A Novel Framework for Data-Driven Modeling, Uncertainty Quantification, and Deep Learning of Nuclear Reactor Simulations.
저자
발행사항
Ann Arbor : ProQuest Dissertations & Theses, 2019
학위수여대학
University of Illinois at Urbana-Champaign Nuclear, Plasma, & Rad Engr
수여연도
2019
작성언어
영어
주제어
학위
Ph.D.
페이지수
443 p.
지도교수/심사위원
Advisor: Kozlowski, Tomasz.
This work presents a novel and modern method for reactor modeling, simulation, and uncertainty characterization through an integrated framework developed under the terminology of combining four fundamental principles in scientific modeling and computing: Physics, Models, Data, and UQ (Uncertainty Quantification). The framework houses various physical phenomena that occur inside nuclear reactors, such as neutronics, reactor kinetics, fuel depletion, thermal-hydraulics, and fuel performance as well as outside the reactor such as spent fuel and criticality safety. The framework utilizes various computer models in the nuclear area, which are already validated and known to provide accurate results. The framework is supported and validated by a wide range of experimental data from different single and multiphysics experiments, such as delayed neutron data, void fraction measurements, isotopic composition, nuclear data, and others. Many computational models to simulate the actual physical phenomena are developed under this framework, which vary in their complexity from a simple 2D pin-cell to a complex 3D lattice model with multiphysics coupling. Additionally, the framework is built based upon a wide range of mathematical and statistical methods featuring different areas such as sensitivity analysis, variance decomposition, dimensionality analysis and reduction, reduced order modeling, machine learning, data science, deep learning, Monte Carlo and deterministic uncertainty propagation, Bayesian statistics, correlation analysis, and data assimilation. All efforts in this thesis are expected to yield a better understanding of nuclear reactor simulations, which in turn can lead to improved performance, safety, and reduced costs for nuclear industry. Within this thesis, many frameworks, platforms, and models are developed to support the master framework. An integrated UQ approach is developed through the Bayesian framework, which handles various forms of uncertainty in scientific modeling such as parametric, experimental, predictive, interpolation, and model-form uncertainty. The methodology is useful to account for various uncertainty sources in nuclear computer models. This integrated UQ methodology can also be used for model selection of different physical models, through evaluating them against real data. The methodology is applied in this thesis to nuclear thermal-hydraulics and two-phase flow codes to quantify their predictive and model-form uncertainties. Data science methods are a core part of the framework. Machine learning methods are integrated to alleviate the computational burden of the complex simulations to construct cheap-to-evaluate reduced order or surrogate models. Modern deep learning methods form a major part of this thesis to analyze complex datasets resulting from the advanced simulations generated using the master framework. These machine and deep learning models are tested using real-world and benchmarked nuclear simulations with different underlying physics, from fundamental nuclear data to nuclear fuel performance. Data-driven models are constructed using simulation and experimental data to perform uncertainty propagation, surrogate modeling, model validation, and variance decomposition. Development of a new precursor-group kinetics framework is done to propagate the uncertainty into reactor kinetic parameters due to the fundamental nuclear and delayed neutron data. Coupling of single physics processes (e.g. neutronics, thermal-hydraulics, fuel performance) to form more realistic multiphysics simulations is also accomplished through FUSE platform. FUSE is verified through two test cases of two-way coupled neutronics-thermal-hydraulics and neutronics-fuel performance simulations. Spent fuel analysis and criticality safety frameworks are built as a validation object to assess the accuracy of the framework modeling approaches. The spent fuel composition discharged from the reactor core is assessed in the spent fuel cask to determine the overall system safety. A comprehensive application of the spent fuel framework on BWR spent fuel is carried out in this thesis. All the physics, data, methods, and frameworks are integrated into the master framework developed in this thesis. The major achievements of the framework developed to the nuclear area include: a set of kinetic parameters' values and uncertainties for light water reactor systems, advanced depletion models for accurate burnup credit of BWR, integrated assessment and advanced UQ of nuclear computer models, a platform for nuclear multiphysics simulations, and building deep learning models for high dimensional UQ purposes.Most of the methods and the frameworks developed here are extendable to other problems outside the nuclear area. The reader is strongly recommended to read the first chapter of this thesis as it will provide directions to efficiently access the whole document. The first chapter presents an executive summary of the work done over the whole thesis. This thesis is published in several peer-reviewed articles in premier conferences and journals specialized in nuclear engineering, system safety, uncertainty quantification, and energy resources. A summary of the framework developed in this thesis is published in Radaideh and Kozlowski (2019b).
분석정보
서지정보 내보내기(Export)
닫기소장기관 정보
닫기권호소장정보
닫기오류접수
닫기오류 접수 확인
닫기음성서비스 신청
닫기음성서비스 신청 확인
닫기이용약관
닫기학술연구정보서비스 이용약관 (2017년 1월 1일 ~ 현재 적용)
학술연구정보서비스(이하 RISS)는 정보주체의 자유와 권리 보호를 위해 「개인정보 보호법」 및 관계 법령이 정한 바를 준수하여, 적법하게 개인정보를 처리하고 안전하게 관리하고 있습니다. 이에 「개인정보 보호법」 제30조에 따라 정보주체에게 개인정보 처리에 관한 절차 및 기준을 안내하고, 이와 관련한 고충을 신속하고 원활하게 처리할 수 있도록 하기 위하여 다음과 같이 개인정보 처리방침을 수립·공개합니다.
주요 개인정보 처리 표시(라벨링)
목 차
3년
또는 회원탈퇴시까지5년
(「전자상거래 등에서의 소비자보호에 관한3년
(「전자상거래 등에서의 소비자보호에 관한2년
이상(개인정보보호위원회 : 개인정보의 안전성 확보조치 기준)개인정보파일의 명칭 | 운영근거 / 처리목적 | 개인정보파일에 기록되는 개인정보의 항목 | 보유기간 | |
---|---|---|---|---|
학술연구정보서비스 이용자 가입정보 파일 | 한국교육학술정보원법 | 필수 | ID, 비밀번호, 성명, 생년월일, 신분(직업구분), 이메일, 소속분야, 웹진메일 수신동의 여부 | 3년 또는 탈퇴시 |
선택 | 소속기관명, 소속도서관명, 학과/부서명, 학번/직원번호, 휴대전화, 주소 |
구분 | 담당자 | 연락처 |
---|---|---|
KERIS 개인정보 보호책임자 | 정보보호본부 김태우 | - 이메일 : lsy@keris.or.kr - 전화번호 : 053-714-0439 - 팩스번호 : 053-714-0195 |
KERIS 개인정보 보호담당자 | 개인정보보호부 이상엽 | |
RISS 개인정보 보호책임자 | 대학학술본부 장금연 | - 이메일 : giltizen@keris.or.kr - 전화번호 : 053-714-0149 - 팩스번호 : 053-714-0194 |
RISS 개인정보 보호담당자 | 학술진흥부 길원진 |
자동로그아웃 안내
닫기인증오류 안내
닫기귀하께서는 휴면계정 전환 후 1년동안 회원정보 수집 및 이용에 대한
재동의를 하지 않으신 관계로 개인정보가 삭제되었습니다.
(참조 : RISS 이용약관 및 개인정보처리방침)
신규회원으로 가입하여 이용 부탁 드리며, 추가 문의는 고객센터로 연락 바랍니다.
- 기존 아이디 재사용 불가
휴면계정 안내
RISS는 [표준개인정보 보호지침]에 따라 2년을 주기로 개인정보 수집·이용에 관하여 (재)동의를 받고 있으며, (재)동의를 하지 않을 경우, 휴면계정으로 전환됩니다.
(※ 휴면계정은 원문이용 및 복사/대출 서비스를 이용할 수 없습니다.)
휴면계정으로 전환된 후 1년간 회원정보 수집·이용에 대한 재동의를 하지 않을 경우, RISS에서 자동탈퇴 및 개인정보가 삭제처리 됩니다.
고객센터 1599-3122
ARS번호+1번(회원가입 및 정보수정)