Applied Mathematics Colloquium: Dr. Michael Lee
NASA Langley
Location
Online
Applied Mathematics Colloquium: Dr. Michael Lee – Online Event
Date & Time
September 11, 2020, 2:00 pm – 3:00 pm
Description
Title: A Primer in Fluid Reduced-order Modeling
Abstract: Reduced-order modeling of nonlinear dynamical systems advertises both low computational cost and high simulation fidelity through the identification of a system’s most important characteristics and how they interact. From an analytical perspective, elucidating the coherent structures of a nonlinear system – those which define the attractor – is of fundamental interest. These normally manifest in the form of a modal basis of finite size; the fewer modes in the basis, the more reduced the model. From an applied perspective, the appeal of simulating very high-dimensional systems like turbulent fluid flows in orders of magnitude less time is readily apparent. The challenges of reduced-order modeling are as serious as the benefits, however: limited predictable accuracy, model stability, and application utility. In this talk, a survey of fluid reduced-order modeling approaches will be presented with an emphasis on how to understand their relative strengths and weaknesses. It will be argued that the method used to construct a modal basis is equally important as the method used to construct the model from the system’s governing equations. The spectrum of applications for reliable reduced-order models will also be explored.
Abstract: Reduced-order modeling of nonlinear dynamical systems advertises both low computational cost and high simulation fidelity through the identification of a system’s most important characteristics and how they interact. From an analytical perspective, elucidating the coherent structures of a nonlinear system – those which define the attractor – is of fundamental interest. These normally manifest in the form of a modal basis of finite size; the fewer modes in the basis, the more reduced the model. From an applied perspective, the appeal of simulating very high-dimensional systems like turbulent fluid flows in orders of magnitude less time is readily apparent. The challenges of reduced-order modeling are as serious as the benefits, however: limited predictable accuracy, model stability, and application utility. In this talk, a survey of fluid reduced-order modeling approaches will be presented with an emphasis on how to understand their relative strengths and weaknesses. It will be argued that the method used to construct a modal basis is equally important as the method used to construct the model from the system’s governing equations. The spectrum of applications for reliable reduced-order models will also be explored.
Please login to the event from your UMBC account. If you have to login from your personal gmail account, please send an email to Justin Webster at websterj@umbc.edu to ask for permission to the event.
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