My research focuses on computational modeling of fluid and thermal systems to enhance energy efficiency, reduce emissions, and promote sustainable technologies. I specialize in reactive molecular dynamics (RMD) simulations and machine learning to predict particulate behavior in combustion systems. Key interests include:
Investigating the molecular dynamics of soot formation during combustion, focusing on the evolution of physicochemical properties and particle morphology using advanced RMD simulations.
Applying high-fidelity simulations to model fluid dynamics and thermal systems, with an emphasis on optimizing energy conversion processes.
Developing predictive models to reduce particulate emissions from combustion devices, aiming to enhance combustion efficiency and minimize environmental impact.
Investigating the role of new materials in improving heat transfer, energy storage, and thermal management in industrial and transportation sectors.
Exploring the potential of biofuels and alternative energy sources to reduce emissions and improve the efficiency of energy systems.
Integrating machine learning techniques with computational models to automate the extraction and analysis of key features from molecular dynamics simulations and experimental data.