Adjoint-based methods for optimization and goal-oriented error control applied to fluid-structure interaction: implementation of a partition-of-unity dual-weighted residual estimator for stationary forward FSI problems in deal.II
Thomas Wick

TL;DR
This paper develops and implements an adjoint-based goal-oriented error estimator for stationary fluid-structure interaction problems, enabling efficient mesh adaptivity and accurate computation of quantities like displacements, drag, and lift.
Contribution
It introduces a partition-of-unity dual-weighted residual estimator for FSI problems within a variational-monolithic framework, with open-source implementation in deal.II.
Findings
Effective error control for FSI quantities of interest
Open-source implementation available on GitHub
Enhanced mesh adaptivity improves solution accuracy
Abstract
In this work, we implement goal-oriented error control and spatial mesh adaptivity for stationary fluid-structure interaction. The a posteriori error estimator is realized using the dual-weighted residual method in which the adjoint equation arises. The fluid-structure interaction problem is formulated within a variational-monolithic framework using arbitrary Lagrangian-Eulerian coordinates. The overall problem is nonlinear and solved with Newton's method. We specifically consider the FSI-1 benchmark problem in which quantities of interest include the elastic beam displacements, drag, and lift. The implementation is provided open-source published on github https://github.com/tommeswick/goal-oriented-fsi. Possible extensions are discussed in the source code and in the conclusions of this paper.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComputational Fluid Dynamics and Aerodynamics · Advanced Numerical Methods in Computational Mathematics · Numerical methods for differential equations
