Abstract Detail
Santoshi Misra
St. Ann's College for women, India
Abstract
Nanofluids, engineered by dispersing nanoparticles in base fluids, have gained significant attention due to their enhanced thermal properties. However, optimizing their heat transfer efficiency requires precise modeling of governing equations involving fluid dynamics, heat conduction, and nanoparticle interactions. This study integrates artificial intelligence (AI) with mathematical modeling to develop predictive frameworks for nanofluid behavior in heat transfer applications. Machine learning algorithms, trained on experimental and simulated data, enhance traditional models by accurately predicting thermal conductivity, viscosity, and convective heat transfer coefficients under varying conditions. AI-assisted optimization techniques further refine nanoparticle concentration, shape, and flow parameters to maximize efficiency. The proposed framework bridges theoretical and experimental gaps, providing a robust tool for designing next-generation thermal management systems in energy, electronics, and biomedical applications.