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Data-Driven Computational Poromechanics Across Length Scales – Professor Steve WaiChing Sun
March 8, 2017 @ 4:00 pm - 5:00 pm
Speaker: Professor Steve WaiChing Sun, Columbia University Department of Civil Engineering and Engineering Mechanics
Time: 4 pm – 5 pm
Henry L. Pierce Laboratory Seminar Series
Faculty Host: Professor Ruben Juanes
Many geomechanical applications, such as geological disposal of nuclear waste and CO2, require reliable predictions of the multiscale thermo-hydromechanical responses of fluid-infiltrating porous media exposed to extreme environments. This presentation highlights a modeling framework designed specific for porous media vulnerable to various form of failures, ranging from brittle fracture, strain localization to cataclastic flow under different temperature, confining pressure and loading rates. In particular, we introduce (1) the specific variational eigen-deformation model to capture the onset and propagation of brittle fracture (cracks) and compaction bands (Mode-I anticrack) and (2) the underlying graph-based data-driven multiscale algorithm that adaptively hybridizes conventional mathematical theories and data-driven knowledge in a directed graph based on the amount of data available for verifications. This hybrid modeling technique is then applied to various applications related to geological systems including calibrating and verifying micropolar models with micro-CT images, predicting coalescence and branching of fluid-driven fractures, modeling reactivation of fault, and the thawing of frozen soil.
Steve WaiChing Sun is an assistant professor in the Department of Civil Engineering and Engineering Mechanics at Columbia University. From 2011 to 2013, he served as a senior member of technical staff at Sandia National Laboratories. Professor Sun works in the fields of theoretical and computational poromechanics with a special emphasis on geomechanical applications. His research includes multiscale modeling porous media, multiscale verification and validation with microCT images, digital rock and granular physics, applications of mathematical tools, such as graph theory, Lie algebra and recently graphbased machine learning for modern engineering problems involving geological materials. He received the Air Force Young Investigator Program Award in 2017, Dresden Fellowship in 2016, US Army Young Investigator Program Award in 2015, and the Caterpillar Best Paper Prize in 2013. He holds BS degree from UC Davis (2005), Master degrees from Stanford (2007) and Princeton (2008) and a PhD degree from Northwestern (2011).
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