Thermal transport and irreversibility analysis for radially magnetized Carreau–Yasuda nanofluids flow over polished surfaces

Modern Physics Letters B, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

A major challenge in modern industry is the need for efficient heat transfer fluids, as conventional fluids often do not provide necessary efficiency heating and cooling processes. Nanofluids, seen future of are developed by dispersing nanoparticles into base fluids. Owing to their unique dynamic thermophysical properties, these innovative nanofluids have a broad spectrum applications nanotechnology advanced systems. Based on nanofluids, flow composed graphene suspended lubricant oil with studied here. Flow induced curved stretching sheet. The surface sheet considered be polished uniformly, which hence facilitates velocity slip. novelty this study lies utilizing rheological properties dispersed oil-based fluid, empirically investigated Bakak et al. (2021). Their findings suggest that nanofluid exhibits non-Newtonian behavior, experimental data closely aligning Carreau–Yasuda model. radially varying magnetic field influences generating Lorentz force Ohmic heating. In view experimentally reported results, depicted using Furthermore, configuration influenced thermal radiation, source, viscous dissipation, convective at boundary. Irreversibility analysis carried out propose ways energy optimization. Boundary layer approximations utilized model partial differential equations (PDEs) governing system. By applying non-similarity transformation, original PDEs converted dimensionless nonlinear PDEs. local approach, truncated second order, reducing them ordinary (ODEs) can more easily solved. resulting system tackled through BVP4c algorithm MATLAB. Influences pertinent parameters profile, drag force, isotherms, nanofluid’s temperature, streamlines, entropy generation number, rates, Bejan number analyzed graphs tables. Findings indicate temperature distribution improves higher values Biot Weissenberg number. Additionally, decreases increasing Also, it noted velocity. demonstrates direct relationship profile. magnitude coefficient curvature parameter volume fraction nanoparticles. Heat rises elevated parameter, Eckert source radiation but an increase

Language: Английский

Artificial neural network aided computing for two dimensional magnetohydrodynamic peristaltic movement of nanofluid with heat and mass transfer

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 154, P. 110990 - 110990

Published: May 9, 2025

Language: Английский