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Title page for ETD etd-03262012-125407

Type of Document Master's Thesis
Author McLemore, Kyle Scott
URN etd-03262012-125407
Title Test campaign design for model uncertainty reduction
Degree Master of Science
Department Civil Engineering
Advisory Committee
Advisor Name Title
Sankaran Mahadevan Committee Chair
Ronald Schrimpf Committee Member
  • calibration
  • resource allocation
  • test design
  • testing
  • Bayesian networks
  • multi-disciplinary analysis
Date of Defense 2012-03-21
Availability unrestricted
Testing or performing inspections to gain information and reduce uncertainty is a necessary activity during the life-cycle of any engineering system. This study develops analytical methods for the optimization of test and inspection campaigns at various system levels in order to reduce the uncertainty of the full system model prediction. The Bayesian network methodology is utilized to connect models, uncertain quantities, testing or inspection data, and various errors in a unified framework so that gaining information at a lower level can be used to reduce the uncertainty of the full system output. Once the Bayesian network is established, different test campaign options can be compared. The testing campaign which is likely to provided data that most efficiently and effectively reduces the uncertainty in the full system output is chosen as optimal. Four methodologies are developed that solve different test selection problems for engineering systems: (1) test-type selection, (2) test input setting design, (3) test campaign design for manufacturing optimization, and (4) inspection type selection during system operation. These methodologies are demonstrated on various multi-physics, multi-scale aerospace application problems including, a thermal vibration problem, a simplified telescope mirror problem, and a fatigue crack growth problem.
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