Stochastic material characterization of heterogeneous media with randomly distributed material properties
Shen Shang · OhioLink ETD Center (Ohio Library and Information Network) · 2012
In the field of computational mechanics, there has been a very challenging problem, which is the characterization of heterogeneous media with randomly distributed material properties.In reality, no material is homogeneous and deterministic in nature and it has been well-known that randomness in microstructures and properties of materials could significantly influence scatter of structural response at larger scales.Therefore, stochastic characterization of heterogeneous materials has increasingly received attention in various engineering and science fields.In order to deal with this challenging problem, two major challenges need to be addressed: 1) developing an efficient modeling technique to discretize the material uncertainty in the stochastic domain and 2) developing a robust and general inverse identification computational framework that can estimate parameters related to material uncertainties.In this dissertation, two major challenges have been addressed by proposing a robust inverse analysis framework that can estimate parameters of material constitutive models based on a set of limited global boundary measurements and combining the framework with a general stochastic finite element analysis tool.Finally a new stochastic inverse analysis framework has been proposed, which has a novel capability of modeling effects of spatial variability of both linear and nonlinear material properties on macroscopic material and structural response.By inversely identifying statistical parameters (e.g.spatial mean, spatial variance, spatial correlation length, and random 2000 1