Machine learning simulation for radiation effects on MoS₂-Al₂O₃ hybrid nanoliquid with local thermal non-equilibrium conditions.

The current study examines the Soret-Dufour influence on a MoS₂-Al₂O₃ hybrid nanomaterial with LTNECs, using a novel intelligent numerical computing paradigm. The current study uses a simple mathematical model to investigate the features of heat transmission in the absence of LTECs. It can help forecast and optimize medication delivery since temperature and concentration gradients can better direct nanoparticles in blood. The model also aids in understanding heat and mass transmission in stenosed arteries, which is critical for designing medical remedies like hyperthermia therapy and preventing arterial blockages. It also sheds light on biomedical device design, such as stents or targeted nanofluid-based cooling systems, by accurately simulating blood flow under complex temperature and concentration scenarios. The ODEs are then numerically solved using MATLAB's built-in bvp4c solver. Comprehensive error analysis, histogram evaluations, regression metrics, and mean squared error (MSE) figures of merit were routinely employed to address fluid model variants, allowing us to assess the efficacy of the newly given ANN-BLMA methodology. As the interphase heat transfer parameter increases, the solid phase thermal profile falls while the liquid phase thermal profile rises.