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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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THE SCHOOL OF MATERIALS SCIENCE AND ENGINEERING
GEORGIA INSTITUTE OF TECHNOLOGY
Under the provisions of the regulations for the degree
DOCTOR OF PHILOSOPHY
on Tuesday, November 17, 2020
2:00 PM
via
Bluejeans Video Conferencing
https://bluejeans.com/447133714
will be held the
DISSERTATION DEFENSE
for
Gary Whelan
"Uncertainty Informed Integrated Computational Materials Engineering for Design and Development of Fatigue Critical Alloys"
Committee Members:
Prof. David L. McDowell, Advisor, ME/MSE
Prof. Richard W. Neu, ME/MSE
Prof. Hamid Garmestani, MSE
Prof. Yan Wang, ME
Laura P. Swiler, Ph.D., Sandia National Laboratories
Abstract:
Uncertainty is intrinsically tied to decision-making in design. Process-Structure-Property (PSP) relations are central to development of new and improved materials. The multitude of PSP linkages for any performance objective can be explored using the top down, inductive design exploration method (IDEM). Each PS and SP linkage has associated uncertainty, arising both from the types of models or interpretation of experimental results used to form linkages, as well as model parameters. These uncertainties can propagate and significantly affect the decision-making process in design and development of materials for specific performance targets. Uncertainty quantification (UQ) can be a highly computationally expensive undertaking in materials design and development. In this research, computationally efficient protocols are developed to effectively incorporate UQ in the IDEM. The uncertainty associated with PS linkages is assigned based on existing literature results. Gaussian process (GP) surrogate models are developed for the various SP linkages of interest as lower order approximations of computational expensive computational materials science simulations (e.g., the crystal plasticity finite element method (CPFEM)). These GP models are used to propagate uncertainty in microstructure attributes to the quantities of interest associated with properties that are then optimized in design. These surrogate models are then integrated into existing python IDEM (pyDEM) protocols in the form of mapping functions. In this work, novel protocols are developed and demonstrated for uncertainty-informed design and development of Ti-6Al-4V and Al7075-T6 microstructures for targeted performance requirements involving combinations of fatigue resistance, elastic stiffness, and yield strength.
