Excellence in research: Leveraging Machine Learning for Fundamental Investigation of Anomalous Transport Phenomena of Nano-Fluids and Modulated using External Stimuli

Prairie View A & M University, Prairie View TX

Investigators

Abstract

Ferro-nanofluids are stable colloidal suspensions of magnetic nanoparticles. They have significant potential to enhance industrial applications like thermal energy storage, thermal management, corrosion prevention and smart lubrication. These fluids demonstrate unique thermo-physical behaviors, particularly when exposed to external stimuli like magnetic fields. This project investigates how these external fields influence the fluid properties like thermal conductivity, viscosity, and heat transfer. The research aims to uncover the underlying mechanisms that cause the anomalous transport behaviors observed in ferro-nanofluids. The study will contribute to advancements in energy efficiency and improved performance across multiple engineering domains. Additionally, the project will support education and broadening participation by providing research opportunities and promoting interdisciplinary learning in fields like nanotechnology and computational modeling. This project will employ a one-step in-situ synthesis technique to produce ferro-nanofluids, followed by experiments to measure their material properties under varying conditions like exposure to static and dynamic magnetic fields. Machine learning algorithms will be developed to model the experimental data, allowing for the prediction of how synthesis and operational conditions affect fluid properties. The integration of theoretical modeling with experimental validation will enable the identification of critical parameters that drive the enhanced heat transfer and energy storage capabilities of ferro-nanofluids. Broader impacts include enhanced educational opportunities for students, fostering interdisciplinary skills in thermal-fluids, nanotechnology, and computational modeling, while contributing to next-generation energy storage and management technologies. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.