Discrete probability distributions for probabilistic fracture mechanics
Recently, discrete probability distributions (DPDs) have been suggested for use in risk analysis calculations to simplify the numerical computations which must be performed to determine failure probabilities. Specifically, DPDs have been developed to investigate probabilistic functions, that is, functions whose exact form is uncertain. The analysis of defect growth in materials by probabilistic fracture mechanics (PFM) models provides an example in which probabilistic functions play an important role. This paper compares and contrasts Monte Carlo simulation and DPDs as tools for calculating material failure due to fatigue crack growth. For the problem studied, the DPD method takes approximately one third the computation time of the Monte Carlo approach for comparable accuracy. It is concluded that the DPD method has considerable promise in low-failure-probability calculations of importance in risk assessment. In contrast to Monte Carlo, the computation time for the DPD approach is relatively insensitive to the magnitude of the probability being estimated.
- Research Organization:
- Battelle Columbus Lab., Columbus, OH
- OSTI ID:
- 6992809
- Journal Information:
- Risk Anal.; (United States), Journal Name: Risk Anal.; (United States) Vol. 5:3; ISSN RIAND
- Country of Publication:
- United States
- Language:
- English
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Related Subjects
220900* -- Nuclear Reactor Technology-- Reactor Safety
ALLOYS
CARBON STEELS
CHROMIUM ALLOYS
COMPUTERIZED SIMULATION
COOLING SYSTEMS
CORROSION RESISTANT ALLOYS
CRACK PROPAGATION
DISTRIBUTION FUNCTIONS
ENERGY SYSTEMS
FAILURE MODE ANALYSIS
FAILURES
FRACTURE MECHANICS
FUNCTIONS
IRON ALLOYS
IRON BASE ALLOYS
MECHANICS
MONTE CARLO METHOD
PIPES
PROBABILITY
REACTOR COMPONENTS
REACTOR COOLING SYSTEMS
RISK ASSESSMENT
SIMULATION
STAINLESS STEELS
STEELS
STRESS ANALYSIS
SYSTEM FAILURE ANALYSIS
SYSTEMS ANALYSIS