Rahamat Ellahi

Purpose: The study aims to investigate the effects of multi-slips on the peristaltic pumping of a tangent hyperbolic nanofluid through an inclined magnetic field, thermal radiation and double-diffusive convection, with emphasis on thermal and mass conservation characteristics. Design/methodology/approach: A mathematical model describing the nonlinear flow is formulated by incorporating magnetohydrodynamic effects, slip boundary conditions, thermal radiation and double-diffusive convection. The governing flow equations are first reduced to a system of nonlinear differential equations and then solved numerically using the NDSolve function in Mathematica. Interpolating functions are employed to get velocity, temperature, nanoparticle concentration and solutal profiles, while the Nusselt and Sherwood numbers are computed to assess heat and mass exchange rates. Findings: The analysis reveals that the flow exhibits velocity reversal under variation of the Prandtl number, solutal Grashof number and thermal radiation, whereas the velocity slip factor speed up the flow near the boundaries. The fluid temperature decreases by increasing the Weissenberg number and thermal radiation but increases for higher values of thermal slip parameter. Furthermore, it is observed that the buoyancy and viscous effects significantly enlarge the trapped bolus which indicates enhanced fluid entrapment. The calculated Nusselt and Sherwood numbers demonstrate the sensitivity of thermal and mass transport to the governing physical parameters. For the validation and convergence, a comparison with previous results is made as a limiting case of present study and found in good agreement. Originality/value: This study presents a novel analysis of tangent hyperbolic nanofluid peristaltic flow by incorporating slip effects, oblique magnetic field, thermal radiation and double diffusion.

Purpose: The current study aims to investigate the effects of volumetric energy generation, magnetic fields and Ellis Ternary hybrid (silver, titanium oxide and graphene oxide in sodium alginate) nanofluid rheology over a solid sphere. The model consists of induced exothermic reactions, volumetric energy generation, radiative surface heat flux and convective boundary conditions. Design/methodology/approach: The strongly coupled nonlinear partial differential equations are nondimensionalized with the help of appropriate similarity variables. The formulated nonlinear differential equations are solved using the spectral Chebyshev collocation methods. The skin friction and Nusselt number are compared with the literature for various values of the mixed convection parameter, and agreement is observed. Findings: The key findings reveal that the induced exothermic reaction strength decelerates all the fluid fields, except the Nusselt number, while the reaction rate enhances the fluid velocity, temperature, skin friction, Nusselt number and Sherwood number by boosting the buoyancy forces, de-escalating boundary layers and steepening wall gradients. The Biot number accelerates the fluid profiles but decelerates the concentration. The fluid temperature and skin friction also drop with the internal heat generation parameter, while the velocity, nanoparticle concentration, Nusselt and Sherwood numbers improve, indicating that flow acceleration-driven convective cooling has the tendency to dominate over volumetric heating. Originality/value: The ternary hybrid nanofluid system demonstrated superior heat and mass transfer performance compared to mono and binary nanoparticle systems, as shown by broader intensity ranges and steeper gradients in contour and isotherm plots. This enhancement is due to the synergistic improvement of thermophysical properties, indicated by higher Nusselt and Sherwood numbers.

Purpose: This study aims to investigate the nonlinear coupled boundary-layer flow of a magnetohydrodynamic (MHD) non-Newtonian Eyring–Powell nanofluid over a porous cylinder using Physics-Informed Neural Networks (PINNs). Design/methodology/approach: The governing nonlinear partial differential equations associated with momentum and heat transfer are transformed into a non-dimensionless form using appropriate transformations. The proposed PINNs framework incorporates governing physical laws directly into the learning process to achieve accurate and computationally efficient predictions of nonlinear heat transfer behavior. A deep PINN framework is then constructed in TensorFlow to solve the resulting equations while satisfying the imposed boundary conditions. The neural architecture consists of eight hidden layers with 156 neurons in each layer and is trained using a learning rate. The predictive performance of the developed PINN model is validated against the local non-similarity (LNS) method through comparisons. Findings: The proposed PINN model demonstrates strong predictive accuracy and successfully captures the nonlinear transport behavior of MHD Eyring–Powell nanofluid flow. The numerical results exhibit excellent agreement with those obtained from the LNS method and established theoretical trends. The trained network accurately reproduces the thermal boundary conditions and heat transfer characteristics governed by the Prandtl number. Moreover, this study reveals that increasing the Eyring–Powell fluid material parameter significantly enhances the boundary layer thickness. This work also examines the effectiveness of PINNs in predicting heat transport characteristics and boundary layer behavior in comparison with the conventional LNS and finite difference method. This study confirming the robustness of the PINN framework as a reliable alternative for solving complex non-Newtonian transport phenomena in fluid flow and thermal engineering applications. Originality/value: PINN-based modelling of MHD Eyring–Powell non-Newtonian boundary layer flow with heat transfer over horizontal cylindrical surfaces is still scarce in the literature.

This study develops a novel mathematical model to examine blood flow dynamics in a catheterized, inclined artery with overlapping stenoses, accounting for mild stenotic conditions. The analysis incorporates the combined influences of body acceleration, slip velocity, and viscous dissipation, while heat transfer is modeled via the Cattaneo-Christov framework to capture non-Fourier thermal effects. The coupled momentum and energy equations are solved analytically using a perturbation approach, yielding the expressions for wall shear stress, flow rate, velocity, and impedance. Parametric analysis reveals that higher body acceleration, slip velocity, and arterial inclination enhance both velocity and flow rate, whereas larger catheter radii reduce velocity. Wall shear stress intensifies in converging arterial segments due to elevated forces on the vessel wall. These results provide valuable insights into the coupled hemodynamic-thermal behavior of catheterized stenotic arteries, supporting improved clinical strategies for managing pathological arterial conditions and optimizing interventional procedures.

The goal of this study is to investigate the magnetohydrodynamics boundary layer flow of non-Newtonian Casson hybrid nanofluid past a horizontal circular cylinder in a porous medium. The effects of viscous and ohmic dissipation are also taken into account. Blood is used as a base fluid, and aluminum oxide (Al₂O₃) and copper (Cu) nanoparticles are used to make it hybrid nanofluid. The local nonsimilarity method is employed to obtain dimensionless nonsimilar partial differential equations. A supervised learning approach based on artificial neural networks (ANNs) is used to compute approximate solutions. The accuracy and reassurance of the estimated solutions from ANNs are assessed through the correlation index and residual evaluation. The mean square error values is evaluated for different situations. The results indicate that nanoparticles significantly enhance both fluid flow and heat transfer primarily due to increased thermal conductivity and alterations in flow characteristics. The machine learning−based ANNs surpass traditional computational techniques due to their superior accuracy and efficiency and their ability to handle the complex data. Because neural networks can learn patterns and relationships from data without explicit programming, they are adaptable to a wide range of tasks.

The reflection of harmonic waves, like sound or light, has diverse applications, including sonar for object location, understanding seismic waves in geophysics, and even in the human eye's ability to see. The reflection of plane waves with constant material properties are available in existing literature but a very little attention has been given to the temperature-dependent modulus of elasticity. A novel model is proposed to study the propagation of harmonic plane waves through a nonlocal micropolar medium which temperature dependent material properties. The influence of nonlocality and rotation effects are also taken into account. The precise formulations of the field quantities are presented and examined using the normal mode approach. Three phase lag (TPL) theory is applied to model and solve the governing equations. The effects of rotation, temperature-dependent constants, and the nonlocality parameter on the different physical quantities have been examined and displayed graphically. Energy ratios are also computed by using the amplitude ratios. It is concluded that in a nonlocal, rotating, micropolar medium, reflection of harmonic waves provides four coupled quasi-waves namely; quasitransverse; quasilongitudinal; quasimicrorotational; and quasithermal with different speeds and the energy ratios and reflection coefficients are affected by nonlocal parameters, rotation and micropolarity.