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Polynomial chaos expansion method in estimating probability distribution of rotor-shaft dynamic responses

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The main purpose of the study is an assessment of computational efficiency of selected numerical methods for estimation of vibrational response statistics of a large multi-bearing turbo-generator rotor-shaft system. The effective estimation of the probability distribution of structural responses is essential for robust design optimization and reliability analysis of such systems. The analyzed scatter of responses is caused by random residual unbalances as well as random stiffness and damping parameters of the journal bearings. A proper representation of these uncertain parameters leads to multidimensional stochastic models. Three estimation techniques are compared: Monte Carlo sampling, Latin hypercube sampling and the sparse polynomial chaos expansion method. Based on the estimated values of the first four statistical moments the probability density function of the maximal vibration amplitude is evaluated by the maximal entropy principle method. The method is inherently suited for an accurate representation of the probability density functions with an exponential behavior, which appears to be characteristic for the investigated rotor-shaft responses. Performing multiple numerical tests for a range of sample sizes it was found that the sparse polynomial chaos method provides the best balance between the accuracy and computational effectiveness in estimating the unknown probability distribution of the maximal vibration amplitude.
Rocznik
Strony
413--422
Opis fizyczny
Bibliogr. 34 poz., rys., wykr.
Twórcy
autor
  • Institute of Fundamental Technological Research, Polish Academy of Sciences, 5B Pawińskiego St., 02-106 Warsaw, Poland
autor
  • Institute of Aviation, 110/114 Krakowska Av., 02-256 Warsaw, Poland
autor
  • Institute of Fundamental Technological Research, Polish Academy of Sciences, 5B Pawińskiego St., 02-106 Warsaw, Poland
autor
  • Institute of Fundamental Technological Research, Polish Academy of Sciences, 5B Pawińskiego St., 02-106 Warsaw, Poland
Bibliografia
  • [1] Y. Tsompanakis, N.D. Lagaros, and M. Papadrakakis, Structural Design Optimization Considering Uncertainties, Structures and Infrastructures Series, Taylor and Francis, London, 2007.
  • [2] Z. Kang, “Robust design optimization of structures under uncertainty”, PhD Thesis, Institut f¨ur Statik und Dynamik der Luft- und Raumfahrkonstruktionen Universit¨at Stuttgart, Stuttgart, 2005.
  • [3] H.-G. Beyer and B. Sendhoff, “Robust optimization - a comprehensive survey”, Computer Methods in Applied Mechanics and Engineering 196, 3190-3218 (2007).
  • [4] J.C. Helton and F.J. Davis, “Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems”, Reliability Engineering and System Safety 81, 23-69 (2003).
  • [5] B. Hu and X. Du, “Analytical robustness assessment for robust design”, Structural and Multidisciplinary Optimization 34, 123-137 (2007).
  • [6] S.H. Lee and W. Chen, “A comparative study of uncertainty propagation methods for black-box-type problems”, Structural and Multidisciplinary Optimization 37, 239-253 (2009).
  • [7] R. Stocki, P. Tauzowski, and M. Kleiber, “Efficient sampling techniques for stochastic simulation of structural systems”, Computer Assisted Mechanics and Engineering Sciences 14, 127-140 (2007).
  • [8] B. Blachowski and W. Gutkowski, “Graph based discrete optimization in structural dynamics”, Bull. Pol. Ac.: Tech. 62, 91-102 (2014).
  • [9] K. Sobczyk, “Stochastic dynamics and reliability of degrading systems”, Bull. Pol. Ac.: Tech. 54, 125-136 (2006).
  • [10] M. Liefvendahl and R. Stocki, “A study on algorithms for optimization of Latin hypercubes”, J. Statistical Planning and Inference 136 (9), 3231-3247 (2006).
  • [11] M. Bulik, M. Liefvendahl, R. Stocki, and C. Wauquiez, “Stochastic simulation for crashworthiness”, Advances in Engineering Software 35, 791-803 (2004).
  • [12] R.G. Ghanem and P.D. Spanos, Stochastic Finite Elements: A Spectral Approach, Springer, Berlin, 1991.
  • [13] G. Blatman and B. Sudret, “An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis”, Probabilistic Engineering Mechanics 25, 183-197 (2010).
  • [14] D. Xiu and G.E. Karniadakis, “Modeling uncertainty in flow simulations via generalized polynomial chaos”, J. Computational Physics 187, 137-167 (2003).
  • [15] P. Prempraneerach, F.S. Hover, M.S. Triantafyllou, and G.E. Karniadakis, “Uncertainty quantification in simulations of power systems: multi-element polynomial chaos methods”, Reliability Engineering and System Safety 95, 632-646 (2010).
  • [16] J. Didier, B. Faverjon, and J. Sinou, “Analysing the dynamic response of a rotor system under uncertain parameters by polynomial chaos expansion”, J. Vibration and Control 80, 712-732 (2012).
  • [17] O. Ditlevsen and H.O. Madsen, Structural Reliability Methods, Wiley, London, 1996.
  • [18] P.-L. Liu and A. Der Kiureghian, “Multivariate distribution models with prescribed marginals and covariances”, Probabilistic Engineering Mechanics 1 (2), 105-112 (1986).
  • [19] S.-K. Choi, R.V. Grandhi, and R.A. Canfield, “Structural reliability under non-gaussian stochastic behavior”, Computers and Structures 82, 1113-1121 (2004).
  • [20] Z. Xi, C. Hu, and B.D. Youn, “A comparative study of probability estimation methods for reliability analysis”, Structural and Multidisciplinary Optimization 45, 33-52 (2012).
  • [21] E. Saliby, “Descriptive sampling: an improvement over latin hypercube sampling”, eds. S. Andradottir, K.J. Healy, D.H. Withers, and B.L. Nelson, Proc. Winter Simulation Conf. 1, 230-233 (1997).
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  • [23] B. Sudret, “Uncertainty propagation and sensitivity analysis in mechanical models - contributions to structural reliability and stochastic spectral methods”, Habilitation `a Diriger des Recherches, Universit´e Blaise Pascal, Aubi`ere, 2007.
  • [24] A. Nataf, “Determination des distribution dont les marges sont donnees”, Comptes Rendus de l’Academie des Sciences 225, 42-43 (1962).
  • [25] W.H. Press, B.P. Flannery, S.A. Teukolsky, and W.T. Vetterling, Numerical Recipes, Cambridge University Press, Cambridge, 1988.
  • [26] M. Berveiller, B. Sudret, and M. Lemaire, “Stochastic finite element: a non-intrusive approach by regression”, Revue Europ ´eenne de M´ecanique Num´erique 15, 81-92 (2006).
  • [27] G. Blatman, “Adaptive sparse polynomial chaos expansion for uncertainty propagation and sensitivity analysis”, PhD Thesis, Universit´e Blaise Pascal, Clermont-Ferrand, 2009.
  • [28] E.T. Jaynes, “Information theory and statistical mechanics”, Physical Reviews 106, 361-373 (1957).
  • [29] M. Srikanth, H.K. Kesavan, and P.H. Roe, “Probability density function estimation using the MinMax measure”, IEEE Trans. Systems, Man, and Cybernetics 30, 77-83 (2000).
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  • [31] T. Szolc, “On the discrete-continuous modeling of rotor systems for the analysis of coupled lateral-torsional vibrations”, Int. J. Rotating Machinery 6 (2), 135-149 (2000).
  • [32] T. Szolc, P. Tauzowski, J. Knabel, and R. Stocki, “Nonlinear and parametric coupled vibrations of the rotor-shaft system as fault identification symptom using stochastic methods”, Nonlinear Dynamics 57, 533-557 (2009).
  • [33] T. Szolc, P. Tauzowski, R. Stocki, and J. Knabel, “Damage identification in vibrating rotor-shaft systems by efficient sampling approach”, Mechanical Systems and Signal Processing 23, 1615-1633 (2009).
  • [34] R. Stocki, T. Szolc, P. Tauzowski, and J. Knabel, “Robust design optimization of the vibrating rotor shaft system subjected to selected dynamic constraints”, Mechanical Systems and Signal Processing 29, 34-44 (2012).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b7523d61-e814-45b4-9b18-67e0d08c81c1
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