Dondu Harish Babu

This study analyzes gas dynamics and heat transfer behind shock waves, incorporating complex parameters, making it essential for improving the performance and stability of systems requiring precise control over shock waves, thermal management, and non-ideal gas behavior. This explores the impact of shock wave intensity, slippage, dissipation, van der Waals constants, the Prandtl number, the Knudsen number, the Brinkman number, and the magnetic field on the flow's dynamic and thermal behavior, including frictional forces and heat transfer characteristics, while considering the temperature ratio between the flow and the wall. Self-similar transformation variables were employed to transform the governing equations into ordinary differential equations, which were subsequently solved using MATLAB's boundary value problem (BVP4c) solver and cross-verified through artificial neural networks (ANN). The study revealed that increasing the Knudsen number enhances the velocity gradient and broadens the boundary layer, while a stronger magnetic field suppresses flow velocity and increases temperature uniformity. Higher Eckert numbers result in greater viscous dissipation and temperature rise near the wall, while van der Waals forces significantly reduce skin friction and heat transfer. The ANN model achieved over 95% prediction accuracy, and sensitivity analysis indicated that a 10% increase in the Knudsen number led to a 15% rise in the velocity gradient, with variations in the magnetic field causing a 20% change in temperature profiles. This model is applicable in microfluidics, magnetohydrodynamics, thermal management, and high-speed fluid dynamics, where precise control of friction and heat transfer is crucial.

Shock wave propagation in gases through turbulent flow has wide-reaching implications for both theoretical research and practical applications, including aerospace engineering, propulsion systems, and industrial gas processes. The study of normal shock propagation in turbulent flow over non-ideal gas investigates the changes in pressure, density, and flow velocity across the shock wave. The Mach number is derived for the system and explored across various gas molecule quantities and turbulence intensities. This study analytically investigated the normal shock wave propagation in turbulent flow of adiabatic gases with modified Rankine–Hugoniot conditions. Artificial neural network (ANN) techniques are used to estimate the solutions for shock strength and Mach number training validation phases of back-propagated neural networks with the Levenberg–Marquardt algorithm. The results reveal that pressure ratio with density ratio increase for higher values of increase in the turbulence level as well as intermolecular forces. A reverse trend is observed in velocity coefficient after shock in the presence of adiabatic gas. The regression coefficient values obtained using the network model ranged from 0.999 99 to 1, indicating an almost perfect correlation. These findings demonstrate that the ANN can predict the Mach number with high accuracy.

The nanoliquid flows place a significant role in modern research due to the vast number of applications in the Food industry, manufacturing, thermal management, enhanced oil recovery, biomedical applications, and so on. This article presents the analysis of entropy generation on convective nanofluid flow in a three‐dimensional nonlinear stretching sheet with slip effects. Brownian motion and thermophoresis impacts are adopted by the nanoparticle concentration and temperature profiles. The PDEs are remodeled into ODEs by employing the appropriative simulated alternations, and the ODEs are solved by utilizing the R–K–F scheme along with the shooting technique. The main contributions of physical stream factors on the nanofluid concentration, heat, and speed profiles are displayed and explored via plots. Moreover, the rate of heat transfer and surface drag force are examined through tabular structures. In view of this, the nanofluids have more heat transfer capability and improve thermal properties. The slip factors are used in the boundary conditions of the velocity, and the limit conditions are utilized for the hotness and speed of the nanoparticles. The rate of heat transfer in a non‐linear stretching sheet is 1%–2% more than linear stretching sheet for varying values of Prandtl number Pr and Magnetic field M.

A mathematical model for hybrid nanofluid is proposed to study the influence of oblique magnetic field and thermal radiation on an exponentially elongated sheet. A comparison of the thermal characteristics of the hybrid nanofluid and the mono nanofluids (Al2O3 /water and TiO2/water) is made. The governing flow equations are transformed into a system of ODEs with the assistance of similarity variables and are then computationally addressed using bvp4c.The graphs are displayed for velocity, heat measure, and reduced frictional coefficients for selected flow parameters. Hybrid nanofluid has 1–4% growth in the rate of heat transfer when compared to mono nanofluid while it is 1–4.5% in comparison to viscous fluid for increasing radiation parameter. The outcomes of this work revealed that the heat transfer as a consequence of the dispersion of dual nanomaterials is more promising than the mono nanofluid. To accomplish very effective cooling/heating in industrial and engineering applications, hybrid nanofluids can substitute mono nanofluids.