptive Neuro-Fuzzy Inference System(ANFIS)is developed to estimate the heat transfer rate(HTR),achieving aminimal RMSE of 0.011012 for the heat transfer coefficient(HTC).The findings reveal that rotational motion and Hall–ion slip effects suppress primary velocity but enhance secondary flow,while the modified Hartmann number(Lorentz force)accelerates both components.Thermal radiation increases fluid temperature,whereas higher Schmidt numbers and reaction rates diminish solute concentration.The HTR decreases with increasing radiation and nanoparticle volume fraction,while the mass transfer rate(MTR)improves under stronger chemical reactivity.Overall,the proposed hybrid analytical–AI framework demonstrates high accuracy and efficiency,offering valuable insights for the design and optimization of electromagnetic nanofluid systems in advanced thermal and process engineering applications.展开更多
This article scrutinizes the features of viscous dissipation in the stagnation point ?ow past through a linearly stretched Riga wall by implementing Cattaneo-Christov heat ?ux model. Viscous dissipation is carried out...This article scrutinizes the features of viscous dissipation in the stagnation point ?ow past through a linearly stretched Riga wall by implementing Cattaneo-Christov heat ?ux model. Viscous dissipation is carried out in Cattaneo-Christov diffusion analysis for the ?rst time in this letter. As a result of Cattaneo-Christov model, some extra terms of viscous dissipation are appeared in the energy equation. These extra terms of viscous dissipation are missing in the literature. On the utilization of suitable transformations, the equations governing the problem are reduced under the boundary layer approximation into the non-linear and dimensionless ordinary differential equations. Convergent approach is utilized to solve the dimensionless governing equations. The solution thus acquired is used to highlight the effects of emerging parameters on velocity distribution and ?uid's temperature through the graphs. Features of the drag force(or skin friction co-e?cient) are graphically interpreted. It is noticed that the presence of modi?ed Hartman number helps to reduce the ?uid's temperature but enhances the velocity pro?le. Further an enlargement in the value of thermal time relaxation parameter helps to decrease the temperature distribution.展开更多
The present study describes long term PM10 and PM2.5 changes in typical street canyon with particular emphasis on seasonal, diurnal variations in context with meteorological data. In order to understand PM10 pollution...The present study describes long term PM10 and PM2.5 changes in typical street canyon with particular emphasis on seasonal, diurnal variations in context with meteorological data. In order to understand PM10 pollution sources during 28 April 2007-31 December 2007, chemical composition measurements were done with particular emphasis on heavy metals (As, Cd, Ni, and Pb), crustal material (Ca, Mg, Na, and K) and anions (sulphates, nitrates, chlorides). Meteorological data used for this evolutional analysis were measured close to traffic related stations and several meteorological parameters were analyzed in relation to particulate measurements. Keep in mind that atmospheric aerosols are generally hydroscopic. Relative humidity which plays very important role in rain/snow and humidity impact are analyzed.展开更多
Casson fluid-mediated hybrid nanofluids are more effective at transferring heat than traditional heat transfer fluids in terms of thermal conductivity.Heat exchangers,cooling systems and other thermal management syste...Casson fluid-mediated hybrid nanofluids are more effective at transferring heat than traditional heat transfer fluids in terms of thermal conductivity.Heat exchangers,cooling systems and other thermal management systems are ideal for use with Casson fluids.Precise control of the flow and release of medication is necessary when using Casson fluids in drug delivery systems because of their unique rheological properties.Nanotechnology involves the creation of nanoparticles that are loaded with drugs and distributed in Casson fluid-based carriers for targeted delivery.In this study,to create a hybrid nanofluid,both single-walled carbon nanotubes(SWCNTs)and multi-walled carbon nanotubes(MWCNTs)are dispersed in a Casson fluid with Fourier’s and Fick’s laws assumptions.The Casson fluid is suitable for various engineering and medical applications due to the enhancement of heat transfer and thermal conductivity by the carbon nanotubes.Our objective is to understand how SWCNTs and MWCNTs impact the flow field by studying the flow behavior of the Casson hybrid nanofluid when it is stretched against a Riga plate.The Darcy-Forchheimer model is also used to account for the impact of the porous medium near the stretching plate.Both linear and quadratic drag terms are taken into account in this model to accurately predict the flow behavior of the nanofluid.In addition,the homotopy analysis method is utilized to address the model problem.The outcomes are discussed and deliberated based on drug delivery applications.These findings shed valuable light on the flow characteristics of a Casson hybrid nanofluid comprising SWCNTs and MWCNTs.It is observed that the incorporation of carbon nanotubes makes the nanofluid a promising candidate for medical applications due to its improved heat transfer properties.展开更多
This article is based on the impulsively started horizontal Riga plate in two dimensional unsteady Casson fluid flows with rotation. The plate starts abruptly from the rest relative to the rotating fluids moving with ...This article is based on the impulsively started horizontal Riga plate in two dimensional unsteady Casson fluid flows with rotation. The plate starts abruptly from the rest relative to the rotating fluids moving with uniform acceleration in its plane. Numerical solutions are acquired by using explicit finite difference method and estimated results have been gained for various values of the Rotational parameter, modified Hartmann number, Prandtl number, Radiative parameter, Eckert number, Heat source parameter, Schmidt number, and the Soret number. Both the Compaq visual FORTRAN 6.6a and MATLAB R2015a tools have been used to find the numerical solutions and the graphical presentation. The Skin friction, Nusselt number and Sherwood number have been computed and the effects of some pertinent parameters on various distributions are discussed briefly and presented graphically.展开更多
The present investigation inspects the unsteady,incompressible MHD-induced flow of a ternary hybrid nanofluid made of SiO_(2)(silicon dioxide),ZnO(zinc oxide),and MWCNT(multi-walled carbon nanotubes)suspended in a wat...The present investigation inspects the unsteady,incompressible MHD-induced flow of a ternary hybrid nanofluid made of SiO_(2)(silicon dioxide),ZnO(zinc oxide),and MWCNT(multi-walled carbon nanotubes)suspended in a water-ethylene glycol base fluid between two perforated squeezing Riga plates.This problem is important because it helps us understand the complicated connections between magnetic fields,nanofluid dynamics,and heat transport,all of which are critical for designing thermal management systems.These findings are especially useful for improving the design of innovative cooling technologies in electronics,energy systems,and healthcare applications.No prior study has been done on the theoretical study of the flow of ternary nanofluid(SiO_(2)+ZnO+MWCNT/Water−EthylGl ycol,(60∶40))past a pierced squeezed Riga plates using the boundary value problem solver 4th-order collocation(BVP4C)numerical approach to date.So,the current work has been carried out to fill this gap,and the core purpose of this study is to explore the aspects that enhance the heat transfer of base fluids(H_(2)O/EG)suspended with three nanomaterials SiO_(2),ZnO,and MWCNT.The Riga plates introduce electromagnetic forcing through an embedded array of magnets and electrodes,generating Lorentz forces to regulate the flow.The squeezing effect introduces dynamic boundary movement,which enhances mixing;however,permeability,due to porosity,replicates the true material limits.Similarity transformations of the Navier-Stokes and energy equations result in a highly nonlinear set of ordinary differential equations that govern momentum and thermal energy transport.The subsequent boundary value problem is solved utilizing the BVP4C numerical approach.The study observes the impact of magnetic parameters,squeezing velocity,solid volume percentages of the three nanoparticles,and porous medium factors on velocity and temperature fields.Results show that magnetic fields reduce the velocity profile by 6.75%due to increased squeezing and medium effects.Tri-hybrid nanofluids notice a 9%rise in temperature with higher thermal radiation.展开更多
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2603).
文摘A high-order hybrid numerical framework is developed by coupling a three-stage exponential time integrator with a Runge–Kutta scheme for the efficient solution of partial differential equations involving first-order time derivatives.The proposed scheme attains third-order temporal accuracy and is rigorously validated through stability and convergence analyses for both scalar and coupled systems.Its effectiveness is demonstrated by simulating unsteady Eyring-Prandtl non-Newtonian nanofluid flow over a Riga plate with coupled heat and mass transfer under electromagnetic actuation.The physical model accounts for Brownian motion and thermophoresis,and the nanofluid considered is a Prandtl-type non-Newtonian base fluid containing suspended nanoparticles,with heat and mass transport governed by coupled momentum,energy,and concentration equations.Numerical simulations are performed over practically relevant parameter ranges,with the Reynolds number fixed at Re=5 and the Prandtl number set to Pr=3 to represent moderate inertial and thermal diffusion effects typical of nanofluid transport systems.To enhance computational efficiency,an artificial neural network(ANN)-based surrogate model is developed to predict the skin friction coefficient and local Sherwood number as functions of Reynolds number,Prandtl number,Schmidt number,Brownian motion,and thermophoresis parameters.The training dataset is generated entirely from high-fidelity numerical simulations produced by the proposed hybrid scheme.The data are systematically partitioned into 70%for training,15%for validation,and 15%for testing,ensuring reliable generalization.Regression analysis yields a near-unity correlation coefficient(R≈0.99),while error histograms exhibit tightly clustered residuals around zero,confirming high predictive accuracy.Furthermore,a benchmark convergence study using Stokes’first problem demonstrates that the proposed scheme consistently achieves lower global error norms than the classical Runge–Kutta method for identical spatial and temporal resolutions.Overall,this study introduces a novel computational intelligence framework that integrates high-order numerical solvers with machine learning,offering a robust and time-efficient tool for advanced modeling and real-time prediction of non-Newtonian nanofluid transport phenomena under electromagnetic flow control.
文摘Hybrid nanofluids have gained significant attention for their superior thermal and rheological characteristics,offering immense potential in energy conversion,biomedical transport,and electromagnetic flow control systems.Understanding their dynamic behavior under coupled magnetic,rotational,and reactive effects is crucial for the development of efficient thermal management technologies.This study develops a neuro-fuzzy computational framework to examine the dynamics of a reactive Cu–TiO_(2)–H_(2)Ohybrid nanofluid flowing through a squarely elevated Riga tunnel.The governing model incorporates Hall and ion-slip effects,thermal radiation,and first-order chemical reactions under ramped thermo-solutal boundary conditions and rotational electromagnetic forces.Closed-form analytical solutions are derived via the Laplace transform method to describe the transient velocity,temperature,and concentration fields.To complement and validate the analytical model,an artificial neural network(ANN)optimized using the Levenberg–Marquardt backpropagation algorithm(ANN-LMBPA)is trained on datasets generated in Mathematica.Regression and error analyses confirm the model’s predictive robustness,with mean squared errors ranging between 10^(-4) and 10^(-9).In addition,an Adaptive Neuro-Fuzzy Inference System(ANFIS)is developed to estimate the heat transfer rate(HTR),achieving aminimal RMSE of 0.011012 for the heat transfer coefficient(HTC).The findings reveal that rotational motion and Hall–ion slip effects suppress primary velocity but enhance secondary flow,while the modified Hartmann number(Lorentz force)accelerates both components.Thermal radiation increases fluid temperature,whereas higher Schmidt numbers and reaction rates diminish solute concentration.The HTR decreases with increasing radiation and nanoparticle volume fraction,while the mass transfer rate(MTR)improves under stronger chemical reactivity.Overall,the proposed hybrid analytical–AI framework demonstrates high accuracy and efficiency,offering valuable insights for the design and optimization of electromagnetic nanofluid systems in advanced thermal and process engineering applications.
文摘This article scrutinizes the features of viscous dissipation in the stagnation point ?ow past through a linearly stretched Riga wall by implementing Cattaneo-Christov heat ?ux model. Viscous dissipation is carried out in Cattaneo-Christov diffusion analysis for the ?rst time in this letter. As a result of Cattaneo-Christov model, some extra terms of viscous dissipation are appeared in the energy equation. These extra terms of viscous dissipation are missing in the literature. On the utilization of suitable transformations, the equations governing the problem are reduced under the boundary layer approximation into the non-linear and dimensionless ordinary differential equations. Convergent approach is utilized to solve the dimensionless governing equations. The solution thus acquired is used to highlight the effects of emerging parameters on velocity distribution and ?uid's temperature through the graphs. Features of the drag force(or skin friction co-e?cient) are graphically interpreted. It is noticed that the presence of modi?ed Hartman number helps to reduce the ?uid's temperature but enhances the velocity pro?le. Further an enlargement in the value of thermal time relaxation parameter helps to decrease the temperature distribution.
文摘The present study describes long term PM10 and PM2.5 changes in typical street canyon with particular emphasis on seasonal, diurnal variations in context with meteorological data. In order to understand PM10 pollution sources during 28 April 2007-31 December 2007, chemical composition measurements were done with particular emphasis on heavy metals (As, Cd, Ni, and Pb), crustal material (Ca, Mg, Na, and K) and anions (sulphates, nitrates, chlorides). Meteorological data used for this evolutional analysis were measured close to traffic related stations and several meteorological parameters were analyzed in relation to particulate measurements. Keep in mind that atmospheric aerosols are generally hydroscopic. Relative humidity which plays very important role in rain/snow and humidity impact are analyzed.
基金extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)for funding this work(Grant No.IMSIURPP2023053).
文摘Casson fluid-mediated hybrid nanofluids are more effective at transferring heat than traditional heat transfer fluids in terms of thermal conductivity.Heat exchangers,cooling systems and other thermal management systems are ideal for use with Casson fluids.Precise control of the flow and release of medication is necessary when using Casson fluids in drug delivery systems because of their unique rheological properties.Nanotechnology involves the creation of nanoparticles that are loaded with drugs and distributed in Casson fluid-based carriers for targeted delivery.In this study,to create a hybrid nanofluid,both single-walled carbon nanotubes(SWCNTs)and multi-walled carbon nanotubes(MWCNTs)are dispersed in a Casson fluid with Fourier’s and Fick’s laws assumptions.The Casson fluid is suitable for various engineering and medical applications due to the enhancement of heat transfer and thermal conductivity by the carbon nanotubes.Our objective is to understand how SWCNTs and MWCNTs impact the flow field by studying the flow behavior of the Casson hybrid nanofluid when it is stretched against a Riga plate.The Darcy-Forchheimer model is also used to account for the impact of the porous medium near the stretching plate.Both linear and quadratic drag terms are taken into account in this model to accurately predict the flow behavior of the nanofluid.In addition,the homotopy analysis method is utilized to address the model problem.The outcomes are discussed and deliberated based on drug delivery applications.These findings shed valuable light on the flow characteristics of a Casson hybrid nanofluid comprising SWCNTs and MWCNTs.It is observed that the incorporation of carbon nanotubes makes the nanofluid a promising candidate for medical applications due to its improved heat transfer properties.
文摘This article is based on the impulsively started horizontal Riga plate in two dimensional unsteady Casson fluid flows with rotation. The plate starts abruptly from the rest relative to the rotating fluids moving with uniform acceleration in its plane. Numerical solutions are acquired by using explicit finite difference method and estimated results have been gained for various values of the Rotational parameter, modified Hartmann number, Prandtl number, Radiative parameter, Eckert number, Heat source parameter, Schmidt number, and the Soret number. Both the Compaq visual FORTRAN 6.6a and MATLAB R2015a tools have been used to find the numerical solutions and the graphical presentation. The Skin friction, Nusselt number and Sherwood number have been computed and the effects of some pertinent parameters on various distributions are discussed briefly and presented graphically.
基金funded by King Saud University,Riyadh,Saudi Arabia,through the Ongo-ing Research Funding program—Research Chairs(ORF-RC-2025-0127)funded via Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R443).
文摘The present investigation inspects the unsteady,incompressible MHD-induced flow of a ternary hybrid nanofluid made of SiO_(2)(silicon dioxide),ZnO(zinc oxide),and MWCNT(multi-walled carbon nanotubes)suspended in a water-ethylene glycol base fluid between two perforated squeezing Riga plates.This problem is important because it helps us understand the complicated connections between magnetic fields,nanofluid dynamics,and heat transport,all of which are critical for designing thermal management systems.These findings are especially useful for improving the design of innovative cooling technologies in electronics,energy systems,and healthcare applications.No prior study has been done on the theoretical study of the flow of ternary nanofluid(SiO_(2)+ZnO+MWCNT/Water−EthylGl ycol,(60∶40))past a pierced squeezed Riga plates using the boundary value problem solver 4th-order collocation(BVP4C)numerical approach to date.So,the current work has been carried out to fill this gap,and the core purpose of this study is to explore the aspects that enhance the heat transfer of base fluids(H_(2)O/EG)suspended with three nanomaterials SiO_(2),ZnO,and MWCNT.The Riga plates introduce electromagnetic forcing through an embedded array of magnets and electrodes,generating Lorentz forces to regulate the flow.The squeezing effect introduces dynamic boundary movement,which enhances mixing;however,permeability,due to porosity,replicates the true material limits.Similarity transformations of the Navier-Stokes and energy equations result in a highly nonlinear set of ordinary differential equations that govern momentum and thermal energy transport.The subsequent boundary value problem is solved utilizing the BVP4C numerical approach.The study observes the impact of magnetic parameters,squeezing velocity,solid volume percentages of the three nanoparticles,and porous medium factors on velocity and temperature fields.Results show that magnetic fields reduce the velocity profile by 6.75%due to increased squeezing and medium effects.Tri-hybrid nanofluids notice a 9%rise in temperature with higher thermal radiation.
