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Enhanced error estimator based on a nearly equilibrated moving least squares recovery technique for FEM and XFEM

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  • معلومة اضافية
    • Contributors:
      Centro de Investigación en Tecnología de Vehículos (CITV); Universitat Politècnica de València = Universitad Politecnica de Valencia = Polytechnic University of Valencia (UPV); Institute of Mechanics and Advanced Materials (IMAM); Cardiff University; Institut de Recherche en Génie Civil et Mécanique (GeM); Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST); Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS); EPSRC grant EP/G042705/1, Ministerio de Ciencia y e Innovación y (Spain) DPI2010-20542, Universitat Politècnica de València , Generalitat Valenciana
    • بيانات النشر:
      HAL CCSD
      Springer Verlag
    • الموضوع:
      2013
    • Collection:
      Université de Nantes: HAL-UNIV-NANTES
    • نبذة مختصرة :
      International audience ; In this paper a new technique aimed to obtain accurate estimates of the error in energy norm using a moving least squares (MLS) recovery-based procedure is presented. We explore the capabilities of a recovery technique based on an enhanced MLS fitting, which directly provides continuous interpolated fields, to obtain estimates of the error in energy norm as an alternative to the superconvergent patch recovery (SPR). Boundary equilibrium is enforced using a nearest point approach that modifies the MLS functional. Lagrange multipliers are used to impose a nearly exact satisfaction of the internal equilibrium equation. The numerical results show the high accuracy of the proposed error estimator.
    • Relation:
      info:eu-repo/semantics/altIdentifier/arxiv/1208.6381; hal-00726633; https://hal.science/hal-00726633; https://hal.science/hal-00726633/document; https://hal.science/hal-00726633/file/MLSCX.pdf; ARXIV: 1208.6381
    • الرقم المعرف:
      10.1007/s00466-012-0814-7
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • الرقم المعرف:
      edsbas.A500FD95