Hongkyu Yoon, Sandia National Laboratories
Waiching Sun, Columbia University
SeonHong Na, McMaster University
Sanghyun Lee, Florida State University
Fluid flow and coupled mechanical response in fractured and porous media are fundamental to predicting coupled multiphysics processes in geomaterials such as soil, rock and concrete. Recent advances in experimental methods and multi-scale imaging capabilities have revolutionized our ability to quantitatively characterize geomaterials, which allows us to reach ever-increasing spatial resolution across scales. Digital rocks reconstructed from multiscale images (e.g., microCT images) and theoretical/stochastic generations are now routinely used to characterize hydrological and mechanical properties across scales. Additive manufacturing (AM) is a fast-growing manufacturing technique that produces custom parts or whole products by printing materials by layers only where it is needed. The properties of geomaterials are often non-uniform and heterogeneous, requiring multiple samples to unravel competing contributions to physical and chemical behavior from compositional, textural and structural components. These properties are also influenced by other environmental conditions. As a result, predicting fluid physics and the mechanical response of geomaterials often requires knowledge on how various processes interact with each other across length and time scales.
This mini-symposium is intended to provide a forum for researchers to present contributions on recent advances in additive manufacturing for geomaterials, computational fluid physics and geomechanics for multiphysics and multiscale processes for energy systems. Topics within the scope of interests include, but are not limited to, the following: (1) development and validation of additive manufacturing processes for surrogate granular and fractured materials, (2) development and validation of constitutive models for coupled processes, (3) theoretical and computational development and applications for fluid physics and mechanical problems, (4) uncertainty quantification of computational simulations, and (5) machine learning/deep learning applications for Multiphysics problems with granular and fractured materials.