Prediction of apparent properties with uncertain material parameters using high‐order fictitious domain methods and PGD model reduction |
| |
Authors: | Gregory Legrain Mathilde Chevreuil Naoki Takano |
| |
Affiliation: | 1. GeM Institute, UMR CNRS 6183, école Centrale de Nantes, Université de Nantes, Nantes, France;2. Department of Mechanical Engineering, Keio University, Yokohama, Japan |
| |
Abstract: | This contribution presents a numerical strategy to evaluate the effective properties of image‐based microstructures in the case of random material properties. The method relies on three points: (1) a high‐order fictitious domain method; (2) an accurate spectral stochastic model; and (3) an efficient model‐reduction method based on the proper generalized decomposition in order to decrease the computational cost introduced by the stochastic model. A feedback procedure is proposed for an automatic estimation of the random effective properties with a given confidence. Numerical verifications highlight the convergence properties of the method for both deterministic and stochastic models. The method is finally applied to a real 3D bone microstructure where the empirical probability density function of the effective behaviour could be obtained. Copyright © 2016 John Wiley & Sons, Ltd. |
| |
Keywords: | fictitious domain method homogenization high‐order stochastic proper generalized decomposition |
|
|