CAREER: An Integrated Experimental and Computational Framework for Hydro-chemo-mechanics of Geomaterials Across Scales

This Faculty Early Career Development (CAREER) award will support research to develop an integrated experimental and computational framework for modeling of the mechanical behavior of geomaterials and its coupling with chemical processes and transport of fluids. Reliable understanding and prediction of the interaction of fluid flow with geomaterials is essential for managing groundwater supplies, producing geothermal energy, preventing or remediating contamination, disposing of waste products, managing safety and sustainability of geological carbon and hydrogen storage facilities, and assessing stability of slopes and underground caves.

However, due to the complex multiscale and heterogeneous nature of geomaterials and coupled effects of chemical reactions on transport and mechanical properties, the fundamental mechanisms behind hydro-chemo-mechanical (HCM) behavior of geomaterials have remained poorly understood. This research will enable advanced modeling capabilities for HCM behavior of geomaterials via a novel integration of physics-based modeling, artificial intelligence, material characterization, and experiments.

The outcomes of this research will elucidate the fundamental mechanisms contributing to coupling of rock-fluid interactions and mechanical and transport properties. Research objectives will also be tightly integrated with education activities in the form of outreach to under-represented high school and undergraduate students, short courses, workshops and development of broadly accessible tools.

This research aims to discover the fundamental mechanisms governing hydro-chemo-mechanical (HCM) behavior of geomaterials from the pore-scale to the continuum level. For this purpose, a coupled multiscale HCM modeling framework aided by machine learning techniques will be developed and validated by experiments. The overall research objectives of this work include: 1) development of a machine learning enabled integrated multiscale material characterization framework for heterogeneous materials, and its application to reacted and unreacted core rocks; 2) development of a novel multiscale HCM modeling framework and machine learning based scale bridging techniques; and 3) multiscale virtual testing for scientific discovery of fundamental mechanisms that control geochemical and geomechanical coupling and structure-property relationships.

This work will take a significant step towards enabling artificial intelligence assisted tools for geomechanics and unraveling the mechanisms driving the behavior of heterogeneous materials across different scales.

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.