| LDR | | 02695nam u200457 4500 |
| 001 | | 000000420415 |
| 005 | | 20190215164447 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438282834 |
| 035 | |
▼a (MiAaPQ)AAI10969768 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 539.76 |
| 100 | 1 |
▼a Chang, Chih-Wei. |
| 245 | 10 |
▼a Data-Driven Modeling of Nuclear System Thermal-Hydraulics. |
| 260 | |
▼a [S.l.]:
▼b North Carolina State University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 184 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Adviser: Nam T. Dinh. |
| 502 | 1 |
▼a Thesis (Ph.D.)--North Carolina State University, 2018. |
| 520 | |
▼a The 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 |
| 520 | |
▼a The 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 |
| 520 | |
▼a Five 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 |
| 520 | |
▼a Various 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 |
| 520 | |
▼a The 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 |
| 590 | |
▼a School code: 0155. |
| 650 | 4 |
▼a Nuclear engineering. |
| 650 | 4 |
▼a Mechanical engineering. |
| 650 | 4 |
▼a Aerospace engineering. |
| 690 | |
▼a 0552 |
| 690 | |
▼a 0548 |
| 690 | |
▼a 0538 |
| 710 | 20 |
▼a North Carolina State University.
▼b Nuclear Engineering. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0155 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15001259
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |