Degree Type
Dissertation
Date of Award
2019
Degree Name
Doctor of Philosophy
Department
Aerospace Engineering
Major
Aerospace Engineering
First Advisor
Leifur Leifsson
Abstract
Physics-based simulation models are important to the decision-making process in the
design of modern engineered systems. The key challenges of using accurate predictive
simulations in this process are (1) time-consuming model evaluations, (2) a large number of
parameters, (3) often complex and highly coupled systems, and (4) conventional modeling
and optimization techniques typically require a large amount of model evaluations. The
research objective of this work is to accelerate the process of UP and optimum design
under uncertainty when the high-delity computational budget is limited. In particular,
the objective is to create and evaluate new metamodeling and multidelity methods that
enable the solution of problems that cannot be addressed with the current state-of-the-art
methods. The scope of the work is limited to nondestructive testing (NDT) systems and
aerodynamic surfaces.
In this work, the least-angle regression (LARS)-based polynomial chaos expansions
(PCE), polynomial chaos-based Kriging (PCKriging) metamodeling, Cokriging and the proposed
polynomial chaos-based Cokriging (PC-Cokriging) multidelity method are used to
enable the fast uncertainty propagation (UP) for reliability and sensitivity analysis of NDT
systems for the rst time. In addition, the manifold mapping (MM) multidelity metamodeling
method was implemented for ecient aerodynamic forward/inverse shape optimization
for the rst time. Lastly, utility theory was introduced for aerodynamic optimum shape
design under uncertainty.
The results of several numerical examples show that the aforementioned metamodeling
and multidelity methods proposed in this work for the reliability and sensitivity analysis of
NDT systems outperformed the current state-of-the-art Kriging and ordinary least-squares
(OLS)-based PCE by reducing the high-delity (HF) training data from one to two orders of
magnitude. In particular, the new and unique PC-Cokriging multidelity method reduced
the cost by up to two orders of magnitude in the NDT benchmark cases. Furthermore, the
proposed PC-Cokriging method is shown to be robust in terms of the user-specied detection
thresholds. For the aerodynamic shape design, the MM-based aerodynamic local optimization
algorithm alleviated the computational cost of direct HF model-based optimization by
up to one order of magnitude. Moreover, utility theory was shown to yield ecient decision
making for aerodynamic design under uncertainty without using weighted-sum method and
estimating statistics of the objective function.
Copyright Owner
Xiaosong Du
Copyright Date
2019-05
Language
en
File Format
application/pdf
File Size
245 pages
Recommended Citation
Du, Xiaosong, "Efficient uncertainty propagation for model-assisted probability of detection and sensitivity analysis via metamodeling and multifidelity methods" (2019). Graduate Theses and Dissertations. 17007.
https://lib.dr.iastate.edu/etd/17007