TY - GEN
T1 - Quanttifying safety margin using the risk-informed safety margin characterization (RISMC)
AU - Grabaskas, David
AU - Bucknor, Matthew
AU - Brunett, Acacia
AU - Nakayama, Marvin
N1 - Publisher Copyright:
© 2015 by the American Nuclear Society.
PY - 2015
Y1 - 2015
N2 - The Risk-Informed Safety Margin Characterization (RISMC), developed by Idaho National Laboratory as part of the Light-Water Reactor Sustainability Project, utilizes a probabilistic safety margin comparison between a load and capacity distribution, rather than a deterministic comparison between two values, as is usually done in best-estimate plus uncertainty analyses. The goal is to determine the failure probability, or in other words, the probability of the system load equaling or exceeding the system capacity. While this method has been used in pilot studies, there has been little work conducted investigating the statistical signcance of the resulting failure probability. In particular, it is dfjIcult to determine how many simulations are necessary to properly characterize the failure probability. This work uses classical (frequentist) statistics and confidence intervals to examine the impact in statistical accuracy when the number of simulations is varied. Two methods are proposed to establish confidence intervals related to the failure probability established using a RISMC analysis. The confidence interval provides information about the statistical accuracy of the method utilized to explore the uncertainty space, and offers a quantitative method to gauge the increase in statistical accuracy due to performing additional simulations.
AB - The Risk-Informed Safety Margin Characterization (RISMC), developed by Idaho National Laboratory as part of the Light-Water Reactor Sustainability Project, utilizes a probabilistic safety margin comparison between a load and capacity distribution, rather than a deterministic comparison between two values, as is usually done in best-estimate plus uncertainty analyses. The goal is to determine the failure probability, or in other words, the probability of the system load equaling or exceeding the system capacity. While this method has been used in pilot studies, there has been little work conducted investigating the statistical signcance of the resulting failure probability. In particular, it is dfjIcult to determine how many simulations are necessary to properly characterize the failure probability. This work uses classical (frequentist) statistics and confidence intervals to examine the impact in statistical accuracy when the number of simulations is varied. Two methods are proposed to establish confidence intervals related to the failure probability established using a RISMC analysis. The confidence interval provides information about the statistical accuracy of the method utilized to explore the uncertainty space, and offers a quantitative method to gauge the increase in statistical accuracy due to performing additional simulations.
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M3 - Conference contribution
AN - SCOPUS:84945137536
T3 - International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2015
SP - 728
EP - 735
BT - International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2015
PB - American Nuclear Society
T2 - 2015 International Topical Meeting on Probabilistic Safety Assessment and Analysis, PSA 2015
Y2 - 26 April 2015 through 30 April 2015
ER -