TADDEI, Tommaso; XU, Xuejun; ZHANG, Lei

doi:10.48550/arXiv.2310.20267

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TADDEI, Tommaso
Modeling Enablers for Multi-PHysics and InteractionS [MEMPHIS]

XU, Xuejun
Tongji University
Chinese Academy of Sciences [Beijing] [CAS]

ZHANG, Lei
Tongji University

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2023

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We present a component-based model order reduction procedure to efficiently and accurately solve parameterized incompressible flows governed by the Navier-Stokes equations. Our approach leverages a non-overlapping optimization-based domain decomposition technique to determine the control variable that minimizes jumps across the interfaces between sub-domains. To solve the resulting constrained optimization problem, we propose both Gauss-Newton and sequential quadratic programming methods, which effectively transform the constrained problem into an unconstrained formulation. Furthermore, we integrate model order reduction techniques into the optimization framework, to speed up computations. In particular, we incorporate localized training and adaptive enrichment to reduce the burden associated with the training of the local reduced-order models. Numerical results are presented to demonstrate the validity and effectiveness of the overall methodology.< Réduire

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component-based model order reduction

optimization-based domain decomposition

non-overlapping methods

Navier-Stokes equations

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A non-overlapping optimization-based domain decomposition approach to component-based model reduction of incompressible flows

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