Prediction of steam/water stratified flow characteristics in NPPs transients using SVM learning algorithm with combination of thermal-hydraulic model and new data mapping technique-Reference-Cited by-同舟云学术

Prediction of steam/water stratified flow characteristics in NPPs transients using SVM learning algorithm with combination of thermal-hydraulic model and new data mapping technique

Published:2022-02 Issue: Volume:166 Page:108699
ISSN:0306-4549
Container-title:Annals of Nuclear Energy
language:en
Short-container-title:Annals of Nuclear Energy

Author:

Moshkbar-Bakhshayesh Khalil,Ghafari Mohsen

Funder

Sharif University of Technology

Publisher

Elsevier BV

Subject

Nuclear Energy and Engineering

Reference47 articles.

1. Effect of the turbulent contribution of the interfacial momentum transfer on the bubbles dynamics and void fraction distribution in vertical bubbly jets;Aouadi;International Journal of Multiphase Flow,2019

2. CFD based approach for modeling direct contact condensation heat transfer in two-phase turbulent stratified flows;Apanasevich;International Journal of Thermal Sciences,2015

3. The physical closure laws in the CATHARE code;Bestion;Nuclear Engineering and Design,1990

4. Extension of CFD codes application to two-phase flow safety problems;Bestion;Nuclear Engineering and Technology,2010

5. On the modelling of two-phase flow in horizontal legs of a PWR;Bestion;Nuclear Engineering and Technology,2012

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. LSTM-GCN based multidimensional parameter relationship analysis and prediction framework for system level experimental bench;Annals of Nuclear Energy;2025-01

2. A deep learning-based framework for the operation prediction of primary heat transfer loop in nuclear power plants;Frontiers in Energy Research;2023-02-10

3. Flow Pattern Identification of Oil–Water Two-Phase Flow Based on SVM Using Ultrasonic Testing Method;Sensors;2022-08-16

4. Multivariate Time Series Prediction for Loss of Coolant Accidents With a Zigmoid-Based LSTM;Frontiers in Energy Research;2022-04-12