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Title: Memory embedded non-intrusive reduced order modeling of non-ergodic flows

Abstract

Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two, but the novel nonintrusive approach we present in this study is an attempt at taking all three into account effectively compared to their traditional counterparts. Based on dimensionality reduction using proper orthogonal decomposition (POD), we introduce a long short-term memory neural network architecture together with a principal interval decomposition (PID) framework as an enabler to account for localized modal deformation. As an effective partitioning tool for breaking the Kolmogorov barrier, our PID framework, therefore, can be considered a key element in the accurate reduced order modeling of convective flows. Our applications for convection-dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations demonstrate that the proposed approach yields significantly more accurate predictions than the POD-Galerkin method and could be a key enabler toward near real-time predictions of unsteady flows.

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2];  [3]
  1. Oklahoma State Univ., Stillwater, OK (United States)
  2. Norwegian Univ. of Science and Technology, Trondheim (Norway)
  3. Florida State Univ., Tallahassee, FL (United States)
Publication Date:
Research Org.:
Oklahoma State Univ., Stillwater, OK (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1593557
Alternate Identifier(s):
OSTI ID: 1580167
Grant/Contract Number:  
SC0019290
Resource Type:
Accepted Manuscript
Journal Name:
Physics of Fluids
Additional Journal Information:
Journal Volume: 31; Journal Issue: 12; Journal ID: ISSN 1070-6631
Publisher:
American Institute of Physics (AIP)
Country of Publication:
United States
Language:
English

Citation Formats

Ahmed, Shady E., Rahman, Sk. Mashfiqur, San, Omer, Rasheed, Adil, and Navon, Ionel M. Memory embedded non-intrusive reduced order modeling of non-ergodic flows. United States: N. p., 2019. Web. doi:10.1063/1.5128374.
Ahmed, Shady E., Rahman, Sk. Mashfiqur, San, Omer, Rasheed, Adil, & Navon, Ionel M. Memory embedded non-intrusive reduced order modeling of non-ergodic flows. United States. doi:10.1063/1.5128374.
Ahmed, Shady E., Rahman, Sk. Mashfiqur, San, Omer, Rasheed, Adil, and Navon, Ionel M. Mon . "Memory embedded non-intrusive reduced order modeling of non-ergodic flows". United States. doi:10.1063/1.5128374.
@article{osti_1593557,
title = {Memory embedded non-intrusive reduced order modeling of non-ergodic flows},
author = {Ahmed, Shady E. and Rahman, Sk. Mashfiqur and San, Omer and Rasheed, Adil and Navon, Ionel M.},
abstractNote = {Generating a digital twin of any complex system requires modeling and computational approaches that are efficient, accurate, and modular. Traditional reduced order modeling techniques are targeted at only the first two, but the novel nonintrusive approach we present in this study is an attempt at taking all three into account effectively compared to their traditional counterparts. Based on dimensionality reduction using proper orthogonal decomposition (POD), we introduce a long short-term memory neural network architecture together with a principal interval decomposition (PID) framework as an enabler to account for localized modal deformation. As an effective partitioning tool for breaking the Kolmogorov barrier, our PID framework, therefore, can be considered a key element in the accurate reduced order modeling of convective flows. Our applications for convection-dominated systems governed by Burgers, Navier-Stokes, and Boussinesq equations demonstrate that the proposed approach yields significantly more accurate predictions than the POD-Galerkin method and could be a key enabler toward near real-time predictions of unsteady flows.},
doi = {10.1063/1.5128374},
journal = {Physics of Fluids},
number = 12,
volume = 31,
place = {United States},
year = {2019},
month = {12}
}

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