Adjacency-based, Non-intrusive Reduced-order Modeling for Fluid-Structure Interactions
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arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866929300843790336 |
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| author | Gkimisis, Leonidas Richter, Thomas Benner, Peter |
| author_facet | Gkimisis, Leonidas Richter, Thomas Benner, Peter |
| contents | Non-intrusive model reduction is a promising solution to system dynamics prediction, especially in cases where data are collected from experimental campaigns or proprietary software simulations. In this work, we present a method for non-intrusive model reduction applied to Fluid-Structure Interaction (FSI) problems. The approach is based on the a priori known sparsity of the full-order system operators, which is dictated by grid adjacency information. In order to enforce this type of sparsity, we solve a local, regularized least-squares problem for each degree of freedom on a grid, considering only the training data from adjacent degrees of freedom, thus making computation and storage of the inferred full-order operators feasible. After constructing the non-intrusive, sparse full-order model, Proper Orthogonal Decomposition (POD) is used for its projection to a reduced dimension subspace and thus the construction of a reduced-order model (ROM). The methodology is applied to the challenging Hron-Turek benchmark FSI3, for Re = 200. A physics-informed, non-intrusive ROM is constructed to predict the two-way coupled dynamics of a solid with a deformable, slender tail, subject to an incompressible, laminar flow. Results considering the accuracy and predictive capabilities of the inferred reduced models are discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2306_14748 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Adjacency-based, Non-intrusive Reduced-order Modeling for Fluid-Structure Interactions Gkimisis, Leonidas Richter, Thomas Benner, Peter Fluid Dynamics Dynamical Systems Non-intrusive model reduction is a promising solution to system dynamics prediction, especially in cases where data are collected from experimental campaigns or proprietary software simulations. In this work, we present a method for non-intrusive model reduction applied to Fluid-Structure Interaction (FSI) problems. The approach is based on the a priori known sparsity of the full-order system operators, which is dictated by grid adjacency information. In order to enforce this type of sparsity, we solve a local, regularized least-squares problem for each degree of freedom on a grid, considering only the training data from adjacent degrees of freedom, thus making computation and storage of the inferred full-order operators feasible. After constructing the non-intrusive, sparse full-order model, Proper Orthogonal Decomposition (POD) is used for its projection to a reduced dimension subspace and thus the construction of a reduced-order model (ROM). The methodology is applied to the challenging Hron-Turek benchmark FSI3, for Re = 200. A physics-informed, non-intrusive ROM is constructed to predict the two-way coupled dynamics of a solid with a deformable, slender tail, subject to an incompressible, laminar flow. Results considering the accuracy and predictive capabilities of the inferred reduced models are discussed. |
| title | Adjacency-based, Non-intrusive Reduced-order Modeling for Fluid-Structure Interactions |
| topic | Fluid Dynamics Dynamical Systems |
| url | https://arxiv.org/abs/2306.14748 |