Probabilistic High-Cycle Fatigue Risk Assessment of an Integrally Bladed Rotor

2011 ◽  
Author(s):  
Benjamin D. Hall ◽  
Lauren Gray
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
High Cycle Fatigue ◽  
Probability Distributions ◽  
Direct Monte Carlo Simulation ◽  
Model Parameters ◽  
Cycle Fatigue ◽  
Turbine Engine ◽  
Engineering Models ◽  
Mcmc Simulation

A fully probabilistic high-cycle fatigue (HCF) risk assessment methodology for application to turbine engine blades is described. The assessment uses the Bayesian paradigm of probability theory in which probability distributions are used to encode states of knowledge. Multi-level (or hierarchical) models are employed to capture engineering knowledge of the factors important for assessing HCF risk. This structure allows us to use standard probability distributions to adequately represent uncertainties in model parameters. The model accounts for engine-to-engine, run-to-run, and blade-to-blade variability as well as uncertainty in material capability, usage (flight conditions, time at resonance), and steady and vibratory stresses. Markov chain Monte Carlo (MCMC) simulation is used to fit observed data to the engineering models, then direct Monte Carlo simulation is used to assess the HCF risk.

Comparison of Monte Carlo and Fuzzy Math Simulation Methods for Quantitative Microbial Risk Assessment

2003 ◽  
Vol 66 (10) ◽  
pp. 1900-1910 ◽  
Author(s):  
VALERIE J. DAVIDSON ◽  
JOANNE RYKS
Keyword(s):  
Risk Assessment ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Interval Arithmetic ◽  
Food Systems ◽  
Probability Distributions ◽  
Economic Cost ◽  
Fuzzy Mathematics ◽  
Quantitative Microbial Risk Assessment ◽  
Mean Values

The objective of food safety risk assessment is to quantify levels of risk for consumers as well as to design improved processing, distribution, and preparation systems that reduce exposure to acceptable limits. Monte Carlo simulation tools have been used to deal with the inherent variability in food systems, but these tools require substantial data for estimates of probability distributions. The objective of this study was to evaluate the use of fuzzy values to represent uncertainty. Fuzzy mathematics and Monte Carlo simulations were compared to analyze the propagation of uncertainty through a number of sequential calculations in two different applications: estimation of biological impacts and economic cost in a general framework and survival of Campylobacter jejuni in a sequence of five poultry processing operations. Estimates of the proportion of a population requiring hospitalization were comparable, but using fuzzy values and interval arithmetic resulted in more conservative estimates of mortality and cost, in terms of the intervals of possible values and mean values, compared to Monte Carlo calculations. In the second application, the two approaches predicted the same reduction in mean concentration (−4 log CFU/ml of rinse), but the limits of the final concentration distribution were wider for the fuzzy estimate (−3.3 to 5.6 log CFU/ml of rinse) compared to the probability estimate (−2.2 to 4.3 log CFU/ml of rinse). Interval arithmetic with fuzzy values considered all possible combinations in calculations and maximum membership grade for each possible result. Consequently, fuzzy results fully included distributions estimated by Monte Carlo simulations but extended to broader limits. When limited data defines probability distributions for all inputs, fuzzy mathematics is a more conservative approach for risk assessment than Monte Carlo simulations.

Very high cycle fatigue behaviors of a turbine engine blade alloy at various stress ratios

2017 ◽  
Vol 99 ◽  
pp. 35-43 ◽  
Author(s):  
Kun Yang ◽  
Chao He ◽  
Qi Huang ◽  
Zhi Yong Huang ◽  
Cong Wang ◽  
...  
Keyword(s):  
High Cycle Fatigue ◽  
Cycle Fatigue ◽  
Turbine Engine ◽  
Very High Cycle Fatigue ◽  
Engine Blade ◽  
Stress Ratios ◽  
Blade Alloy ◽  
Very High
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