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 (COFe2O4) 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.

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.

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.