Wasim Jamshed
57196410581
Publications - 10
AI-neural network modelling of Williamson blood flow in porous medium Soret-Dufour effects with tetra hybrid nanoparticles
Publication Name: International Communications in Heat and Mass Transfer
Publication Date: 2026-02-01
Volume: 171
Issue: Unknown
Page Range: Unknown
Description:
This manuscript delineates a thorough study on the heat and mass transfer phenomenon of the Williamson fluid flow embedded with a tetra-hybrid nanofluid evaluating a wide range of considered and physical effects such as magnetohydrodynamics (MHD), porous medium, radiative heat flux, Joule heating, Soret and Dufour effects, and a Stefan blowing parameter at the boundary, and the rest. A tetra-hybrid nanofluid containing nanofluid gold (Au), silver (Ag), titanium dioxide (TiO₂), and aluminium oxide (Al₂O₃) is used for the improvement of significant thermal and mass transport characteristics. In the back, the demand for efficient thermal systems relates to sets with multiple, integrated transport mechanisms; however, the synergistic transport mechanisms have been largely unexplored, and the coupled hybrid advanced dimensions nanofluids have been unexplored in terms of their combined influences on these parameters. The core target was to examine the active relationships within the physical dynamics parameters while also evaluating the relative increases in the velocity, temperature, and concentration. This paper employs a robust computational approach to the study by solving the governing systems of non-linear ordinary differential equations using an appropriate method of similarity transformation and subsequent numerical techniques. The integration of artificial neural network (ANN) models within this spectrum for the first time, with predictions and optimization set for the outputs, adds a new dimension to this work. The data show that incorporating Soret and Dufour effects, along with the tetra-hybrid nanoparticles, markedly increases the Nusselt and Sherwood numbers, indicating improved heat and mass transfer rates. Furthermore, streamline plots are created to illustrate alterations in the flow structure induced by the Soret and Dufour parameters. This research makes valuable contributions to the development of refined cooling technologies, particularly in energy, chemical, and other process-oriented industries, highlighting the practical utility and innovation potential of the synergistic application of artificial neural networks alongside sophisticated nanofluid models.
Open Access: Yes
Heat transfer control in MHD flow through internally finned vertical duct: A finite volume approach
Publication Name: International Communications in Heat and Mass Transfer
Publication Date: 2026-03-01
Volume: 172
Issue: Unknown
Page Range: Unknown
Description:
The purpose of this investigation is to explore in depth a duct flow that incorporates the Al2 O3 /H2 O nanofluid while it is subjected to an external field impact. The duct is made up of two opposing fins that are joined to the walls that are opposite each other. The temperature may be considered to be uniform at the cross-sectional plane of the duct. Additionally, the heat flow at the border is not variable. The finite volume approach was chosen because it offers a satisfactory balance between computing efficiency and the accuracy of its solutions. Importantly, our results indicate that the slowness of flow that is caused by increased Rayleigh numbers may be efficiently regulated by introducing an external magnetic field that has been carefully measured. The significance of this study demonstrates how magnetic-field modulation can be strategically employed to control thermal-hydraulic behavior in internally finned duct systems. The results provide valuable guidance for designing advanced cooling channels, energy devices, and thermal management systems where enhanced heat transfer and flow stability are required under magnetic field environments. The installation of an external magnetic field of moderate strength resulted in a drop of about 75 % in both the maximum velocity and temperature across the duct. Further, a jump of approximately 66 % in the average Nusselt number has been brought about by 25 % increase in the fin height. Through the use of this study framework, a link between thermal-hydraulic behavior and the application of magnetic force is established. The involvement of the Lorentz force, which offers resistance to the motion of the fluid by operating in a direction that is perpendicular to the direction in which the fluid is flowing, and the magnetic force, is brought about as a consequence of the magnetic forces. Consequently, it is possible to draw the conclusion that a larger Nusselt number is the result of both a higher Rayleigh number and a higher magnetic parameter.
Open Access: Yes
MHD Casson nanofluid flow over a vertical stretchable sheet saturated with a porous medium: a parametric approach for sensitive analysis
Publication Name: Discover Nano
Publication Date: 2026-12-01
Volume: 21
Issue: 1
Page Range: Unknown
Description:
Purpose: After being motivated by the diverse applications of blood rheology, nanotechnology, magnetic field, chemical reaction, solar radiation, and non-Darcy porous media in nano-industrial, medical, and chemical engineering domains. The current computational study aims to numerically examine the influences of velocity slip, internal thermal generation or absorption, chemical reactions, and thermal radiation on magneto-hydrodynamic blood-based nanofluid flow with thermo-Brownian motion through an extending interface within a high-permeability medium. Furthermore, the sensitive analysis of flow features with respect to the independent flow parameters is considered. Design/methodology/approach: Suitable similarity equations are employed to convert the partial differential equations into ordinary differential equations together with their boundary constraints. The NDSolve method in Mathematica 11.0 is employed to numerically analyze the flow model, yielding data for the stream function, velocity profile, frictional force coefficient, temperature profile, concentration profile, local Nusselt number, and Sherwood number across several rheological parameters. Main findings: A boundary slip diminishes momentum transmission from the fluid to the surface; when velocity slip escalates, the velocity profile declines. The intensity of the thermal boundary layer escalates with the thermal Grashof number. The temperature distribution is exacerbated by the influence of radiation. As the Brownian parameter grows, the nanofluid temperature intensifies. The chemical reaction parameter substantially affects the enhancement of both skin friction and the Sherwood number. The Nusselt number is enhanced by increasing the thermal Grashof number. The sensitivity analysis indicates that the chemical reaction and concentration Grashof number significantly influence the improvement of rheological properties. Applications: The results of this work are relevant for regulating film thickness, chemical vapour deposition, drug delivery systems, and process optimization.
Open Access: Yes
Magneto-bioconvective stagnation point flow of a three-dimensional Casson nanofluid over a rotating Riga surface with exponential heat source: Homotopy analysis method
Publication Name: Results in Surfaces and Interfaces
Publication Date: 2026-08-01
Volume: 24
Issue: Unknown
Page Range: Unknown
Description:
The analytical results presented here not only deepen the understanding of coupled magneto-bioconvective transport phenomena but also highlight the possibility of various applications including microelectronic cooling, renewable energy systems, electromagnetic flow control, biomedical transport, microbial fuel cells, and advanced nanofluid-based thermal technologies. The present study investigates a three-dimensional Casson nanofluid flow over a Riga surface at stagnation point under the influence of an applied magnetic field, an exponential heat source, and a rotating frame. This study explores how these combined physical mechanisms influence velocity, temperature, nanoparticle concentration, and microorganism distributions. Also, it assesses whether the Homotopy analysis method (HAM) is capable of yielding precise analytical solutions for such a highly nonlinear transport model. The original nonlinear partial differential equations representing magneto-bioconvective Casson nanofluid flow are first converted to a dimensionless system of ordinary differential equations by using appropriate similarity transformations. The coupled system thus obtained is then solved analytically by the HAM. The solutions achieved through this method are checked against results from the literature to ensure their validity. The finding shows that enhancement in the Casson fluid parameter, magnetic parameter, and mass Grashof number leads to a notable decrease in velocity field as a result of increased flow resistance. In contrast, the higher Hartmann numbers produced by the Riga surface aid fluid motion via electromagnetic forcing. A stronger heat source and larger Biot number cause temperature distribution to rise, whereas thermophoresis lowers nanoparticle concentration. Also, higher activation energy affects concentration transport, but an increase in Peclet number boosts microorganism distribution and bioconvection strength.
Open Access: Yes
Neuro-computing analysis of MHD bioconvective ternary nanofluid flow over a curved stretching surface
Publication Name: Results in Surfaces and Interfaces
Publication Date: 2026-08-01
Volume: 24
Issue: Unknown
Page Range: Unknown
Description:
Objective Magnetically influenced bioconvective flow of ternary nanofluid induced by the expansion of curved surface by incorporating thermophoresis, Brownian motion, chemical species, activation energy and motile microbes to elucidate complex thermal fluid transport phenomena. Method ology: The mathematical model describing flow mechanism was formulated in sense of PDEs (partial differential equations), which are converted in ODEs (ordinary differential equations) by employing similar set of variables. Numerical technique by integrating the shooting method and RK-4 approach is employed to obtain the outcomes of study. Afterwards, neuro-computing model is designed to forecast Nusselt number for mono, hybrid and ternary nanoparticles comparatively. Key findings Findings of the analysis indicate that velocity of fluid intensifies by uplifting curvature factor while thermal profile goes down. Thermophoretic and Brownian diffusion factors cause the temperature of the fluid to rise but lower the associated flux. Higher curvature and activation energy factors elevate concentration distribution, whereas microbe density depreciates versus Peclet and bioconvective Lewis numbers. The MSE values obtained during training (2.79e-08, 7.63e-08, and 1.55e-07) demonstrate the model's robustness. Applications It is concluded that heat and mass transportation phenomenon is superior with the induction of ternary nanoparticles as compared to mono and hybrid, giving valuable insights for the design of improved thermal energy storage and bioconvective transference mechanism in engineering and biomedicine utilizations.
Open Access: Yes
ANN-based modeling and comparative analysis of two-phase dusty fluid flow over a Riga curved surface under modified Fourier law
Publication Name: Results in Surfaces and Interfaces
Publication Date: 2026-08-01
Volume: 24
Issue: Unknown
Page Range: Unknown
Description:
Purpose Magnetohydrodynamic (MHD) flows are widely applied in electromagnetic casting, magnetic drug targeting, plasma confinement and nuclear reactor cooling. The model can be used in microelectronic heat management, electromagnetic cooling, and aircraft thermal systems. The purpose of this study is to analyze the momentum and heat transport characteristics of a two-phase dusty nanofluid flowing over a curved Riga surface under the modified Fourier (Cattaneo–Christov) heat flux model. Methodology The governing equations are converted into ordinary differential equations by similarity transformations after being created with suitable boundary conditions. Using the bvp4c method, numerical results are obtained. An artificial neural network (ANN) based on the Backpropagated Levenberg–Marquardt method and Bayesian Regularisation is created using the collected numerical data in order to envisage flow behaviour under several physical factors. Results The flow is greatly restricted by the suspension of dust particles of uniform size, leading to decrease in heat transfer rate and reduction in velocity, depending on the curvature and magnetic field parameters. While decreasing the velocity of dust-phase, increasing the curvature parameter increases the velocity of fluid-phase. Originality and conclusion The originality of this work lies in integrating a dusty two-phase model, a curved Riga surface, and an ANN-based predictive framework under the modified Fourier law. This work is novel because it combines an ANN approach with a dusty two-phase Riga curved flow and the Modified Fourier heat flux model. The results demonstrate that dust loading and surface curvature strongly influence MHD flow behavior, while LM modeling provides an efficient and accurate alternative to traditional numerical approaches. The findings demonstrate that while Bayesian Regularisation outperforms the Levenberg–Marquardt algorithm in terms of prediction accuracy, the adjusted Hartmann parameter decreases velocity. For complicated nonlinear thermal-fluid transport phenomena, the suggested methodology offers both intelligent and numerical predictive analysis.
Open Access: Yes
Solar thermal radiation effects on magneto-Casson squeezing nanofluid flow for energy-efficient solar tile applications
Publication Name: Applied Thermal Engineering
Publication Date: 2026-08-01
Volume: 302
Issue: Unknown
Page Range: Unknown
Description:
Modern industrial buildings and solar panels are both reliant on thermal efficiency. The Casson nanofluids are a potential working fluid due to their excellent heat transfer properties and adjustable flow behavior. Due to such significant uses, we examine solar-driven magneto-Casson squeezing nanofluid flow over a linearly stretched surface in porous media while incorporating combining effects of Joule heating, internal heat generation as well as thermal radiation. Additionally, Newtonian heating is applied to the bottom surface to improve thermal transmission. Linear thermal stratification is inadequate for accurately capturing heat transport in industrial machineries, because they need large temperature differences. For a more realistic depiction, quadratic thermal stratification is thus used. The working nanofluid contains cobalt ferrite (COFe2 O4 ) nanoparticles that are suspended in sodium alginate, while the Hamilton–Crosser model is used to examine the impact of different nanoparticle shapes on the system. After applying similarity transformation to reduce the governing equations to nonlinear ordinary differential equations, Mathematica's NDSolve is used to resolve the resulting equations numerically. A thorough analysis is conducted of the impacts of important physical factors on skin friction, flow, temperature fields, and Nusselt number. Results indicate that the squeezing constraint increases the flow velocity, whereas the flow velocity is reduced by high magnetic effects. Increasing the Newtonian heating parameter increases the temperature field. However, due to the effect of thermal stratification, this increase is reduced. Diverse morphologies of the particles exhibit varying thermal performance; platelets-like the highest temperature, cylinders exhibit the lowest, and bricks and blades provide modest results. The present results are in close agreement with the results from previous studies, thus confirming the effectiveness of the simulation methodology being used for this work. The findings provide important information for improving contemporary heat transfer technology and creating energy-efficient solar thermal power systems.
Open Access: Yes
Magnetohydrodynamic bioconvective transport of Carreau hybrid nanofluid over nonlinear stretching surface with activation energy and radiation effects in porous media
Publication Name: Discover Nano
Publication Date: 2026-12-01
Volume: 21
Issue: 1
Page Range: Unknown
Description:
The present study investigates magnetohydrodynamic (MHD) bioconvective flow and transport characteristics of a Carreau hybrid nanofluid (HNF) over a nonlinear stretching surface embedded in a porous medium. The hybrid nanoparticle suspension (Fe3 O4 + CoFe2 O4 in water) accounts for thermal radiation, activation energy, Brownian motion, thermophoresis, and gyrotactic microorganisms. By using similarity variables, the original PDEs describing the process are converted and then solved numerically through an adaptive Runge–Kutta-Fehlberg (RKF-45) shooting technique. Findings show that hybrid nanoparticles can be used to increase the heat transfer rate up to 31% and mass transfer approximately by 23%. The magnetic parameter acts to reduce the flow velocity due to Lorentz force, additionally, the radiation parameter and Eckert number upsurge temperature. The Peclet number decreases the distribution of microorganisms while the bioconvective Lewis number promotes their concentration in the region. Dilatant fluids give rise to stronger heat transfer whereas in pseudoplastic fluids their mass diffusion is facilitated more. The results complement well developing thermal management and energy utilization systems.
Open Access: Yes
Physics-informed neural network analysis of kerosene-based penta-hybrid nanofluid flow and heat transfer
Publication Name: Discover Nano
Publication Date: 2026-12-01
Volume: 21
Issue: 1
Page Range: Unknown
Description:
Kerosene oil-based penta-hybrid nanofluids have attracted significant attention because of their improved thermal conductivity, mechanical stability, and potential use in advanced heat transfer systems. In this study, a Physics-Informed Neural Network (PINN) Analysis of Kerosene-Based Penta-Hybrid Nanofluid Flow and Heat Transfer, is performed to understand the flow pattern and heat transfer characteristics of a nanostructured fluid loaded with several types of nanoparticles. This new approach integrates the physical soundness of governing transport equations with the deep learning’s ability to predict, for modeling nanofluid flow and heat transfer phenomena. The initial partial differential equations governing momentum and energy transfers are converted via appropriate similarity transformations into dimensionless ordinary differential equations. These are then used as the key ingredients (embedded alongside boundary conditions) of the loss function of Physics-Informed Neural Network to make the model’s output comply with physical laws. Variations in parameters leading to changes in velocity and temperature distributions are explored, and to check the correctness and trustworthiness results are compared with classical numerical solutions and previously published data. The findings indicate that the PINN approach accurately characterizes the complex flow and heat transfer features of kerosene-based penta-hybrid nanofluids. By incorporating physics-based modeling with deep learning, reliance on extensive numerical data is diminished while excellent predictive capability is preserved. This research draws attention to PINN-based methods as promising and powerful instruments for the study of high-tech nanofluid products and direct engineering of superior heat exchangers, refrigeration systems, and thermal management devices.
Open Access: Yes
Fractional-order thermal analysis of magnetized blood-based octa hybrid nanofluid flow through stenosed arteries with heat generation and thermal radiation
Publication Name: International Communications in Heat and Mass Transfer
Publication Date: 2026-09-01
Volume: 178
Issue: P5
Page Range: Unknown
Description:
This study presents a fractional-order investigation of the thermal performance of magnetized blood-based octa-hybrid nanofluids flowing through stenosed arteries in the presence of heat generation and thermal radiation effects. The mathematical model is formulated within the Caputo fractional derivative framework to analyze the combined influence of arterial constriction, magnetic field strength, thermal radiation, nanoparticle interactions, and memory-dependent fluid behavior on blood flow and heat transfer. By incorporating the Caputo fractional derivative into the governing momentum and energy equations, the model effectively captures the hereditary and memory characteristics of the fluid, which are not adequately represented by classical integer-order models. The transformed governing equations are solved using an appropriate analytical technique to obtain velocity and temperature distributions under various physical conditions. Particular attention is given to the effects of the fractional-order parameter, magnetic parameter, thermal radiation parameter, stenosis severity, nanoparticle volume fraction, and heat generation/absorption parameter on the thermal and flow characteristics of the nanofluid. The results reveal that the inclusion of octa-hybrid nanoparticles substantially enhances the effective thermal conductivity of blood, resulting in improved heat transfer performance. It is further observed that increasing the heat generation parameter significantly elevates the fluid temperature, whereas heat absorption suppresses the thermal field. Additionally, thermal radiation contributes to an increase in temperature distribution within the arterial region, thereby enhancing thermal transport. The magnetic field and fractional-order parameter are also found to play crucial roles in regulating flow resistance and temperature profiles in the stenosed artery. The findings demonstrate that fractional-order modeling provides a more realistic description of complex bio-thermal transport processes in magnetized blood-based octa-hybrid nanofluids. This study offers valuable insights into thermal management in diseased arteries and may contribute to the development of biomedical applications such as targeted drug delivery, hyperthermia treatment, thermal therapy, and advanced cardiovascular nanofluid technologies.
Open Access: Yes
