Approximate Deconvolution Reduced Order Modeling
Xuping Xie, David Wells, Zhu Wang, Traian Iliescu
TL;DR
This paper introduces a novel LES-ROM framework utilizing POD and approximate deconvolution to improve the simulation of complex flows, demonstrated through Burgers equation tests.
Contribution
It develops a new AD-ROM approach that enhances closure modeling in reduced order simulations of realistic fluid flows.
Findings
AD-ROM accurately captures flow dynamics
Improved stability over traditional ROM methods
Effective in simulating Burgers equation with low diffusion
Abstract
This paper proposes a large eddy simulation reduced order model(LES-ROM) framework for the numerical simulation of realistic flows. In this LES-ROM framework, the proper orthogonal decomposition(POD) is used to define the ROM basis and a POD differential filter is used to define the large ROM structures. An approximate deconvolution(AD) approach is used to solve the ROM closure problem and develop a new AD-ROM. This AD-ROM is tested in the numerical simulation of the one-dimensional Burgers equation with a small diffusion coefficient(10^{-3})
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Taxonomy
TopicsModel Reduction and Neural Networks · Probabilistic and Robust Engineering Design · Nuclear Engineering Thermal-Hydraulics