Hydromechanics of porous media 

Flow, transport, and deformation in (soft) porous media play a major role in numerous phenomena in biology, medicine, and the earth sciences, as well as various industrial and manufacturing processes. Methane venting, nutrient transport in cells, biofilms, and soft tissues, drug delivery, moisture absorption and dewatering of suspensions are among many occasions that involve the two-way coupling between solid and fluid phases. We use numerical and experimental approaches to study flow, transport, and deformation in (soft) porous media and shed new light on hydro-mechanical processes that take place in natural and synthetic porous materials.

Machine Learning and deep learning

Machine learning techniques can significantly augment human intuition and minimize human bias to help identify signals of importance to predict failure, offering powerful path to extract information rapidly from complex datasets. It is posited that machine learning improves abilities to listen for fluid leakage paths, characterize fault friction, quantify stress changes and predict deformation in geo-materials helping to understand natural and human-induced catastrophic events.

Cohesive/cemented geo-materials

Natural geo-material particles that are involved in catastrophic phenomena such as earthquakes, landslides and avalanches are often cohesive, cemented, and wet, which makes the characterization of their rheology complex. Novel experimental and computational methods that aim at addressing such challenging problems can improve our understanding from the micro-mechanics of cohesive/cemented geo-materials leading to a better prediction of their rheological behavior.

Friction and granular flow dynamics

Understanding the frictional behavior of granular materials under shear or generalized stress conditions is the key element for many open questions about the natural phenomena that involve geo-materials. Numerical simulations can shed light on the micro-mechanics of granular dynamics’ change i.e. from stick-slip to slow slip and stable sliding, providing grain-scale information concerning the evolution of stress, precursory activities and granular networks.

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