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ACMD Seminar: Methods for Prediction and Control of Complex Systems

Evelyn Lunasin

Associate Professor, US Naval Academy


Abstract:  In the first part of the talk I will discuss some of the basic theoretical and practical properties of a turbulence model known as Lagrangian-Averaged Navier-Stokes nist-equations, also called Navier-Stokes-\alpha model.  In the process, I’ll identify core issues that arise when one uses a PDE model to characterize a complex flow.   Similarly, there are common issues that arise when one uses a purely data driven modeling strategy, with no governing nist-equations,  to characterize multi-scale highly nonlinear complex systems.   This motivates the need to properly merge the model nist-equations derived from physical laws with the available observational measurements.  I will introduce a continuous data assimilation (downscaling) algorithm for the two-dimensional Navier-Stokes nist-equations employing coarse mesh measurements of only one component of the velocity field. This algorithm can be implemented with a variety of finitely many observables: low Fourier modes, nodal values, finite volume averages, or finite elements.   Using a similar type of data assimilation algorithm one can recover  the exact full reference solution (i.e. velocity and temperature) of the 3D Planetary Geostrophic model, at an exponential rate in time, by employing  coarse spatial mesh observations of the temperature alone. This provides a rigorous justification to an earlier conjecture of Charney which states that temperature history of the atmosphere, for certain simple atmospheric models, determines all other state variables of the system.


Dr. Evelyn Lunasin is an Associate Professor at the U.S. Naval Academy.  She obtained her Ph.D in Mathematics at the University of California, Irvine under the supervision of Prof. Edriss S. Titi.    The research of Dr. Lunasin concerns the analytical and numerical analysis of nonlinear PDEs, such as, certain sub-grid scale turbulence models and other related models for multi-scale systems. Her research extends to many interesting applications such as optimal stirring strategies for passive scalars and mixing, data assimilation, which is of practical interest to weather and climate prediction, image reconstruction, and nonlinear control theory. Her background in the mathematical theory of fluid dynamics and as well as in scientific computing has opened many doors for collaboration with researchers in numerous fields working in a wide range of applications in nonlinear science and engineering. 

Created March 26, 2019