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Metropolis-Hastings method for Wohler curve parameter identification

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this work, the Metropolis-Hastings sampling technique has been used for the parameter identification of Wohler curve of aluminium alloy 2024-T4. The Metropolis-Hasting algorithm is one of the most widespread Markov chain Monte Carlo methods for posterior distribution estimation, and it is presented with an adaptive formulation to estimate the probability density functions of Wohler parameters. Results are presented in terms of distribution shape and parameter correlations. The information about parameter distributions of Wohler equation is useful to prepare risk analyses based on statistical safe life approach.
Rocznik
Strony
143--150
Opis fizyczny
Bibliogr. 12 poz., tab., wykr.
Twórcy
autor
  • Air Force Institute of Technology, Warsaw, Poland
autor
  • Politecnico di Milano, Milan, Italy
autor
  • Politecnico di Milano, Milan, Italy
  • Politecnico di Milano, Milan, Italy
autor
  • Warsaw University of Technology, Warsaw, Poland
Bibliografia
  • [1] Ayyub, B.M. (2011). Vulnerability, Uncertainty, and Risk - Analysis, Modeling, and Management. American Society of Civil Engineers (ASCE).
  • [2] Corbetta, M., Sbarufatti, C., Manes, A. & Giglio, M. (2013). Fatigue crack growth under random spectrum loading: Markov chain Monte Carlo methods for parameter identification. In: European safety and reliability conference (ESREL), Amsterdam; Sept. 29 – Oct. 2.
  • [3] Corbetta, M., Sbarufatti, C., Manes, A. & Giglio, M. (2013). On-line updating of Dynamic StateSpace model for Bayesian filtering through Markov chain Monte Carlo techniques. Chemical Engineering Transactions Vol. 33, 133-138.
  • [4] Corbetta, M., Sbarufatti, C., Manes, A. & Giglio, M. (2014). On Dynamic State-Space models for fatigue-induced structural degradation. Int J fatigue 61, 202-219.
  • [5] DEF STAN 00-970 PART 1/3 SECTION LEAFLET 35 Fatigue. Safe-life substantiation.
  • [6] DOT/FAA/AR-MMPDS-01 (2003) Metallic Materials Properties Development and Standardization (MMPDS) U.S. Department of Transportation, Federal Aviation Administration.
  • [7] Fassò, A. & Perri, P.F. (2002). Sensitivity analyssi. In Encyclopedia of Environmetrics Edited by El-Shaarawi A H, Piegorsch W W, Vol. 4, 1968-1982. John Wiley & Sons, Ltd, Chichester, UK.
  • [8] Haario, H., Saksman, E. & Tamminen, J., (2001). An adaptive metropolis algorithm. Bernoulli 7, 223-242.
  • [9] Leski, A., Klimaszewski, S., Baraniecki, R., Malinowski, L. & Reymer, P. (2009). Oszacowanie indywidualnego zuŜycia zmęczeniowego struktur określonej populacji statków powietrznych. ITWL, Warsaw.
  • [10] Mattrand, C., Bourinet, J.M. & Theret, D. (2011). Analysis of Fatigue Crack Growth under Random Load Sequences Derived from Military In-flight Load Data. 26th ICAF Symposium, Montreal
  • [11] Montgomery, D.C., Runger, G.C. & Hubele, N.F. (2010). Engineering statistics, fifth edition. John Wiley & Sons Inc. New York, USA.
  • [12] Sbarufatti, C. (2013). Fatigue crack monitoring of helicopter fuselages and life evaluation through sensor network Politecnico di Milano, Dipartimento di Meccanica, PhD thesis.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-65c475a9-62ac-4872-85c7-7f3dc63a7d29
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