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SICODYN international benchmark on dynamic analysis of structure assemblies: variability and numerical-experimental correlation on an industrial pump (part 2)

Published online by Cambridge University Press:  07 April 2014

Sylvie Audebert ,
Alexeï Mikchevitch  and
Irmela Zentner
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Abstract

An international benchmark has been organized by EDF R&D during 2008–2010, gathering 11 partners. Its main objective is to quantify the confidence in numerical models. The built-up dynamical system considered is a pump actually in service in power plants, considered in its work environment. Blind eigenfrequency numerical values relative to the pump assembly fixed in concrete present a larger variability than the 5% rough variability concerning the separate parts, essentially due to the modeling of the boundary conditions and interfaces at macro level. For the nine first eigenmodes considered, experimental-numerical correlation shows a frequency error less than 15% for the free sub-structures and nearly 30% for the free five-component system. Though the first overall modes are correctly identified, the frequency error is significantly larger for the clamped pump assembly, with however MAC coefficients higher than 0.9 for two modes; but the frequency error can be reduced to less than 9% for the four first modes after updating procedure. A lesson to draw is that measurement information is needed to improve the quality of theoretical built-up structure. After this benchmark, a more ambitious research program will follow as a FUI project, gathering the observation of numerical and experimental variabilities, updating model improvement and numerical quantification of the total uncertainty.

Keywords

Benchmarkbuilt-up structuremodal analysisnumerical-experimental correlationnumerical variability
Type
Research Article
Information
Mechanics & Industry , Volume 15 , Issue 1 , 2014 , pp. 1 - 17
Copyright
© AFM, EDP Sciences 2014

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References

Trucano, T.G., Swiler, L.P., Igusa, T., Oberkampf, W.L., Pilch, M., Calibration, validation, and sensitivity analysis: What’s what, Reliab. Eng. Syst. Saf. 91 (2006) 1331–1357 CrossRefGoogle Scholar
Oberkampf, W.L., Trucano, T.G., Verification and validation benchmarks, Nucl. Eng. Design 238 (2008) 716–743 CrossRefGoogle Scholar
Zang, C., Schwingshackl, C.W., Ewins, D.J., Model validation for structural dynamic analysis: An approach to the Sandia Structural Dynamic Challenge, Comput. Methods Appl. Mech. Engrg. 197 (2008) 2645–2659 CrossRefGoogle Scholar
Hills, R.G., Pilch, M., Dowding, K.J., Red-Horse, J., Paez, T.L., Babuska, I., Tempone, R., Validation Challenge Workshop, Comput. Methods Appl. Mech. Engrg. 197 (2008) 2375–2380 CrossRefGoogle Scholar
Paez, T.L, Red-Horse, J., Structural dynamics challenge problem: Summary, Comput. Methods Appl. Mech. Engrg. 197 (2008) 2660–2665 CrossRefGoogle Scholar
Red-Horse, J., Pae, T.L, Sandia National Laboratories Validation workshop: Structural dynamics application, Comput. Methods Appl. Mech. Engrg. 197 (2008) 2578–2584 CrossRefGoogle Scholar
Link, M., Friswell, M., Working group 1: generation of validated structural dynamic models – Results of a benchmark study utilising the GARTEUR SM-AG19 test-bed, Mech. Syst. Signal Process. 17 (2003) 9–20 CrossRefGoogle Scholar
Atamturkur, S., Hemez, F.M., Laman, J.A., Uncertainty quantification in model verification and validation as applied to large scale historic masonry monuments, Eng. Struct. 43 (2012) 221–234 CrossRefGoogle Scholar
M.H. de A. Carqueja, J.D. Riera, Model uncertainty in the determination of dynamic response of generator foundation, in: Michel Livolant; The International Association for Structural Mechanics in Reactor Technology (IASMIRT) (eds.), Transactions of the 14th International Conference on Structural Mechanics on Reactor technology SMiRT14 (1997), Lyon, France, 1997, pp. 95–102
Audebert, S., SICODYN International benchmark on dynamic analysis of structure assemblies: variability and numerical-experimental correlation on an industrial pump, Mécanique et Industries 11 (2010) 439–451 CrossRefGoogle Scholar
Kim, Jeong, Yoon, Joo-Cheol, Kang, Beom-Soo, Finite element analysis and modeling of structure with bolted joints, Appl. Math. Modell. 31 (2007) 895–911 CrossRefGoogle Scholar
Mackerle, J., Finite element analysis of fastening and joining: a bibliography (1990–2002), Int. J. Press. Vessels Piping 80 (2003) 253–271 CrossRefGoogle Scholar
Dane Quinn, D., Modal analysis of joined structures, J. Sound Vib. 331 (2012) 81–93 CrossRefGoogle Scholar
Gallina, A., Lisowski, W., Pichler, L., Strachowski, A., Uhl, T., Analysis of natural frequency variability of a brake component, Mech. Syst. Signal Process. 32 (2012) 188–199 CrossRefGoogle Scholar
Code Aster, general public licensed structural mechanics finite element software, http://www.code-aster.org/
S. Audebert, I. Zentner, A. Mikchevitch, Variability and propagation of uncertainties on modal simulations of a built-up structure (SICODYN benchmark), in: G. de Roeck, G. Degrande, G. Lombaert, G. Müller (eds.), Proceedings of the 8th International Conference on Structural Dynamics, ISBN 978–90–760–1931–4, EURODYN 2011, Leuwen, Belgium, 2011, pp. 3000–3007
Sacks, et al., Design and analysis of computer experiments, Stat. Sci. 4 (1989) 409–423 CrossRefGoogle Scholar
I. M. Sobol’, V.I. Turchaninov, Y. L. Levitan, B.V. Shukhman, Quasirandom sequence generators, Ipm zak. no. 30, Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Moscow, 1992
DACE. A Matlab Kriging Toolbox. S. Lophaven et al., Technical report IMM-TR-2002–12, 2002
E. de Rocquigny, N. Devictor, S. Tarantola, Uncertainty in industrial practice. A guide to quantitative uncertainty management, Wiley & Sons eds. Chichester, England, 2008
Zentner, I., Tarantola, S., de Rocquigny, E., Sensitivity analysis for reliable design verification of nuclear turbosets, Reliab. Eng. Syst. Saf. 96 (2010) 391–397 CrossRefGoogle Scholar
A. Saltelli, M. Ratto, T. Andres, E. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, S. Tarantola, Global sensitivity analysis, The primer, Wiley, 2008
Gant, F., Rouch, P., Louf, F., Champaney, L., Definition and updating of simplified models of joint stiffness, Int. J. Solids Struct. 48 (2011) 775–784 CrossRefGoogle Scholar
F. Gant, L. Champaney, P. Rouch, Modeling of the bolted joint behavior variability with the Lack of Knowledge theory, in: ICCES Organizing Committee, Tech. Science Press (ed.), ISSN:1933–2815, ICCES 2010 International Conference on Computational and Experimental Engineering and Sciences, Las Vegas, USA, 2010, vol. 14, pp. 97–98
Roy, C.J., Oberkampf, W.L., A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing, Comput. Methods Appl. Mech. Engrg. 200 (2011) 2131–2144 CrossRefGoogle Scholar
Unal, C., Williams, B., Hemez, F., Atamturktur, S.H., Mc Clure, P., Improved best estimate plus uncertainty methodology, including advanced validation concepts, to licence evolving nuclear reactors, Nucl. Eng. Design 241 (2011) 1813–1833 CrossRefGoogle Scholar
Atamturktur, S., Hemez, F., Williams, B., Tome, C., Unal, C., A forecasting metric for predictive modelling, Comput. Struct. 89 (2011) 2377–2387 CrossRefGoogle Scholar
Langenbrunner, J.R., Hemez, F.M., Booker, J.M., Ross, T.J., Model choice considerations and information integration using analytical hierarchy process, Sixth International Conference on Sensitivity Analysis of Model Output, Procedia Social and Behavioral Sciences 2 (2010) 7700–7701 CrossRefGoogle Scholar
Pilch, M., Trucano, T.G., Helton, J.C., Ideas underlying the quantification of margins and uncertainties, Reliab. Eng. Syst. Saf. 96 (2011) 965–975 CrossRefGoogle Scholar
Batou, A., Soize, C., Corus, M., Experimental identification of an uncertain computational dynamical model representing a family of structures, Comput. Struct. 89 (2011) 1440–1448 CrossRefGoogle Scholar
Oberkampf, W.L., Barone, M.F., Measures of agreement between computation and experiment: validation metrics, J. Comput. Phys. 217 (2006) 5–36 CrossRefGoogle Scholar
Van den Nieuwenhof, B., Coyette, J.P., Modal approaches for the stochastic finite element analysis of structures with material and geometry uncertainties, Comput. Methods Appl. Mech. Engrg. 192 (2003) 3705–3729 CrossRefGoogle Scholar
Kammer, D.C., Nimityongskul, S., Propagation of uncertainty in test-analysis correlation of substructured spacecraft, J. Sound Vib. 330 (2011) 1211–1224 CrossRefGoogle Scholar
Guedri, M., Cogan, S., Bouhaddi, N., Robustness of structural reliability analyses to epistemic uncertainties, Mech. Syst. Signal Process. 28 (2012) 458–469 CrossRefGoogle Scholar
Der Kiureghian, A.K., Ditlevsen, O., Aleatory or epistemic? Does it matter? Struct. Safe. 31 (2009) 105–111 Google Scholar
L. Hinke, Modelling approaches for the low-frequency analysis of built-up structures with non-deterministic properties, Master Thesis, University of Southampton, Faculty of Engineering, Science and Mathematics, Institute of Sound and Vibration Research, 2008
Mace, B.R., Shorter, P.J., A local modal perturbational method for estimating frequency response statistics of built-up structures with uncertain properties, J. Sound Vib. 242 (2001) 793–811CrossRefGoogle Scholar