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Our research interests lie in the fundamental understanding of inherently multiscale and multiphysics energy systems. Such understanding is critical to systematically develop accurate models for prediction, optimization and design of such systems at relevant scales. We are interested in the entire lifecycle of multiscale models, from development (theory-driven; bottom-up) to deployment (application-driven; top-down). We are specifically focused on systems involving coupled (single and multiphase) flow and reactive transport processes in porous media, with applications spanning from geologic (e.g., rocks, vegetation) to engineered porous media (e.g., batteries, membranes). We contribute to the science of multiscale multiphysics systems by (i) developing new methods and technologies to improve current predictive and design capabilities, (ii) cutting down the time from model development to model deployment from years/decades to months/day to promptly address ever changing energy security scenarios, and (iii) by advancing the fundamental science associated with specific application areas. In our research, we broadly and ubiquitously use principles from engineering science and mechanics, applied mathematics, symbolic and numerical methods. We complement our theory development efforts through experimental data and microfluidic experiments. Currently, we are developing new tools in symbolic computing for multiscale models, hybrid algorithms, AI-aided multiscale algorithm development, and coarse-graining methods. Some of the application areas include CO2 sequestration and hydrogen storage, reactive transport and thermal runaway in battery packs, reactive transport in porous and fractured media, CFD-guided design of membrane systems for water purification, single and multi-phase reactive transport in unconventional formations.


Symbolic Scientific Computing for Multiscale Systems

Multiscale Modeling of Battery Systems: Electrochemical Transport and Thermal Runaway

Hybrid models of transport in crowded environments