Component-Based Reduced Order Modeling of Large-Scale Complex Systems

Large-scale engineering systems, such as propulsive engines, ship structures, and wind farms, feature complex, multi-scale interactions between multiple physical phenomena. Characterizing the operation and performance of such systems requires detailed computational models. Even with advances in mode...

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Main Authors: Cheng Huang, Karthik Duraisamy, Charles Merkle
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-08-01
Series:Frontiers in Physics
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fphy.2022.900064/full
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author Cheng Huang
Karthik Duraisamy
Charles Merkle
author_facet Cheng Huang
Karthik Duraisamy
Charles Merkle
author_sort Cheng Huang
collection DOAJ
description Large-scale engineering systems, such as propulsive engines, ship structures, and wind farms, feature complex, multi-scale interactions between multiple physical phenomena. Characterizing the operation and performance of such systems requires detailed computational models. Even with advances in modern computational capabilities, however, high-fidelity (e.g., large eddy) simulations of such a system remain out of reach. In this work, we develop a reduced‐order modeling framework to enable accurate predictions of large-scale systems. We target engineering systems which are difficult to simulate at a high-enough level of fidelity, but are decomposable into different components. These components can be modeled using a combination of strategies, such as reduced-order models (ROM) or reduced-fidelity full-order models (RF-FOM). Component-based training strategies are developed to construct ROMs for each individual component. These ROMs are then integrated to represent the full system. Notably, this approach only requires high-fidelity simulations of a much smaller computational domain. System-level responses are mimicked via external boundary forcing during training. Model reduction is accomplished using model-form preserving least-squares projections with variable transformation (MP-LSVT) (Huang et al., Journal of Computational Physics, 2022, 448: 110742). Predictive capabilities are greatly enhanced by developing adaptive bases which are locally linear in time. The trained ROMs are then coupled and integrated into the framework to model the full large-scale system. We apply the methodology to extremely complex flow physics involving combustion dynamics. With the use of the adaptive basis, the framework is demonstrated to accurately predict local pressure oscillations, time-averaged and RMS fields of target state variables, even with geometric changes.
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spelling doaj.art-3230d61fd1ad406e9558c6b155636f292022-12-22T02:15:34ZengFrontiers Media S.A.Frontiers in Physics2296-424X2022-08-011010.3389/fphy.2022.900064900064Component-Based Reduced Order Modeling of Large-Scale Complex SystemsCheng Huang0Karthik Duraisamy1Charles Merkle2Department of Aerospace Engineering, University of Kansas, Lawrence, KS, United StatesDepartment of Aerospace Engineering, University of Michigan, Ann Arbor, MI, United StatesSchool of Aeronautics and Astronautics, Purdue University, West Lafayette, IN, United StatesLarge-scale engineering systems, such as propulsive engines, ship structures, and wind farms, feature complex, multi-scale interactions between multiple physical phenomena. Characterizing the operation and performance of such systems requires detailed computational models. Even with advances in modern computational capabilities, however, high-fidelity (e.g., large eddy) simulations of such a system remain out of reach. In this work, we develop a reduced‐order modeling framework to enable accurate predictions of large-scale systems. We target engineering systems which are difficult to simulate at a high-enough level of fidelity, but are decomposable into different components. These components can be modeled using a combination of strategies, such as reduced-order models (ROM) or reduced-fidelity full-order models (RF-FOM). Component-based training strategies are developed to construct ROMs for each individual component. These ROMs are then integrated to represent the full system. Notably, this approach only requires high-fidelity simulations of a much smaller computational domain. System-level responses are mimicked via external boundary forcing during training. Model reduction is accomplished using model-form preserving least-squares projections with variable transformation (MP-LSVT) (Huang et al., Journal of Computational Physics, 2022, 448: 110742). Predictive capabilities are greatly enhanced by developing adaptive bases which are locally linear in time. The trained ROMs are then coupled and integrated into the framework to model the full large-scale system. We apply the methodology to extremely complex flow physics involving combustion dynamics. With the use of the adaptive basis, the framework is demonstrated to accurately predict local pressure oscillations, time-averaged and RMS fields of target state variables, even with geometric changes.https://www.frontiersin.org/articles/10.3389/fphy.2022.900064/fullreduced order modelingdomain decompositionmodel reductionturbulent reacting flowsadaptive basis
spellingShingle Cheng Huang
Karthik Duraisamy
Charles Merkle
Component-Based Reduced Order Modeling of Large-Scale Complex Systems
Frontiers in Physics
reduced order modeling
domain decomposition
model reduction
turbulent reacting flows
adaptive basis
title Component-Based Reduced Order Modeling of Large-Scale Complex Systems
title_full Component-Based Reduced Order Modeling of Large-Scale Complex Systems
title_fullStr Component-Based Reduced Order Modeling of Large-Scale Complex Systems
title_full_unstemmed Component-Based Reduced Order Modeling of Large-Scale Complex Systems
title_short Component-Based Reduced Order Modeling of Large-Scale Complex Systems
title_sort component based reduced order modeling of large scale complex systems
topic reduced order modeling
domain decomposition
model reduction
turbulent reacting flows
adaptive basis
url https://www.frontiersin.org/articles/10.3389/fphy.2022.900064/full
work_keys_str_mv AT chenghuang componentbasedreducedordermodelingoflargescalecomplexsystems
AT karthikduraisamy componentbasedreducedordermodelingoflargescalecomplexsystems
AT charlesmerkle componentbasedreducedordermodelingoflargescalecomplexsystems