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Data-Driven Modeling of Nuclear System Thermal-Hydraulics

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서명/저자사항Data-Driven Modeling of Nuclear System Thermal-Hydraulics.
개인저자Chang, Chih-Wei.
단체저자명North Carolina State University. Nuclear Engineering.
발행사항[S.l.]: North Carolina State University., 2018.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2018.
형태사항184 p.
기본자료 저록Dissertation Abstracts International 79-12B(E).
Dissertation Abstract International
ISBN9780438282834
학위논문주기Thesis (Ph.D.)--North Carolina State University, 2018.
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Adviser: Nam T. Dinh.
요약The goal of this work is to develop a methodology to enhance predictive power of datadriven nuclear system thermal-hydraulics (NSTH) simulation using machine learning. NSTH simulation is instrumental for reactor design, safety analysis, and oper
요약The technical approach of the dissertation consists of three components. First, the technical background overview navigates the essential knowledge from related disciplines, including thermal-hydraulics models, system simulation, and machine lea
요약Five machine learning frameworks for NSTH have been introduced in the dissertation including physics-separated ML (PSML or Type I ML), physics-evaluated ML (PEML or Type II ML), physics-integrated ML (PIML or Type III ML), physics-recovered (PRM
요약Various numerical experiments are formulated ranging from system-level simulation to computational fluid dynamics (CFD) to exhibit the advantage of deep learning (DL) for model development. The case studies of system-level simulation using Type
요약The CFD case study exhibits that the DL-based Reynolds stress model can assimilate millions of data points to reduce forecast error. Performance of the DL-based stress can be quantified by flow features coverage mapping. The results show that Re
일반주제명Nuclear engineering.
Mechanical engineering.
Aerospace engineering.
언어영어
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