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The incorporation of three distinct nanoparticles in blood within the context of cubic autocatalysis holds significant potential for enhancing biomedical applications, particularly in targeted drug delivery and therapeutic interventions. The increased reaction rate improves the efficiency of catalytic processes within the bloodstream. This research investigates the thermal transport characteristics of a trihybrid Carreau nanofluid (blood) containing copper (Cu), titanium dioxide (TiO2), and aluminum oxide (Al2O3), nanoparticles in the context of a wedge‐shaped artery under the influence of autocatalytic cubic autocatalysis. Effects of thermal radiation, and heat generation are used for heat transport analysis, heterogeneous‐homogeneous chemical process included for blood concentration, and an inclined magnetic field is imposed for securitization of blood velocity. Also, the generated PDEs from the physical model are handled through similarity transformations and converted into ODEs. Bvp4c, a numerical technique is used to get the solution and then Levenberg‐Marquardt neural network (LM‐NN), a multilayer neural network scheme is used to train and predict the solution for each parameter. In addition, the numerical values of volumetric friction of coefficients enhance the thermal conductivity, and the heat transport rate is increased. The magnetic parameter, radiation and chemical processes enhance the rate of heat transport while the Weissenberg number reduces the velocity profile. [ABSTRACT FROM AUTHOR]

The purpose of the study is to investigate the thermal proficiency of a trihybrid magnetized water‐based cross nanofluid over an inclined shrinking sheet. Cross‐fluid is the best model to investigate the fluid flow at a very high and very low share rate. There are three nanoparticles that are added in based fluid (water) to form the requisite posited ternary hybrid nanofluid. Moreover, heat transport analysis is scrutinized by incorporating the melting conditions. The obtained nonlinear system of partial differential equations (PDEs) from assumed physical assumption is converted into the nonlinear setup of ordinary differential equations (ODEs). These ODEs are passed under the boundary value problem of a fourth‐order (bvp4c) MATLAB program for numerical results. With the help of bvp4c, data are further trained through an artificial neural network and results are predicted. Results are compared with both techniques and found smooth agreement. The obtained numerical results provide valuable insight for optimizing heat transfer processes involving nanoparticle‐enhanced fluid on inclined shrinking sheets. From the results, it is concluded that the inclusion of nanoparticles enhances the viscosity and thermal conductivity of the fluid. High temperatures make rapid heat transfer scenarios. [ABSTRACT FROM AUTHOR]