TY - JOUR A1 - Wang, Bin A1 - Mair, Georg A1 - Islam, F. T1 - Evaluation methods for estimation of Weibull parameters used in Monte Carlo simulations for safety analysis of pressure vessels JF - Material Testing N2 - The test data for static burst strength and load cycle fatigue strength of pressure vessels can often be well described by Gaussian normal or Weibull distribution functions. There are various approaches which can be used to determine the parameters of the Weibull distribution function; however, the performance of these methods is uncertain. In this study, six methods are evaluated by using the criterion of OSL (observed significance level) from Anderson-Darling (AD) goodness of Fit (GoF), These are: a) the norm-log based method, b) least squares regression, c) weighted least squares regression, d) a linear approach based on good linear unbiased estimators, e) maximum likelihood estimation and f) method of moments estimation. In addition, various approaches of ranking function are considered. The results show that there are no outperforming methods which can be identified clearly, primarily due to the limitation of the small sample size of the test data used for Weibull analysis. This randomness resulting from the sampling is further investigated by using Monte Carlo simulations, concluding that the sample size of the experimental data is more crucial than the exact method used to derive Weibull parameters. Finally, a recommendation is made to consider the uncertainties of the limitations due to the small size for pressure vessel testing and also for general material testing. KW - Safety assessment KW - Weibull distribution parameters KW - Randomness KW - Sample size KW - Monte Carlo simulation PY - 2021 DO - https://doi.org/10.1515/mt-2020-0058 VL - 63 IS - 4 SP - 279 EP - 385 PB - De Gruyter AN - OPUS4-53105 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Mair, Georg T1 - Monte-Carlo-analysis of requirements for CGH2-CPVs N2 - This presentation explains how the evaluation of technical requirements in regulations effects safety level. This shown by using the Monte-Carlo experiment consequently. The first step is to explain how deterministic requirements and how probabilistic requirements work. Then the statistical behaviour of the generally unknown basic population can be described and that his behaviour is independent from the regulatory approach. Subsequently, the concept of evaluating sample test results with respect to deterministic requirements is explained. The item “acceptance rate” of the basic population that is behind each sample and behind each individuum in a sample is defined. On this collective understanding, the differences in evaluation between the probabilistic approach PA and the most important RC&S are discussed. Some areas are validated in contradictorily. Then the parameters for acceptance of burst strength in the UN-GTR#13 are varied and opens the door for an accurate improvement of acceptance criteria. Finally, the special aspects of degradation in load cycle testing are shown. There, two aspects must be differentiated, the current reliability and its degradation by ongoing load cycles. This is a convincing argument against the very common opinion, which means that the number of hydraulically tested load cycles pro-vides a figure for the number of acceptable filling cycles. T2 - FiBreMoD Training Unit #5 CY - BAM-FB, Berlin, Germany DA - 05.10.2017 KW - Composite pressure vessels KW - Basic population KW - Acceptance criteria KW - Acceptance rate KW - Sample size KW - Lucky punch area KW - Load cycle test KW - Burst test KW - Scatter PY - 2017 AN - OPUS4-43112 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -