Molecular Dynamics Study of Nanofluid Applications

Nanofluids are engineered colloidal suspensions of nanoparticles in conventional base fluids, which exhibit unique thermal and transport properties that make them promising candidates for next-generation energy, electronics, biomedical, and environmental applications. However, conventional experimental and continuum-scale studies often fail to capture the atomic- and nanoscale mechanisms underlying their anomalous behavior. Molecular dynamics (MD) simulations bridge this gap by providing atomistic insights into transport phenomena, interfacial interactions, and structural dynamics that govern nanofluid performance. This chapter presents a comprehensive overview of the role of MD in advancing nanofluid research. Beginning with the historical development of MD and its algorithmic foundations, the chapter explores how simulations have evolved into indispensable tools for probing nanoscale physics. Key applications are discussed, including interfacial thermal resistance, nanoparticle dispersion and agglomeration dynamics, slip flow in confined geometries, electrokinetic and magnetic field effects, and phase-change processes such as nucleation and boiling. Special emphasis is placed on the selection and validation of interatomic potentials and force fields, as these fundamentally determine the accuracy of simulated thermophysical properties, stability, and heat transfer mechanisms. The chapter also highlights the integration of MD data into multi-scale frameworks, where atomistic predictions inform continuum models such as computational fluid dynamics (CFD). Strategies for scale bridging, uncertainty quantification, and parameter optimization are reviewed, along with emerging approaches such as machine learning-assisted force fields and hybrid MD-continuum simulations. Challenges—including computational cost, limited timescales, and parameter transferability—are critically examined, alongside opportunities for digital twin development and real-time predictive modeling.