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The shifted boundary method for solid mechanics
Author(s) -
Atallah Nabil M.,
Canuto Claudio,
Scovazzi Guglielmo
Publication year - 2021
Publication title -
international journal for numerical methods in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 168
eISSN - 1097-0207
pISSN - 0029-5981
DOI - 10.1002/nme.6779
Subject(s) - computer science , boundary representation , computational mechanics , boundary (topology) , representation (politics) , stability (learning theory) , simple (philosophy) , computer graphics , computational geometry , set (abstract data type) , geomechanics , convergence (economics) , boundary value problem , algorithm , mesh generation , level set method , complement (music) , complex geometry , geometry , finite element method , mathematics , mathematical analysis , structural engineering , artificial intelligence , engineering , philosophy , law , economic growth , chemistry , segmentation , biochemistry , epistemology , machine learning , political science , programming language , geotechnical engineering , complementation , politics , economics , image segmentation , gene , phenotype
Abstract We propose a new embedded/immersed framework for computational solid mechanics, aimed at vastly speeding up the cycle of design and analysis in complex geometry. In many problems of interest, our approach bypasses the complexities associated with the generation of CAD representations and subsequent body‐fitted meshing, since it only requires relatively simple representations of the surface geometries to be simulated, such as collections of disconnected triangles in three dimensions, widely used in computer graphics. Our approach avoids the complex treatment of cut elements, by resorting to an approximate boundary representation and a special (shifted) treatment of the boundary conditions to maintain optimal accuracy. Natural applications of the proposed approach are problems in biomechanics and geomechanics, in which the geometry to be simulated is obtained from imaging techniques. Similarly, our computational framework can easily treat geometries that are the result of topology optimization methods and are realized with additive manufacturing technologies. We present a full analysis of stability and convergence of the method, and we complement it with an extensive set of computational experiments in two and three dimensions, for progressively more complex geometries.