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dc.contributor.advisorKalidindi, Surya R.
dc.contributor.authorPaulson, Noah H.
dc.date.accessioned2018-08-20T15:27:42Z
dc.date.available2018-08-20T15:27:42Z
dc.date.created2017-08
dc.date.issued2017-05-05
dc.date.submittedAugust 2017
dc.identifier.urihttp://hdl.handle.net/1853/60113
dc.description.abstractComputational tools that are capable of rapidly exploring candidate microstructures and their associated properties are required to accelerate the rate of development and deployment of novel materials. In this work, a suite of computationally efficient protocols, based on the materials knowledge system (MKS) framework, are developed to evaluate the properties and performance of polycrystalline microstructures. In the MKS approach, physics-capturing coefficients (calibrated with microstructures and their responses obtained via experiments or simulations) store the microstructure-sensitive response of the material system of interest. Once calibrated, the linkages may be employed to predict the local responses (through localization) or effective properties (through homogenization) of new microstructures at low computational expense. Specifically, protocols are developed to predict bulk properties (elastic stiffness and yield strength), local cyclic plastic strains and resistance to fatigue crack formation and early growth (in the high cycle fatigue and transition fatigue regimes). These protocols are demonstrated on a diverse set of α-titanium microstructures, which exhibit heterogeneous microstructure features, in addition to anisotropy on multiple length-scales.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectMicrostructure
dc.subjectStructure-property relationship
dc.subjectPolycrystalline
dc.subjectTitanium alloys
dc.subjectHigh cycle fatigue
dc.subjectTransition fatigue
dc.subjectYield strength
dc.subjectElastic modulus
dc.subjectData science
dc.subjectMaterials informatics
dc.subjectHigh-throughput
dc.subject2-point correlations
dc.subjectComputational model
dc.subjectCrystal plasticity
dc.subjectReduced-order model
dc.subjectExtreme value statistics
dc.titleStructure-property linkages for polycrystalline materials using materials knowledge systems
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentMechanical Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberMcDowell, David L.
dc.contributor.committeeMemberShih, Donald S.
dc.contributor.committeeMemberNeu, Richard W.
dc.contributor.committeeMemberGarmestani, Hamid
dc.date.updated2018-08-20T15:27:42Z


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