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Title page for ETD etd-07202017-185954


Type of Document Dissertation
Author DeCarlo, Erin Camille
URN etd-07202017-185954
Title Uncertainty Quantification and Confidence Assessment in Time-Dependent, Multidisciplinary Simulations
Degree PhD
Department Civil Engineering
Advisory Committee
Advisor Name Title
Sankaran Mahadevan Committee Chair
Benjamin P. Smarslok Committee Member
Haoxiang Luo Committee Member
Hiba Baroud Committee Member
Prodyot K. Basu Committee Member
Keywords
  • model reliability
  • multidisciplinary
  • global sensitivity analysis
  • inverse problems
  • uncertainty quantification
  • Bayesian methods
Date of Defense 2017-07-07
Availability unrestricted
Abstract
Multidisciplinary simulations are often assembled based on limited data and approximate individual and partial-physics model components. Methodologies are proposed to address the following three challenges in the context of both inverse and forward uncertainty quantification (UQ) problems: 1) computational expense of multidisciplinary simulations, 2) error accumulation across multiple models and over time, and 3) uncertainty due to the availability of limited data. For inverse problems, the development of a segmented Bayesian model calibration strategy reduces the computational effort of calibration when multiple information sources are available. Further, prediction confidence is improved by reducing the uncertainty that aggregates between coupled analyses and through time using a partitioned approach to calibrate model errors. Methodology contributions for the forward problem include an efficient global sensitivity analysis method (to support dimension reduction) that incorporates existing model calibration results and an optimization framework that balances prediction confidence and computational effort to select variable model fidelity in multidisciplinary simulations. These methods are illustrated with time-dependent, aerothermoelastic analyses of airfoils subjected to high-speed flow.
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