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.

The study presents a computational framework based on machine learning for the study of blood nanofluid flow and heat transmission via stenotic arteries. Calculation of the velocity and temperature fields that couple together for the system of blood nanofluid is done using a new form of a hyperbolic tangent sigmoid transfer function (HTSTF). This new HTSTF is applied in combination with the Navier–Stokes equations to solve the nonlinear model using a deep neural network (DNN) made up of two hidden layers with 30 neuron count for those layers, respectively. DNN is trained using Bayesian regularization and the Levenberg–Marquardt back propagation method. A valid set of data is created using bvp4c (two-point boundary value problem), which is broken down into a training (15%), testing (13%) and validation (72%) data set. The new approach has both very good convergence and very good accuracy; the sample's generated mean square error (MSE) values are 10^–12 to 10^–9 and absolute error values are as small as 10^–7. The sample also shows that there is good correspondence between predicted and reference solutions (for both velocity and temperature) based on statistical regression results. Nanoparticles' volume fraction greatly affects blood's rheological behaviour as well as heat transfer through the stenosed portion of a vessel. This new method is far more efficient and accurate than standard numerical methods and has potential applications within cardiovascular (CV) diagnostics, targeted drug delivery (TDL), and thermal therapies using nanoparticles.