2015
DOI: 10.1016/j.cma.2015.04.002
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A coupled IGA–Meshfree discretization of arbitrary order of accuracy and without global geometry parameterization

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Cited by 56 publications
(17 citation statements)
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“…This approach uses pixel data obtained from medical images which are used as nodes for domain discretization in the meshless modeling. Valizadeh et al [32] implemented a 3D patient specific leg-muscle pixel-based model using isogeometric analysis (IGA) and the RKPM.…”
Section: Skeletal Muscle Simulationsmentioning
confidence: 99%
“…This approach uses pixel data obtained from medical images which are used as nodes for domain discretization in the meshless modeling. Valizadeh et al [32] implemented a 3D patient specific leg-muscle pixel-based model using isogeometric analysis (IGA) and the RKPM.…”
Section: Skeletal Muscle Simulationsmentioning
confidence: 99%
“…As the tensor product mesh has been a limitation in conventional isogeometric analysis, combinations of various meshfree methods and IGA have been developed by many researchers and applied successfully in many fields [17][18][19][20]. In meshfree methods, the domain of a problem is represented, ideally, only by a set of arbitrarily distributed nodes.…”
Section: Introductionmentioning
confidence: 99%
“…It follows from the Kronecker δ function property of the RPIM that boundary constraints can be directly applied to the field nodes. Then, Equation (16) can be rewritten as KU = F (17) which is the final discretization equation shaped by the meshfree RPIM. When the linear equation in Equation (17) is solved, the displacement U of all field nodes will be gained, and the stress σ can be recovered from:…”
mentioning
confidence: 99%
“…The volume discretization can be challenging for the IGA method, whereas it can be readily solved by meshfree methods. Valizadeh et al combined the IGA method with the RKPM method in the physical domain to avoid global geometry parameterization. In this way, the basis functions are constructed directly in the physical domain without requiring global geometry parameterization in RKPM.…”
Section: Introductionmentioning
confidence: 99%