Kamel Guedri
57732235500
Publications - 10
Galerkin finite element analysis of trihybrid nanofluid flow in porous corrugated cavities with thermal radiation and ANN validation
Publication Name: Results in Engineering
Publication Date: 2026-06-01
Volume: 30
Issue: Unknown
Page Range: Unknown
Description:
This work tackles the issue of enhancing heat transmission and minimizing entropy formation in tiny enclosures pertinent to thermal energy storage. It looks at how magnetohydrodynamic (MHD), non-Darcian porous media, a ternary hybrid nanofluid composition (Fe3 O4 –hBN–CuO/water), and triangle corrugation work together in a corrugated rectangular cavity. The goal is to figure out how these things affect convection, entropy formation, and the overall efficiency of the thermodynamic system. Utilizing the Galerkin finite element technique (GFEM), we found numerical solutions to the mathematical models for momentum, energy, and entropy generation. The effects of the porosity parameter, ternary nanoparticle concentration, Hartmann number, Darcy number, and Rayleigh number were carefully studied for the cavities' flat and triangular corrugated walls. Artificial Neural Network (ANN) model was developed and trained to predict the average Nusselt number and total entropy generation with high precision, using fewer computational resources compared to conventional CFD approaches. It is observed that the ANN model is used mostly as an ancillary prediction instrument derived from FEM-generated data, rather than as the principal computational framework. The results show that corrugated shapes improve local heat transfer by increasing the surface area and causing flow disruptions. However, too many corrugations lower the average Nusselt numbers because they cause recirculation. Higher Rayleigh numbers make buoyancy-driven convection stronger, whereas larger magnetic fields make circulation weaker, which makes conduction-dominated transport more likely and lowers entropy generation. The porosity and Darcy number have a big effect on convective intensity and entropy formation. On the other hand, the right number of nanoparticles may boost thermal conductivity without making irreversibility too high. The ANN model showed great prediction ability (MSE≈1.12 × 10⁻⁷), which proved that it works well for quickly testing Multiphysics systems. These results show that integrating ternary nanofluids, controlling porous media, and changing the magnetic field may improve thermal performance in advanced applications, including solar collectors, cooling electronics, and thermal energy storage devices. Combining ANN prediction gives us a solid base for designing and improving next-generation heat management solutions in a way that works well.
Open Access: Yes
Thermal characteristics of magnetic blood-based hexa-hybrid nanofluids in stenotic arteries with heat source/sink by applying Caputo-Fabrizio fractional derivatives
Publication Name: Results in Surfaces and Interfaces
Publication Date: 2026-08-01
Volume: 24
Issue: Unknown
Page Range: Unknown
Description:
The current examination explores the magnetohydrodynamic flow and transport behavior of a Casson-based blood-derived hexa-hybrid nanofluid via a vertically oriented, mildly stenotic artery using a fractional-order framework. The hexa-hybrid nanofluid is formulated by dispersing Au, Cu, ZnO, Ag, MgO and TiO2 nanoparticles into blood, and the flow is considered highly pulsatile. Mathematical modelling is developed from the conservation laws of mass, momentum, and energy, followed by nondimensionalization under the mild-stenosis approximation. To extend the classical model to its fractional form, the Caputo–Fabrizio fractional derivative is incorporated, enabling closed-form analytical expressions for velocity and temperature through combined Laplace and Hankel transforms. The graphical results highlight the influence of key physical factors on velocity, temperature, and entropy production. The inclusion of hexa-hybrid nanoparticles notably enhances the thermal characteristics of blood due to the substantial rise in effective thermal conductivity. The velocity increases with higher Casson parameter values, whereas temperature decreases as the fractional-order parameter intensifies. Furthermore, entropy generation is found to rise with increasing thermodynamic parameters, while the Bejan number correspondingly decreases, reflecting dominant irreversibility effects within the system.
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
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
Superconducting quantum interference device (SQUID) analysis for (α-Fe₂O₃) nanoparticles with tailored physical and magnetic properties
Publication Name: Discover Nano
Publication Date: 2026-12-01
Volume: 21
Issue: 1
Page Range: Unknown
Description:
This study presents the hydrothermal synthesis of hematite (α-Fe₂O₃) nanoparticles with controlled morphological variations, including rhombohedral, irregular, and Nano plate structures. The phase purity and crystallographic structure of the synthesized samples were validated using X-ray diffraction, confirming the successful formation of hematite. Detailed transmission electron microscopy analysis revealed pronounced differences in particle shape and size, emphasizing the effectiveness of the synthesis method in tailoring morphology. Magnetic properties were systematically investigated using a superconducting quantum interference device magnetometer, demonstrating a clear correlation between nanoparticle morphology and magnetic response. The results collectively show that particle morphology plays a substantial role in determining both the structural and magnetic behavior of hematite nanoparticles. These findings contribute to the rational design and engineering of nanomaterials for advanced technological applications.
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
