Chemically reactive gold blood Casson nanofluid flow on a variable porous convectively heated stretching sheet with Cattaneo-Christov flux model using machine learning approach

The impact of inter-particle spacing and the radius of gold nanoparticles on nanofluid flow have substantial significance across applications. Optimizing these parameters in biomedical engineering enhances the drug delivery systems, thus controlling the release of medicines and accurately targeting the targeted area. We explored nanofluid flow on a bi-directional elongated plate. The surface of the sheet was characterized with variable porosity with inclined magnetic field effects, which is the main novelty of the work. We focused on how nanoparticle radius and spacing affect the overall flow dynamics. Additionally, we incorporated the Cattaneo-Christov heat and mass flux model effects to discuss the mass and thermal diffusions using some flow conditions. The major equations were translated in dimensionless form and solved with artificial neural networks (ANNs). As outcomes, we uncovered that primary velocity has weakened with extension in stretching ratio and magnetic factors and has been amplified with progression in variable porous factor with absolute error (AE) in the range 10-3 to 10-7. Thermal panels have enlarged with escalation in thermophoresis, magnetic, radiation, and Brownian motion factors with absolute errors AEs in the range $ {10^{ - 3}} $ to $ {10^{ - 7}} $. Concentration panels have escalated with augmentation in the thermophoresis factor and activation energy factor and weakened with the expansion in Schmidt number, chemical reactivity factor, and Brownian motion factor. We conclude that the model's optimal performance has observed at epochs 111,225,194,270,179,220,339, and 221 for different scenarios. For all the scenarios, the gradient values are associated at $ 9.97 \times {10^{ - 8}} $, $ 9.91 \times {10^{ - 9}} $, $ 9.92 \times {10^{ - 8}} $, $ 9.91 \times {10^{ - 8}} $, $ 9.95 \times {10^{ - 8}} $, $ 9.91 \times {10^{ - 8}} $, $ 9.92 \times {10^{ - 8}} $, and $ 9.91 \times {10^{ - 8}} $.