TY - CONF A1 - Wang, Bin T1 - Determination of Distribution Function used in MCS on Safety Analysis of Hydrogen Pressure Vessel N2 - The test data of static burst strength and load cycle strength of composite pressure vessels are often described by GAUSSian normal or WEIBULL distribution function to perform safety analyses. The goodness of assumed distribution function plays a significant role in the inferential statistics to predict the population properties by using limited test data. Often, GAUSSian and WEIBULL probability nets are empirical methods used to validate the distribution function; Anderson-Darling and KolmogorovSmirnov tests are the mostly favorable approaches for Goodness of Fit. However, the different approaches used to determine the parameters of distribution function lead mostly to different conclusions for safety assessments. In this study, six different methods are investigated to show the variations on the rates for accepting the composite pressure vessels according to GTR No. 13 life test procedure. The six methods are: a) NormLog 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) The method of moments estimation. In addition, various approaches of ranking function are considered. In the study, Monte Carlo simulations are conducted to generate basic populations based on the distribution functions which are determined using different methods. Then the samples are extracted randomly from a population and evaluated to obtain acceptance rate. Here, the “populations” and “samples” are corresponding to the burst strength or load cycle strength of the pressure vessels made from composite material and a plastic liner (type 4) for the storage of hydrogen. To the end, the results are discussed, and the best reliable methods are proposed. T2 - ICHS2019 Conference CY - Adelaide, Australia DA - 24.09.2019 KW - Monte-Carlo Simulation KW - Distribution function KW - Weibull Distribution PY - 2019 AN - OPUS4-49652 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wang, Bin A1 - Mair, Georg W. A1 - Gesell, Stephan T1 - Determination of Distribution Function used in MCS on Safety Analysis of Hydrogen Pressure Vessel N2 - The test data of static burst strength and load cycle strength of composite pressure vessels are often described by GAUSSian normal or WEIBULL distribution function to perform safety analyses. The goodness of assumed distribution function plays a significant role in the inferential statistics to predict the population properties by using limited test data. Often, GAUSSian and WEIBULL probability nets are empirical methods used to validate the distribution function; Anderson-Darling and Kolmogorov-Smirnov tests are the mostly favorable approaches for Goodness of Fit. However, the different approaches used to determine the parameters of distribution function lead mostly to different conclusions for safety assessments. In this study, six different methods are investigated to show the variations on the rates for accepting the composite pressure vessels according to GTR No. 13 life test procedure. The six methods are: a) 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) The method of moments estimation. In addition, various approaches of ranking function are considered. In the study, Monte Carlo simulations are conducted to generate basic populations based on the distribution functions which are determined using different methods. Then the samples are extracted randomly from a population and evaluated to obtain acceptance rate. Here, the “populations” and “samples” are corresponding to the burst strength or load cycle strength of the pressure vessels made from composite material and a plastic liner (type 4) for the storage of hydrogen. To the end, the results are discussed, and the best reliable methods are proposed. T2 - ICHS International Conference on Hydrogen Safety) 2019 CY - Adelaide, Australia DA - 24.09.2019 KW - Monte-Carlo Simulation KW - Distribution function KW - Weibull Distribution PY - 2019 SP - 103-1 EP - 103-16 AN - OPUS4-50383 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wosniok, Aleksander A1 - Schukar, Marcus A1 - Woody, Paul A1 - Wang, Bin A1 - Breithaupt, Mathias A1 - Kriegsmann, Andreas T1 - Distributed fiber optic strain sensing for structural health monitoring of 70 MPa hydrogen vessels N2 - We report on the development and testing of 70 MPa hydrogen pressure vessels with integrated fiber optic sensing fibers for automotive use. The paper deals with the condition monitoring of such composite pressure vessels (CPVs) using the optical backscatter reflectometry (OBR) applied for a distributed fiber optic strain sensing along fully integrated polyimide-coated single-mode glass optical fiber (SM-GOF). The sensing fibers were embedded into the vessel structure by wrapping them over the polymer liner during the manufacturing process of the carbon fiber reinforced polymer (CFRP). Detecting local strain events by the integrated fiber optic sensors can be an opportunity for monitoring the material degradation of CPVs under static and cyclic loading. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Fiber optic sensor KW - Distributed strain sensing KW - Composite pressure vessel KW - Structural health monitoring KW - Fiber-reinforced plastics PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-603105 SP - 1 EP - 8 PB - NDT.net AN - OPUS4-60310 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Wang, Bin A1 - Mair, Georg W. A1 - Islam, F. T1 - Evaluation methods for estimation of Weibull parameters used in Monte Carlo simulations for safety analysis of pressure vessels 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 - Wang, Bin T1 - Introduction of numerical methods to simulate damage accumulations of composite pressure vessel N2 - The development of hydrogen as a reliable energy sector is strongly connected to the performance and the level of safety of hydrogen storage system. Composite damage due to static, fatigue loading and ageing effect is a progressive process. The common failure modes of composite pressure vessels are majorly fibre break, then interface debonding, matrix cracking and delamination. The damages occur subsequently or even simultaneously, failure modes may interactive each other. These attributes make the composite fatigue more complex and difficult. The presentation here is to show how the numerical methods being developed to match this challenge, particularly the numerical model of composite pressure vessel developed by FibreMod research project is introduced. The potential role of numerical simulation in the certification process and the outlook for the further trend is also discussed. T2 - Abteilungskolloquium CY - BAM Berlin, Germany DA - 10.05.19 KW - Fibre Break KW - Composite Pressure KW - Vessels KW - Simulation PY - 2019 AN - OPUS4-48302 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Li, Yue A1 - Colnaghi, Timoteo A1 - Gong, Yilun A1 - Zhang, Huaide A1 - Yu, Yuan A1 - Wei, Ye A1 - Gan, Bin A1 - Song, Min A1 - Marek, Andreas A1 - Rampp, Markus A1 - Zhang, Siyuan A1 - Pei, Zongrui A1 - Wuttig, Matthias A1 - Ghosh, Sheuly A1 - Körmann, Fritz A1 - Neugebauer, Jörg A1 - Wang, Zhangwei A1 - Gault, Baptiste T1 - Machine learning‐enabled tomographic imaging of chemical short‐range atomic ordering N2 - In solids, chemical short‐range order (CSRO) refers to the self‐organization of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurations of CSRO domains remain elusive. Herein, it is showcased how machine learning‐enhanced atom probe tomography (APT) can mine the near‐atomically resolved APT data and jointly exploit the technique's high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium‐entropy alloy. Multiple CSRO configurations are revealed, with their formation supported by state‐of‐the‐art Monte‐Carlo simulations. Quantitative analysis of these CSROs allows establishing relationships between processing parameters and physical properties. The unambiguous characterization of CSRO will help refine strategies for designing advanced materials by manipulating atomic‐scale architectures. KW - Chemical short-range order (CSRO) KW - Atom probe tomography (APT) KW - Machine learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-623777 DO - https://doi.org/10.1002/adma.202407564 SN - 1521-4095 VL - 36 IS - 44 SP - 1 EP - 9 PB - Wiley-VCH CY - Weinheim AN - OPUS4-62377 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Wang, Bin T1 - Monte Carlo Simulation - Zuverlässigkeit und Plausibilität N2 - Um ein verlässliches Ergebnis der Monte-Carlo-Simulation zu garantieren, muss die Zuverlässigkeit des Tools vor dessen Anwendung sorgfältig geprüft werden. In diesem Vortrag werden die Validierung der angenommenen Verteilungsfunktionen, die Vertrauensbereiche und die Grenzwerte der generierten Grundgesamtheit vorgestellt. Darauf aufbauend wird der Ablauf der gesamten Simulation von der Erzeugung der Zufall-Variablen bis hin zur Akzeptanzrate gezeigt. T2 - Abteilungskolloquium CY - BAM, Berlin, Germany DA - 08.11.2018 KW - Monte Carlo Simulation KW - GAUSSsches Wahrscheinlichkeitsnetz KW - Normalverteilung KW - Weibull-Verteilung KW - Anderson Darling - GoF Test PY - 2018 AN - OPUS4-46666 LA - deu AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mair, Georg W. A1 - Spode, Manfred A1 - Wang, Bin T1 - Monte Carlo simulation and evaluation of burst strength of pressure vessels N2 - The simulation of strength experiments by the Monte-Carlo method enables the numerical generation of data representing a complete populations of composite pressure vessels. In the case of composite pressure vessels used for hydrogen storage, properties like burst strength or fatigue cycle strength are of interest. This paper provides comprehensive information on how populations are generated and how samples can be taken and evaluated; it also explains how to determine the acceptance rate of random samples from simulated populations for passing the approval test "minimum burst pressure". A word of caution is also expressed regarding the evaluation of acceptance rates from a small sample. KW - Monte-Carlo simulation KW - Polar method KW - Composite cylinder KW - Burst strength KW - Acceptance rate PY - 2019 SN - 0025-5300 VL - 61 IS - 12 SP - 1152 EP - 1156 PB - Carl Hanser Verlag CY - München AN - OPUS4-49870 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Mair, Georg W. A1 - Becker, B. A1 - Gesell, Stephan A1 - Wang, Bin T1 - Monte-Carlo-analysis of minimum load cycle requirements for composite cylinders for hydrogen N2 - Hydrogen is an attractive energy carrier that requires high effort for safe storage. For ensuring safety, storage cylinders must undergo a challenging approval process. Relevant standards and regulations for composite cylinders used for the transport of hydrogen and for its onboard storage are currently based on deterministic (e.g. ISO 11119-3) or to some respect semi-probabilistic criteria (UN GTR No. 13; with respect to burst strength). This paper provides a systematic analysis of the load cycle properties resulting from these regulations and standards. Their characteristics are compared with the probabilistic approach of the Federal Institute for Materials Research and Testing BAM. The most important aspect of comparing different concepts is the rate for accepting designs with potentially unsafe or critical safety properties. This acceptance rate is analysed by operating Monte-Carlo simulations over the available range of production properties. T2 - ICHS 2017 CY - Hamburg, Germany DA - 11.09.2017 KW - Safety assessment KW - Failure rate KW - Ageing KW - Degradation KW - End of life KW - Production scatter PY - 2018 DO - https://doi.org/10.1016/j.ijhydene.2018.09.185 SN - 0360-3199 VL - 44 IS - 17 SP - 8833 EP - 8841 PB - Elsevier Ltd AN - OPUS4-46341 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Lengas, Nikolaos A1 - Mair, Georg W. A1 - Müller, Karsten A1 - Sklorz, Christian A1 - Wang, Bin A1 - Natasya, Riska T1 - Overview of experimental testing at BAM: Hydraulic pressure, drop and fire tests N2 - Safe onboard storage is clearly one of the greatest challenges for the hydrogen economy. Even if hydrogen vehicles offer better efficiency, technological barriers remain for short-term implementation. Hydrogen storage difficulties stem from its low density, necessitating very high pressure for storage. In addition, the weight, volume, efficiency, safety of storage as well as the cost of the hydrogen must be considered. Safety is of paramount importance for deployment of hydrogen technologies as it is flammable in a wide range of concentrations with air, more sensitive to ignition due to its low minimum ignition energy, deflagrates faster due to higher burning velocity, and is prone to deflagration-to-detonation transition. Various safety measures must be implemented in order to prevent accidental leakage and ensure inherent safety. Today, the strategy of the OEM’s prioritises the development of a single electrical drivetrain platform where the battery pack is mounted in the underbody of the vehicle. The automotive industry aims to use this same space for hydrogen storage systems, with the expectation that such conformable hydrogen storage systems will be available in the next 3-5 years. The main innovations of BAM’s specialist divisions 3.5 and 8.6 in this project are, firstly, the integration of optical fibres in the filament winding of complete pressure to gain a deeper understanding of the structural behaviour under the different hydraulic and pneumatic loading conditions, and secondly, the development of a fire test platform to test the assembly of 9 tubular vessels under the fire test requirements of GTR13. Therefore, a wind damping and splinter-protecting cage was built from protection modules specifically developed for this project. Extensive safety-related tests are to be carried out at BAM during the project period. T2 - Stakeholders’ Meeting CY - Brussels, Belgium DA - 26.09.2024 KW - Composite pressure vessel KW - Drop test KW - Hydraulic pressure test KW - Fire test PY - 2024 AN - OPUS4-61167 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -