NAMEIlenia Battiato | Assistant Professor of Energy Resources Engineering, Stanford University
TITLE"Upscaling and Automation: Pushing the Boundaries of Multiscale Modeling through Symbolic Computing"
ABSTRACTIn the past decades, there has been a massive investment by the scientific community toward the development of multiscale abstractions, numerical schemes, and related theories for physics-based modeling and simulation of porous media. Such efforts span scientific fields and engineering applications, ranging from energy storage and environmental systems to medical sciences. Yet, the adoption and systematic deployment of multiscale models by practitioners for the purposes of prediction, design, control, and optimization have remained limited. Rigorous upscaling of advective-diffusive-reactive (ADR) systems have enabled the systematic generation of macroscopic differential equations that accurately account for microscopic phenomena. However, the mathematical derivation of such equations at the continuum-scale is tedious, time-consuming, and prone to error, even for syntactically simple ADR systems. In addition, a priori error guarantees and sufficient conditions under which such equations are valid can take months to years of further manual effort to acquire and necessitate a keen understanding of the mathematics involved. The entire process becomes quickly intractable for complex systems with tens or hundreds of equations. We propose to combine upscaling and symbolic computing as a path toward democratization and broad utilization of upscaling methods in real-world applications characterized by complex reactive networks. We present Symbolica , a software that streamlines the upscaling procedure and derivation of applicability conditions to just a few minutes. We demonstrate its ability by reproducing homogenized ADR systems from earlier studies and homogenizing a large ADR system deemed impractical for manual homogenization. Novel upscaling scenarios previously restricted by unnecessarily conservative conditions are discovered and numerical validation of the models derived by Symbolica is provided.
BIOIn 2005 Dr. Battiato obtained a MS (equiv.) in environmental engineering with highest honors from the Politecnico di Milano, Italy. Subsequently she obtained a MS in Engineering Physics in 2008 from the Mechanical and Aerospace Engineering department at University of California at San Diego (UCSD). In 2010 she completed her Ph.D. at UCSD in Engineering Science with spec/computational sciences. She held a postdoctoral position at the Max Planck Institute for Dynamics and Self-Organization in Goettingen, Germany. She was faculty in the Mechanical Engineering Department at Clemson University from 2012 to 2014, and in the Mechanical Engineering department at San Diego State University (SDSU) with a joint appointment at the Computational Science Research Center at SDSU from 2014 to 1016. In 2015, she received the DOE Young Investigator award in Basic Energy Sciences for her work on multiscale models in porous media. She joined the department of Energy Resources Engineering at Stanford University in 2016 where she is currently assistant professor.
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