The proposed project concerns the development of a computational strategy, coined *virtual material testing*, for predicting the macroscopic mechanical properties such as elasticity, plasticity and damage. Essential features are computational homogenization on Representative Volume Elements (RVE) and the statistical sampling of RVE-realizations. The tool will be applied to a few model materials with distinct (and different) mesoscale features: Duplex stainless steel (DSS) and nodular cast-iron. In particular, the purpose is to produce *virtual experimental data* for upscaling, i.e. for calibration of phenomenological constitutive models relevant to the chosen model materials, or it will be used as part of *nested* meso-macroscale computations (FE^2-technology). The following main tasks will be carried out: (1) Develop the *virtual material testing* strategy for estimating bounds on effective properties with given confidence. (2) Develop a strategy for dimensional reduction from 3D subscale modeling to 2D (or 1D) macroscale modeling. (3) Compute macroscale yield and subsequent loading surfaces in the macroscale stress space. (4) Simulate Manson-Coffin and Wöhler relations pertinent to Low-Cycle-Fatigue (LCF). (5) Calibrate the subscale constitutive models from macroscale tests, whereby experimental data from the literature and from (current and previous) partners involved in experimental research will be used.
Professor at Applied Mechanics, Material and Computational Mechanics
Funding years 2011–2013
Chalmers Driving Force