CHOSUN

심층퍼지신경망을 이용한 임계유동 예측

Metadata Downloads
Author(s)
안예지
Issued Date
2020
Abstract
In order to understand and quantify reactor coolant leakage in nuclear power plants, it is important to identify and simulate the leak phenomena of the fluid. When reactor coolant leaks from a high pressure and temperature reactor coolant system, two-phase mixture or vapor is ejected by the flashing phenomenon and has critical flow features. Therefore, critical flow analysis is needed to identify and quantify the leakage phenomena.
In this study, deep fuzzy neural networks (DFNNs) based on the artificial intelligence methods are developed to predict the critical flow. The total acquired data used for predicting critical flow consist of 5116 input and output data pairs.
The DFNNs refer to a methodology that includes the previously developed cascaded fuzzy neural networks (CFNNs) and the simplified cascade fuzzy neural networks (SCFNNs). The proposed DFNN model uses the data obtained from the Henry–Fauske model to understand the fluid properties and predict the critical flow. This model performed very well when compared to existing the machine learning methods.
It is expected that effective information will be provided to quantify the reactor coolant leak from the DFNN model. Also, the developed DFNN model can be used as a universal standalone program to predict critical flow, which runs faster without iterative calculations, and without a need for steam tables.
Alternative Title
Critical Flow Estimation Using Deep Fuzzy Neural Networks
Alternative Author(s)
YE JI AN
Department
일반대학원 원자력공학과
Advisor
나만균
Awarded Date
2020-02
Table Of Contents
목차
표 목차 ii
그림 목차 iii
ABSTRACT iv

제 1 장 서론 1

제 2 장 Deep Fuzzy Neural Network(DFNN) 모델 3
제 1 절 DFNN 방법론 3
제 2 절 DFNN 모델 최적화 10

제 3 장 DFNN 모델을 사용한 임계유동 예측 15

제 4 장 결론 34

참고문헌 35
Degree
Master
Publisher
조선대학교 대학원
Citation
안예지. (2020). 심층퍼지신경망을 이용한 임계유동 예측.
Type
Dissertation
URI
https://oak.chosun.ac.kr/handle/2020.oak/14166
http://chosun.dcollection.net/common/orgView/200000278252
Appears in Collections:
General Graduate School > 3. Theses(Master)
Authorize & License
  • AuthorizeOpen
  • Embargo2020-02-26
Files in This Item:

Items in Repository are protected by copyright, with all rights reserved, unless otherwise indicated.