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Efficient Uncertainty Propagation for Model-Assisted Probability of Detection and Sensitivity Analysis via Metamodeling and Multifidelity Methods
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Efficient Uncertainty Propagation for Model-Assisted Probability of Detection and Sensitivity Analysis via Metamodeling and Multifidelity Methods. |
| 개인저자 | Du, Xiaosong. |
| 단체저자명 | Iowa State University. Aerospace Engineering. |
| 발행사항 | [S.l.]: Iowa State University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 246 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-03B. Dissertation Abstract International |
| ISBN | 9781085626019 |
| 학위논문주기 | Thesis (Ph.D.)--Iowa State University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-03, Section: B.
Advisor: Leifsson, Leifur. |
| 이용제한사항 | This item must not be sold to any third party vendors. |
| 요약 | 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-fidelity computational budget is limited. In particular, the objective is to create and evaluate new metamodeling and multifidelity 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) multifidelity 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 multifidelity 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-specified 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 efficient decision making for aerodynamic design under uncertainty without using weighted-sum method and estimating statistics of the objective function. |
| 일반주제명 | Aerospace engineering. |
| 언어 | 영어 |
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