Title:
Hierarchical multiscale modeling of Ni-base superalloys
Hierarchical multiscale modeling of Ni-base superalloys
Author(s)
Song, Jin E.
Advisor(s)
McDowell, David L.
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Abstract
Ni-base superalloys are widely used in hot sections of gas turbine engines due to the high resistance to fatigue and creep at elevated temperatures. Due to the demands for improved performance and efficiency in applications of the superalloys, new and improved higher temperature alloy systems are being developed. Constitutive relations for these materials need to be formulated accordingly to predict behavior of cracks at notches in components under cyclic loading with peak dwell periods representative of gas turbine engine disk materials. Since properties are affected by microstructure at various length scales ranging from 10 nm tertiary γ' precipitates to 5-30 μm grains, hierarchical multiscale modeling is essential to address behavior at the component level.
The goal of this work is to develop a framework for hierarchical multiscale modeling network that features linkage of several fine scale models to incorporate relevant microstructure attributes into the framework to improve the predictability of the constitutive model. This hierarchy of models is being developed in a collaborative research program with the Ohio State University. The fine scale models include the phase field model which addresses dislocation dissociation in the γ matrix and γ' precipitate phases, and the critical stresses from the model are used as inputs to a grain scale crystal plasticity model in a bottom-up fashion. The crystal plasticity model incorporates microstructure attributes by homogenization.
A major task of the present work is to link the crystal plasticity model, informed by the phase field model, to the macroscale model and calibrate models in a top-down fashion to experimental data for a range of microstructures of the improved alloy system by implementing a hierarchical optimization scheme with a parameter clustering strategy. Another key part of the strategy to be developed in this thesis is the incorporation of polycrystal plasticity simulations to model a large range of virtual microstructures that have not been experimentally realized (processed), which append the experimentally available microstructures. Simulations of cyclic responses with dwell periods for this range of virtual (and limited experimental) polycrystalline microstructures will be used to (i) provide additional data to optimize parameter fitting for a microstructure-insensitive macroscopic internal state variable (ISV) model with thermal recovery and rate dependence relevant to the temperatures of interest, and (ii) provide input to train an artificial neural network that will associate the macroscopic ISV model parameters with microstructure attributes for this material. Such microstructure sensitive macroscopic models can then be employed in component level finite element studies to model cyclic behavior with dwell times at smooth and cracked notched specimens.
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Date Issued
2010-07-08
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Text
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Thesis