Scalable physics-guided data-driven component model reduction for steady Navier-Stokes flow
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910674361253888 |
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| author | Chung, Seung Whan Choi, Youngsoo Roy, Pratanu Roy, Thomas Lin, Tiras Y. Nguyen, Du T. Hahn, Christopher Duoss, Eric B. Baker, Sarah E. |
| author_facet | Chung, Seung Whan Choi, Youngsoo Roy, Pratanu Roy, Thomas Lin, Tiras Y. Nguyen, Du T. Hahn, Christopher Duoss, Eric B. Baker, Sarah E. |
| contents | Computational physics simulation can be a powerful tool to accelerate industry deployment of new scientific technologies. However, it must address the challenge of computationally tractable, moderately accurate prediction at large industry scales, and training a model without data at such large scales. A recently proposed component reduced order modeling (CROM) tackles this challenge by combining reduced order modeling (ROM) with discontinuous Galerkin domain decomposition (DG-DD). While it can build a component ROM at small scales that can be assembled into a large scale system, its application is limited to linear physics equations. In this work, we extend CROM to nonlinear steady Navier-Stokes flow equation. Nonlinear advection term is evaluated via tensorial approach or empirical quadrature procedure. Application to flow past an array of objects at moderate Reynolds number demonstrates $\sim23.7$ times faster solutions with a relative error of $\sim 2.3\%$, even at scales $256$ times larger than the original problem. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_21583 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable physics-guided data-driven component model reduction for steady Navier-Stokes flow Chung, Seung Whan Choi, Youngsoo Roy, Pratanu Roy, Thomas Lin, Tiras Y. Nguyen, Du T. Hahn, Christopher Duoss, Eric B. Baker, Sarah E. Numerical Analysis Computational Physics Fluid Dynamics Computational physics simulation can be a powerful tool to accelerate industry deployment of new scientific technologies. However, it must address the challenge of computationally tractable, moderately accurate prediction at large industry scales, and training a model without data at such large scales. A recently proposed component reduced order modeling (CROM) tackles this challenge by combining reduced order modeling (ROM) with discontinuous Galerkin domain decomposition (DG-DD). While it can build a component ROM at small scales that can be assembled into a large scale system, its application is limited to linear physics equations. In this work, we extend CROM to nonlinear steady Navier-Stokes flow equation. Nonlinear advection term is evaluated via tensorial approach or empirical quadrature procedure. Application to flow past an array of objects at moderate Reynolds number demonstrates $\sim23.7$ times faster solutions with a relative error of $\sim 2.3\%$, even at scales $256$ times larger than the original problem. |
| title | Scalable physics-guided data-driven component model reduction for steady Navier-Stokes flow |
| topic | Numerical Analysis Computational Physics Fluid Dynamics |
| url | https://arxiv.org/abs/2410.21583 |