Adaptive analysis-aware defeaturing
Adaptive analysis-aware defeaturing

"Adaptive analysis-aware defeaturing", organizado polo CITMAga. Será impartido por Rafael Vázquez, MNS, Institute of Mathematics (École Polytechnique Fédérale de Lausanne)
Data: mércores 15 de febreiro
Hora: 11:00 h.
Duración: 45 min
Lugar: En liña (MS Teams)
Abstract:
In computer aided engineering, it is crucial to understand the impact of geometrical model simplification, also called defeaturing, on the solution accuracy of a partial differential equation at hand. Indeed, removing features from a complex geometry is a classical operation in computer aided design for manufacturing that simplifies the meshing process and that enables faster simulations. But removing the wrong features may greatly impact the solution accuracy. This is why understanding well the effects of defeaturing is an important step to be able to adaptively integrate design and analysis. In this talk, we will present an adaptive strategy for analysis-aware defeaturing that is twofold. On the one hand, the algorithm performs standard mesh refinement in a (partially) defeatured geometry, using isogeometric analysis with hierarchical B-splines. On the other hand, the strategy also allows for geometrical refinement. That is, at each iteration, it is able to choose which missing geometrical feature should be added to the simplified geometrical model, in order to obtain a more accurate solution. To drive this adaptive strategy, we will introduce an a posteriori estimator of the energy norm of the error between the exact solution defined in the exact fully-featured geometry, and the numerical approximation of the solution defined in the defeatured geometry. This estimator is proven to be reliable for very general geometrical configurations, it can be computed very efficiently, and it is naturally parallelizable with respect to the number of features. During the talk, we will also show the results of some numerical experiments that illustrate the capabilities of the proposed adaptive strategy.
This is joint work with Pablo Antolin, Annalisa Buffa and Ondine Chanon.