Model Reduction for DAEs with an Application to Flow Control

TL;DR

This paper discusses the use of interpolatory model reduction techniques to simplify large-scale differential algebraic equation systems in flow control, enabling faster simulations and effective control strategies.

Contribution

It applies interpolatory model reduction to large-scale DAEs in flow control, demonstrating its effectiveness in producing accurate, computationally efficient reduced models.

Findings

Interpolatory reduction yields high-fidelity reduced models for large-scale DAEs.

Reduced models enable faster simulations and control design.

The approach matches the structure of more expensive control methods.

Abstract

Direct numerical simulation of dynamical systems is of fundamental importance in studying a wide range of complex physical phenomena. However, the ever-increasing need for accuracy leads to extremely large-scale dynamical systems whose simulations impose huge computational demands. Model reduction offers one remedy to this problem by producing simpler reduced models that are both easier to analyze and faster to simulate while accurately replicating the original behavior. Interpolatory model reduction methods have emerged as effective candidates for very large-scale problems due to their ability to produce high-fidelity (optimal in some cases) reduced models for linear and bilinear dynamical systems with modest computational cost. In this paper, we will briefly review the interpolation framework for model reduction and describe a well studied flow control problem that requires model reduction of a large scale system of differential algebraic equations. We show that interpolatory model reduction produces a feedback control strategy that matches the structure of much more expensive control design methodologies.