DeepAI AI Chat
Log In Sign Up

Characterizing the Probability Law on Time Until Core Damage With PRA

by   Martin Wortman, et al.

Certain modeling assumptions underlying Probabilistic Risk Assessment (PRA) allow a simple computation of core damage frequency (CDF). These assumptions also guarantee that the time remaining until a core damage event follows an exponential distribution having parameter value equal to that computed for the CDF. While it is commonly understood that these modeling assumptions lead to an approximate characterization of uncertainty, we offer a simple argument that explains why the resulting exponential time until core damage distribution under-estimates risk. Our explanation will first review operational physics properties of hazard functions, and then offer a non-measure-theoretic argument to reveal the the consequences of these properties for PRA. The conclusions offered, here, hold for any possible operating history that respects the underlying assumptions of PRA. Hence, the measure-theoretic constructs on filtered probability spaces is unnecessary for our developments. We will then conclude with a brief discussion that connects intuition with our analytical development.


page 1

page 2

page 3

page 4


Is Core Damage Frequency an Informative Risk Metric?

Core Damage Frequency (CDF) is a risk metric often employed by nuclear r...

Predictions of damages from Atlantic tropical cyclones: a hierarchical Bayesian study on extremes

Bayesian hierarchical models are proposed for modeling tropical cyclone ...

BioLeaf: a professional mobile application to measure foliar damage caused by insect herbivory

Soybean is one of the ten greatest crops in the world, answering for bil...

A quantitative analysis of the 2017 Honduran election and the argument used to defend its outcome

The Honduran incumbent president and his administration recently declare...

Fragmentation analysis of a bar with the Lip-field approach

The Lip-field approach is a new way to regularize softening material mod...

What is an OS?

While the engineering of operating systems is well understood, their for...

Deep Transformer Networks for Time Series Classification: The NPP Safety Case

A challenging part of dynamic probabilistic risk assessment for nuclear ...