자료유형  

 학위논문

Control Number  

0015490601

International Standard Book Number  

9781085557764

Dewey Decimal Classification Number  

620

Main Entry-Personal Name  

Christian, Robby.

Publication, Distribution, etc. (Imprint  

[Sl] : Rensselaer Polytechnic Institute, 2019

Publication, Distribution, etc. (Imprint  

Ann Arbor : ProQuest Dissertations & Theses, 2019

Physical Description  

271 p

General Note  

Source: Dissertations Abstracts International, Volume: 81-02, Section: B.

General Note  

Includes supplementary digital materials.

General Note  

Advisor: Kang, Hyun Gook.

Dissertation Note  

Thesis (Ph.D.)--Rensselaer Polytechnic Institute, 2019.

Restrictions on Access Note  

This item must not be sold to any third party vendors.

Summary, Etc.  

요약Dynamic probabilistic risk assessment (DPRA) has received growing attention due to its superior capability in modeling the dynamic dependencies between safety systems or mitigation actions. However, DPRA is technically challenging since it requires an infinite number of scenarios to be assessed to capture these dependencies precisely. To overcome this challenge, we propose the use of a surrogate model to approximate the process dynamics based on a limited number of sampled scenarios, thereby making DPRA implementation more practical.Kriging methodology was chosen to construct the surrogate model, owing to its capability to model the deterministic trend of risk-inducing physics between samples, and the expected stochastic deviations around this trend due to inevitable epistemic uncertainties. Unfortunately, since a surrogate for DPRA needs to include process time-histories, the computational cost of a Kriging-based proxy quickly escalates with the required length of time-history. Therefore, we propose an adaptation to the Kriging algorithm to make it more time-efficient. Additionally, we also propose to pre-align the time-series sample data by considering the time-shifts of key physical processes between samples. This data pre-processing was done through an adaptation of the Dynamic Time Warping algorithm.The proposed surrogate methodology was applied on two separate research projects we work on: (1) risk assessment of spent nuclear fuel transportation, and (2) risk assessment of Accident Tolerant Fuel (ATF). As we developed the risk model of maritime spent fuel transportation, we found that dynamic trajectories of shipment route affect the risk metrics significantly. Since there is an infinite possible route combination over the sea, we leveraged the surrogate model to find the safest shipment route efficiently. The proposed surrogate method was able to identify the safest route, by avoiding risky marine traffic intersections which could lead to ship collisions and transport cask damage. Meanwhile, in the latter project, we optimized the Emergency Core Cooling System (ECCS) performance criteria to properly boost the accident-tolerance characteristics of a multi-layered SiC cladding structure. Because ATF has a better thermal margin relative to current UO2-Zr system, there is a relatively wider range of ECCS operational performance the fuel can safely tolerate. Therefore, we used the surrogate model to predict SiC clad responses over this broad range of allowable ECCS performance uncertainty. The model suggested that conservatism in ECCS performance requirements could be relaxed while still maintaining a net positive safety margin relative to UO2-Zr system, which may lead to operational cost savings to plant operators in conformance to 10 CFR 50.69.

Subject Added Entry-Topical Term  

Nuclear engineering

Subject Added Entry-Topical Term  

Engineering

Added Entry-Corporate Name  

Rensselaer Polytechnic Institute Nuclear Engineering

Host Item Entry  

Dissertations Abstracts International. 81-02B.

Host Item Entry  

Dissertation Abstract International

Electronic Location and Access  

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Control Number  

joongbu:567163