Artificial intelligence computing of blood based nanofluid transport mechanism under thermal radiation influence.

Bionanofluids along a three-dimensional wedge play a significant role in biomedical and engineering applications, including targeted drug delivery and thermal therapy, because they have improved thermal characteristics. The wedge geometry causes sophisticated flow dynamics, enhancing mixing and efficiency in nanofluid based systems, which is important for optimizing medical and industrial process performance. An investigation of bionanofluids along a 3D wedge facilitates the understanding of boundary layer characteristics, heat transfer processes, and nanoparticle distribution, contributing to improved biofluidic device design. Hence, the problem is formulated to analyze the bionanofluid flow through 3D wedge. The basic laws, transformations and supporting bionanofluid characteristics used in the formation leads to nonlinear model. The influence of parameters is than analyzed through ANN (artificial neural network) based LMBPS (Levenberg Marquardt backpropagation scheme) using 15-neurons. The computational output for Nusselt number shown that the depreciation for [Formula: see text] and [Formula: see text]. The velocities [Formula: see text] and [Formula: see text] enhanced for [Formula: see text] and [Formula: see text]. Further, improved Nusselt number computed for [Formula: see text] and [Formula: see text] from [Formula: see text] to [Formula: see text], [Formula: see text] to [Formula: see text] and [Formula: see text] to [Formula: see text]. The results authenticity achieved through FFs (functions fitting), HGE (histogram error), and regressions and found better alignment with test, training, targets and best. The benchmark solutions for future replication of the stud also provided. Clinical trial Not applicable.