A Methodology for Projection-Based Model Reduction with Black-Box High-Fidelity Models
S. Ashwin Renganathan, Yingjie Liu, and Dimitri N. Mavris

TL;DR
This paper introduces a projection-based model reduction methodology for black-box high-fidelity models, enabling efficient parametric simulations by approximating linear operators directly from discretized equations, demonstrated on CFD problems.
Contribution
The paper proposes a novel approach to perform model reduction on black-box models with known governing equations, using discretization to approximate operators for ROM construction.
Findings
Successfully applied to a nonlinear PDE with exponential non-linearity.
Demonstrated on compressible inviscid flow past an airfoil.
Generated online databases of ROMs for various parameters.
Abstract
This paper presents a methodology that enables projection-based model reduction for black-box high-fidelity models such as commercial CFD codes. The methodology specifically addresses the situation where the high-fidelity model may be a black-box but there is complete knowledge of the governing equations. The main idea is that the linear operator matrix, resulting from the discretization of the linear differential terms is approximated directly using a suitable discretization method such as the Finite Volume Method and requires only the computational grid as input. In this regard, the governing equations are first cast in terms of a set of scalar observables of the state variables, leading to a linear set of equations. By applying the snapshots of the observables to the discrete linear operator, a right hand side vector is obtained, providing the necessary system matrices for the…
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Taxonomy
TopicsModel Reduction and Neural Networks · Probabilistic and Robust Engineering Design · Real-time simulation and control systems
