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Stochastic identification of elastic constants for anisotropic materials
Author(s) -
Furukawa Tomonari,
Pan Jan Wei
Publication year - 2009
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.2700
Subject(s) - kalman filter , anisotropy , identification (biology) , parametric statistics , mathematics , algorithm , noise (video) , computer science , statistical physics , physics , optics , artificial intelligence , statistics , botany , biology , image (mathematics)
This paper presents an energy‐based characterization technique that stochastically identifies the elastic constants of anisotropic materials by modeling the measurement noise and removing its effect unlike conventional deterministic techniques, which deterministically identify the elastic constants directly from noisy measurements. The technique recursively estimates the elastic constants at every acquisition of measurements using Kalman Filter. Owing to the non‐linear expression of the measurement model, a Kalman gain has been newly derived and achieves optimal estimation. Since the variances in addition to the means are computed, the proposed technique can not only identify the elastic constants but also describe their certainty as an additional advantage. The validity of the proposed technique and its superiority to the conventional technique were first demonstrated via parametric studies of low‐dimensional problems. The proposed technique was then successfully applied to the identification of elastic constants of an anisotropic material. Copyright © 2009 John Wiley & Sons, Ltd.