Premium
An adaptive stochastic inverse solver for multiscale characterization of composite materials
Author(s) -
Hu Nan,
Fish Jacob,
McAuliffe Colin
Publication year - 2016
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.5341
Subject(s) - solver , latin hypercube sampling , inverse problem , adaptive sampling , sampling (signal processing) , discretization , characterization (materials science) , computer science , measure (data warehouse) , inverse , mathematical optimization , algorithm , salient , convergence (economics) , mathematics , artificial intelligence , data mining , monte carlo method , mathematical analysis , statistics , materials science , geometry , filter (signal processing) , economic growth , economics , computer vision , nanotechnology
Summary We present an adaptive variant of the measure‐theoretic approach for stochastic characterization of micromechanical properties based on the observations of quantities of interest at the coarse (macro) scale. The salient features of the proposed nonintrusive stochastic inverse solver are identification of a nearly optimal sampling domain using enhanced ant colony optimization algorithm for multiscale problems, incremental Latin‐hypercube sampling method, adaptive discretization of the parameter and observation spaces, and adaptive selection of number of samples. A complete test data of the TORAY T700GC‐12K‐31E and epoxy #2510 material system from the National Institute for Aviation Research report is employed to characterize and validate the proposed adaptive nonintrusive stochastic inverse algorithm for various unnotched and open‐hole laminates. Copyright © 2016 John Wiley & Sons, Ltd.