Speaker: Dr. Svetlana Tokareva, Los Alamos National Laboratory
Date: Wednesday, January 20, 2021 , 4:00 PM – 5:00 PM
Title:Uncertainty quantification for PDEs on graphs and applications to simulations of gas networks
Abstract: In this talk we will present a new uncertainty quantification (UQ) method that enables accurate uncertainty quantification (UQ) paradigm for energy networks to characterize input/output relationships of energy supply and delivery variability in both time and space.
Our approach is based on semi-intrusive Stochastic Finite Volume (SFV) method to quantify the uncertainties arising due to random model coefficients in our underlying hyperbolic PDE that models the gas flow. The SFV method requires some modifications of the deterministic code which however only involve additional integration of the numerical fluxes over the cells in the stochastic space and are therefore considered mild. This approach preserves the hyperbolicity of the model, and at the same time is more computationally efficient than e.g. Monte Carlo method.
We then extend the SFV method to perform uncertainty quantification on a graph of PDEs and apply the method to gas networks. A crucial ingredient for the numerical modeling of gas pipe networks is an accurate treatment of physical constraints at pipe junctions. These constraints include, for example continuity of pressure and conservation of flow. The numerical fluxes at junctions are obtained by solving a Junction-Generalized Riemann problem.
We demonstrate the results of our SFV approach first for a single pipe and then for a test network of gas pipes.
Bio: Dr. Svetlana Tokareva graduated from Bauman Moscow State technical University (Russia) in 2008 with a diploma in applied mathematics. She has earned her PhD from ETH Zurich (Switzerland) in 2013. The focus of the PhD thesis was in development of novel highly accurate numerical methods for uncertainty quantification in hyperbolic conservation laws. After the graduation from ETH, Dr. Tokareva has spent one year in R&D for industry and joined ASCOMP, an ETH spin-off company working in CFD consultancy and software development. Following that, she was a postdoctoral researcher in the group of Prof. Remi Abgrall at the University of Zurich, where she was involved in several challenging research projects in the field of computational mathematics and scientific computing with applications in industry. In 2018-2019 Dr. Tokareva was a postdoctoral research associate at the Los Alamos National Laboratory in the Applied Mathematics and Plasma Physics Group, where she continues working on novel high order Lagrangian methods for multiphase and multi-material flows as well as applications of machine learning algorithms in computational fluid dynamics. Since November 2019 Dr. Tokareva is a staff scientist at LANL.
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