2012
DOI: 10.1016/j.cma.2012.02.014
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Goal-oriented error estimation for fluid–structure interaction problems

Abstract: In this work, we present an adaptive finite element method for the numerical simulation of fluid-structure interaction problems. The coupled system is formulated in a variational monolithic Arbitrary Lagrangian Eulerian framework. We derive methods for goal-oriented error estimation and mesh adaptation with the dual weighted residual method. Key to this error estimator is a Petrov-Galerkin approach for deriving the variational formulation of the coupled system. The developed method is applied to two and three … Show more

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Cited by 68 publications
(53 citation statements)
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References 60 publications
(31 reference statements)
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“…The computed u−velocity vector component isosurfaces are shown in Figure 6 with the streamtraces at Re = 40. The computed deformation vector components at the point A(0.45, 0.15, 0.15) are given in Table 5 and the values are compared with the results of Richter [30]. The results are relatively in good agreement.…”
Section: Test Case Ii: 3d Fsi Problem Of An Elastic Beam In a Cross Flowsupporting
confidence: 65%
“…The computed u−velocity vector component isosurfaces are shown in Figure 6 with the streamtraces at Re = 40. The computed deformation vector components at the point A(0.45, 0.15, 0.15) are given in Table 5 and the values are compared with the results of Richter [30]. The results are relatively in good agreement.…”
Section: Test Case Ii: 3d Fsi Problem Of An Elastic Beam In a Cross Flowsupporting
confidence: 65%
“…In addition, the streamtraces computed on the solid walls are shown in Figure 8 on the front and on the back side of the solid body. The computed deformation vector components at the point A(0.45, 0.15, 0.15) are given in Table 5 for more precise comparison and the values are compared with the results of Richter [56]. The results are relatively in good agreement.…”
Section: Test Case Ii: 3d Fsi Problem Of An Elastic Beam In a Cross Flowsupporting
confidence: 63%
“…In that case, a new grid has to be generated for at least part of the domain. Richter [161] uses local grid adaptivity driven by goal-oriented error estimation. The interpolation from the old to the new grid inevitably causes errors.…”
Section: Eulerianmentioning
confidence: 99%