|About this Abstract
||2017 TMS Annual Meeting & Exhibition
||Computational Methods and Experimental Approaches for Uncertainty Quantification and Propagation, Model Validation, and Stochastic Predictions
||An Integrated Microstructure Development and Crystal Plasticity Approach with Uncertainty Quantification for Multi-scale Constitutive Model Development.
||Maxwell Andreas Pinz, George Weber, Somnath Ghosh
|On-Site Speaker (Planned)
||Maxwell Andreas Pinz
Nickel based superalloys, whose strength is derived from its γ-γ’ microstructure, have long been used for high temperature applications. To better understand the effect of the microstructure on the mechanical response, we present an uncertainty quantification framework for the calibration of a dislocation density based hierarchical crystal plastic model for the nickel based superalloy Rene’88DT. The framework consists of a genetic algorithm that can use two sub algorithms: A method to generate statistically equivalent sub-grain microstructures based on microstructural parameters, and a dislocation density based CPFEM model. The genetic algorithm perturbs parameters of the constitutive models as well as the microstructural parameters. The genetic algorithm fits constitutive model parameters, while simultaneously gaining understanding of the uncertainty and sensitivity of both microstructure, and constitutive parameters. Decomposition of variance determines which parameters are relevant to the mechanical response. Irrelevant microstructural parameters will not be perturbed in subsequent rounds of the genetic algorithm.