Multi-Scale Design and Optimization of Microstructures under Uncertainties

Multi-scale modeling of microstructures;Design of microstructures;Optimization of microstructure design;Uncertainty quantification;Optimization of microstructures under uncertainties;Process design;Aerospace Engineering;Engineering;Aerospace Engineering

Acar, PinarWaas, Anthony M ;

University of Michigan

关键词: Multi-scale modeling of microstructures;    Design of microstructures;    Optimization of microstructure design;    Uncertainty quantification;    Optimization of microstructures under uncertainties;    Process design;    Aerospace Engineering;    Engineering;    Aerospace Engineering;   

Others  :  https://deepblue.lib.umich.edu/bitstream/handle/2027.42/138534/acarp_1.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Research on computational modeling and multiscale design of materials has been garnering a lot of interest due to the demand for high performance materials in electronics, energy and structural applications. The primary goal of the present study is to develop a new computational approach for microstructure design for achieving a set of material properties within a designated level of uncertainty. This thesis combines the methods of uncertainty quantification (UQ) and materials design, using a unique linearization approach that is well-suited for metallic materials modeled using probabilistic descriptors such as the orientation distribution function. An analytical UQ formulation is proposed to model the uncertainties in microstructural features from experimental (electron diffraction) data as well as for inverse modeling the uncertainties in optimal microstructural features from property data. Compared to the widely preferred computational UQ algorithms the analytical model reduces the required computational time significantly as well as capturing the effect of stochasticity in microstructure design accurately. The optimal processing route, which produces materials with optimized texture and/or properties, is identified by developing reduced order models to represent the texture evolution. Examples presented include the performance improvement of Titanium aircraft panels for thermal buckling, and optimization of Fe-Ga alloys for vibration response and identification of optimal processing route for Fe-Ga alloy microstructures.