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dc.rights.licenseRestricted to current Rensselaer faculty, staff and students. Access inquiries may be directed to the Rensselaer Libraries.
dc.contributorKang, Hyun
dc.contributorJi, Wei
dc.contributorSahni, Onkar
dc.contributorJulius, Anak Agung
dc.contributor.authorChristian, Robby
dc.date.accessioned2021-11-03T09:10:28Z
dc.date.available2021-11-03T09:10:28Z
dc.date.created2019-10-01T15:33:13Z
dc.date.issued2019-05
dc.identifier.urihttps://hdl.handle.net/20.500.13015/2410
dc.descriptionMay 2019
dc.descriptionSchool of Engineering
dc.description.abstractThe 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.
dc.description.abstractKriging 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.
dc.description.abstractDynamic 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.
dc.language.isoENG
dc.publisherRensselaer Polytechnic Institute, Troy, NY
dc.relation.ispartofRensselaer Theses and Dissertations Online Collection
dc.subjectNuclear engineering
dc.titleDevelopment of a surrogate model for dynamic risk assessment using anisotropic Taylor Kriging methodology
dc.typeElectronic thesis
dc.typeThesis
dc.digitool.pid179696
dc.digitool.pid179697
dc.digitool.pid179698
dc.rights.holderThis electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
dc.description.degreePhD
dc.relation.departmentDept. of Mechanical, Aerospace, and Nuclear Engineering


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