Cart (Loading....) | Create Account
Close category search window
 

Prediction of Axial DNBR Distribution in a Hot Fuel Rod Using Support Vector Regression Models

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Dong Su Kim ; Dept. of Nucl. Eng., Chosun Univ., Gwangju, South Korea ; Sim Won Lee ; Man Gyun Na

The departure from nucleate boiling ratio (DNBR) is one of the most critical parameters in the safety issues of a nuclear reactor. Most reactor core protection systems of current nuclear power plants calculate the minimum DNBR at a pseudo hot fuel rod position to prevent the departure from nucleate boiling (DNB). On the other hand, it gives rise to a more conservative result, which reduces the operating margin of nuclear power plants. In this paper, the axial DNBR distribution at the actual hot fuel rod position was predicted based on the support vector regression (SVR) model, which is a data-based method using a number of measured signals from the reactor coolant system. SVR models were developed using a learning data set and validated by an independent test data set. These models were applied to the first fuel cycle of the Yonggwang unit 3 nuclear power plant. The root mean square (RMS) error averaged for 13 axial locations of the hot rod was 0.87%. The SVR models estimate DNBR values more accurately at central parts that have relatively lower DNBR values, which are more important in terms of safety. This algorithm can predict the DNBR accurately at each time step and provide reliable protection and monitoring information for nuclear power plant (NPP) operation.

Published in:

Nuclear Science, IEEE Transactions on  (Volume:58 ,  Issue: 4 )

Date of Publication:

Aug. 2011

Need Help?


IEEE Advancing Technology for Humanity About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2014 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.