arrow
Return

Geometric element parameterization and parametric model order reduction in finite element based shape optimization

delete2018-08-25
delete17
PRE
AI
B
Benjamin Fröhlich
J
Jan Gade
F
Florian Geiger
M
Manfred Bischoff
P
Peter Eberhard *
DOI:10.1007/s00466-018-1626-1delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
This contribution proposes a new approach to derive geometrically parameterized, reduced order finite element models. An element formulation for geometrically parameterized finite elements is suggested. The parameterized elements are used to derive models with a parameterized geometry where the parameterized system matrices are expressed in an affine representation. Parametric model order reduction can then be efficiently used to reduce the full order parameterized model to a reduced order parameterized model. The approach shows two beneficial features. First, design studies and shape optimizations can be conducted with parameterized reduced order model of much lower dimension compared to the parameterized, full order model. Second, it is possible to compute sensitivities analytically, and therefore, to avoid the computation of finite differences gradients. The approach is illustrated with two numerical examples. The first example includes a detailed error analysis. The second example is a shape optimization example of an adaptive structure.
Keywords:
Parametric model order reduction
Shape optimization
Reduced order modeling
Moment matching
AI Summary

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Computational Mechanics cover
Computational Mechanics
IF:
3.8
Papers:
3.2K
Citations:
9.0K

Organization

U
University of Stuttgart
Scholars:
1.1W
Papers: 9.4K
Citations: 1.3W