|
Activity Number:
|
498
|
|
Type:
|
Topic Contributed
|
|
Date/Time:
|
Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
| Abstract - #308812 |
|
Title:
|
Bayesian Estimation of Scram Rate Trends in Nuclear Power Plants
|
|
Author(s):
|
Kaushal Mishra*+ and Sujit Ghosh
|
|
Companies:
|
North Carolina State University and North Carolina State University
|
|
Address:
|
1707 Crest Road Apt 4, Raleigh, NC, 27606,
|
|
Keywords:
|
Bayesian Inference ; MCMC ; Zero-inflated Model
|
|
Abstract:
|
Nuclear reactors are equipped with reactor scram systems to ensure rapid shutdown of the system in the event of leaks, failure of power conversion systems, or other operational abnormalities. The U.S. Nuclear Regulatory Commission (NRC) collects data of scram rate for various nuclear power plants to obtain their trend of proper functioning over time and to regulate them. The annual scram data obtained from 66 commercial nuclear power plants indicate an increase in no scram event from 1.5% in 1986 to 33% in 1993. To analyze such a correlated count data with excess zeros, a zero-inflated model that accounts for both temporal and plant to plant variation is being proposed. A wide class of possibly non-nested models was fitted using MCMC methods and compared using a predictive criterion.
|
