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Title: OBEST: The Object-Based Event Scenario Tree Methodology

Abstract

Event tree analysis and Monte Carlo-based discrete event simulation have been used in risk assessment studies for many years. This report details how features of these two methods can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology with some of the best features of each. The resultant Object-Based Event Scenarios Tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible (especially those that exhibit inconsistent or variable event ordering, which are difficult to represent in an event tree analysis). Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST method uses a recursive algorithm to solve the object model and identify all possible scenarios and their associated probabilities. Since scenario likelihoods are developed directly by the solution algorithm, they need not be computed by statistical inference based on Monte Carlo observations (as required by some discrete event simulation methods). Thus, OBEST is not only much more computationally efficient than these simulation methods, but it also discovers scenarios that have extremely low probabilities asmore » a natural analytical result--scenarios that would likely be missed by a Monte Carlo-based method. This report documents the OBEST methodology, the demonstration software that implements it, and provides example OBEST models for several different application domains, including interactions among failing interdependent infrastructure systems, circuit analysis for fire risk evaluation in nuclear power plants, and aviation safety studies.« less

Authors:
;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
780308
Report Number(s):
SAND2001-0828
TRN: US0102465
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: 1 Mar 2001
Country of Publication:
United States
Language:
English
Subject:
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS; 32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; 99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE; O CODES; ALGORITHMS; FAILURE MODE ANALYSIS; NUCLEAR POWER PLANTS; RISK ASSESSMENT; SAFETY; COMPUTERIZED SIMULATION; FAULT TREE ANALYSIS; MONTE CARLO METHOD; FIRE HAZARDS; AIRCRAFT; ACCIDENTS

Citation Formats

WYSS, GREGORY D, and DURAN, FELICIA A. OBEST: The Object-Based Event Scenario Tree Methodology. United States: N. p., 2001. Web. doi:10.2172/780308.
WYSS, GREGORY D, & DURAN, FELICIA A. OBEST: The Object-Based Event Scenario Tree Methodology. United States. https://doi.org/10.2172/780308
WYSS, GREGORY D, and DURAN, FELICIA A. 2001. "OBEST: The Object-Based Event Scenario Tree Methodology". United States. https://doi.org/10.2172/780308. https://www.osti.gov/servlets/purl/780308.
@article{osti_780308,
title = {OBEST: The Object-Based Event Scenario Tree Methodology},
author = {WYSS, GREGORY D and DURAN, FELICIA A},
abstractNote = {Event tree analysis and Monte Carlo-based discrete event simulation have been used in risk assessment studies for many years. This report details how features of these two methods can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology with some of the best features of each. The resultant Object-Based Event Scenarios Tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible (especially those that exhibit inconsistent or variable event ordering, which are difficult to represent in an event tree analysis). Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST method uses a recursive algorithm to solve the object model and identify all possible scenarios and their associated probabilities. Since scenario likelihoods are developed directly by the solution algorithm, they need not be computed by statistical inference based on Monte Carlo observations (as required by some discrete event simulation methods). Thus, OBEST is not only much more computationally efficient than these simulation methods, but it also discovers scenarios that have extremely low probabilities as a natural analytical result--scenarios that would likely be missed by a Monte Carlo-based method. This report documents the OBEST methodology, the demonstration software that implements it, and provides example OBEST models for several different application domains, including interactions among failing interdependent infrastructure systems, circuit analysis for fire risk evaluation in nuclear power plants, and aviation safety studies.},
doi = {10.2172/780308},
url = {https://www.osti.gov/biblio/780308}, journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Mar 01 00:00:00 EST 2001},
month = {Thu Mar 01 00:00:00 EST 2001}
}