Skip to main content
Log in

Multi-frequency model reduction for uncertainty quantification in computational vibroacoutics

  • Original Paper
  • Published:
Computational Mechanics Aims and scope Submit manuscript

Abstract

This work is devoted to the vibroacoustics of complex systems over a broad-frequency band of analysis. The considered system is composed of a complex structure coupled with an internal acoustic cavity. On one hand, the global displacements are associated with the main stiff part and on the other hand, the local displacements are associated with the preponderant vibrations of the flexible subparts. Such complex structures induce interweaving of these two types of displacements, which introduce an overlap of the usual three frequency bands (low-, medium- and high-frequency bands (LF, MF, and HF). A reduced-order computational vibroacoustic model is constructed by using a classical modal analysis with the elastic and acoustic modes. Nevertheless, the dimension of such reduced-order model (ROM) is still important when there is an overlap for each one of the three frequency bands. A multi-frequency reduced-order model is then constructed for the structure over the LF, MF, and HF bands. The strategy is based on a multilevel projection consisting in introducing three reduced-order bases that are obtained by using a spatial filtering methodology. To filter out the local displacements in the structure, a set of global shape functions is introduced. In addition, a classical ROM using acoustic modes is carried out for the acoustic cavity. Then, the coupling between the multilevel ROM and the acoustic ROM is presented. A nonparametric probabilistic modeling is then proposed to take into account the model uncertainties induced by modeling errors that increase with the frequency. The proposed approach is applied to a large-scale computational vibroacoustic model of a car.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21

Similar content being viewed by others

References

  1. Durand J, Soize C, Gagliardini L (2008) Structural-acoustic modeling of automotive vehicles in presence of uncertainties and experimental identification and validation. J Acoust Soc Am 123(3):1513–1525. https://doi.org/10.1121/1.2953316

    Article  Google Scholar 

  2. Arnoux A, Batou A, Soize C, Gagliardini L (2013) Stochastic reduced order computational model of structures having numerous local elastic modes in low frequency dynamics. J Sound Vib 332(16):3667–3680. https://doi.org/10.1016/j.jsv.2013..02.019

    Article  Google Scholar 

  3. Arnoux A, Soize C, Gagliardini L (2013) Reduced-order computational model for low-frequency dynamics of automobiles. Adv Mech Eng 31036:1–12. https://doi.org/10.1155/2013/310362

    Article  Google Scholar 

  4. Gagliardini L (2014) Dispersed vibroacoustic responses of industrial products: what are we able to predict? In The 26th international conference on noise and vibration engineering (ISMA2014), Leuven, 15–17 September 2003-01-1555, 17–37

  5. Bucher I, Braun S (1997) Left eigenvectors: extraction from measurements and physical interpretation. J Appl Mech ASME 64(1):97–105

    Article  MathSciNet  MATH  Google Scholar 

  6. Hansen P (1987) The truncated svd as a method for regularization. BIT Numer Math 27(4):534–553

    Article  MathSciNet  MATH  Google Scholar 

  7. Guyan R (1965) Reduction of stiffness and mass matrices. AIAA J 3(2):380–380

    Article  Google Scholar 

  8. Guyan R (1992) A method for selecting master dof in dynamic substructuring using the Guyan condensation method. Comput Struct 45(5–6):941–946

    Google Scholar 

  9. Guyan R (1980) Flexural wave-propagation behavior of lumped mass approximations. Comput Struct 12(6):805–812

    Article  MATH  Google Scholar 

  10. Chan H, Cai C, Cheung Y (1993) High convergence order finite elements with lumped mass matrix. J Sound Vib 165(2):193–207

    Article  MATH  Google Scholar 

  11. Jensen M (1996) Convergence studies of dynamic analysis by using the finite element method with lumped mass matrix. Int J Numer Methods Eng 139(11):1879–1888

    Article  MATH  Google Scholar 

  12. Langley R, Bremner P (1999) A hybrid method for the vibration analysis of complex structural-acoustic systems. J Acoust Soc Am 105(3):1657–1671

    Article  Google Scholar 

  13. Ji L, Mace B, Pinnington R (2006) A mode-based approach for the mid-frequency vibration analysis of coupled long- and short-wavelength structures. J Sound Vib 289(1–2):148–170

    Article  Google Scholar 

  14. Hahn Y, Kikuchi N (2005) Identification of global modeshape from a few nodal eigenvectors using simple free-form deformation. Eng Comput 21(2):115–128

    Article  Google Scholar 

  15. Guyader J (2009) Characterization and reduction of dynamic models of vibrating systems with high modal density. J Sound Vib 328(4–5):488–506

    Article  Google Scholar 

  16. Guyader J (1990) Modal sampling method for the vibration study of systems of high modal density. J Acoust Soc Am 88(5):2269–2276

    Article  Google Scholar 

  17. Noor A, Anderson M, Greene W (1978) Continuum models for beam- and platelike-lattice structures. AIAA J 16(12):1219–1228

    Article  Google Scholar 

  18. Planchard J (1985) Vibrations of nuclear fuel assemblies: a simplified model. Nucl Eng Des 86(3):383–391

    Article  MathSciNet  Google Scholar 

  19. Sigrits J, Broc D (2008) Dynamic analysis of a tube bundle with fluid-structure interaction modelling using a homogenisation method. Comput Methods Appl Mech Eng 197(9–12):1080–1099

    MathSciNet  MATH  Google Scholar 

  20. Craig R (1985) A Review of time domain and frequency domain component mode synthesis method in combined experimental-analytical modeling of dynamic structural systems. D.R. Martinez and A.K. Miller, New York

    Google Scholar 

  21. de Klerk D, Rixen D, Voormeeren S (2008) General framework for dynamic substructuring: history, review, and classification of techniques. AIAA J 4:1169–1181

    Article  Google Scholar 

  22. Leung A (1993) Dynamic stiffness and substructures. Springer, Berlin

    Book  Google Scholar 

  23. Ohayon R, Soize C, Sampaio R (2014) Variational-based reduced-order model in dynamic substructuring of coupled structures through a dissipative physical interface: recent advances. Arch Comput Methods Eng 21(3):321–329. https://doi.org/10.1007/s11831-014-9107-y

    Article  MathSciNet  MATH  Google Scholar 

  24. Argyris J, Kelsey S (1959) The analysis of fuselages of arbitrary cross-section and taper: a DSIR sponsored research program on the development and application of the matrix force method and the digital computer. Aircr Eng Aerosp Technol 31(3):62–74

    Article  Google Scholar 

  25. Przemieniecki J (1963) Matrix structural analysis of substructures. AIAA J 1(1):138–147

    Article  Google Scholar 

  26. Irons B (1965) Structural eigenvalue problems—elimination of unwanted variables. AIAA J 3(5):961–962

    Google Scholar 

  27. Hurty W (1960) Vibrations of structural systems by component mode synthesis. J Eng Mech ASCE 86(4):51–70

    Google Scholar 

  28. Hurty W (1965) Dynamic analysis of structural systems using component modes. AIAA J 3(4):678–685

    Article  Google Scholar 

  29. Craig R, Bampton M (1968) Coupling of substructures for dynamic analyses. AIAA J 6(7):1313–1319

    Article  MATH  Google Scholar 

  30. Bathe K, Gracewski S (1981) On nonlinear dynamic analysis using substructuring and mode superposition. Comput Struct 13(5):699–707

    Article  MATH  Google Scholar 

  31. Farhat C, Geradin M (1994) On a component mode synthesis method and its application to incompatible substructures. Comput Struct 51(5):459–473

    Article  MATH  Google Scholar 

  32. Meirovitch L, Hale A (1981) On the substructure synthesis method. AIAA J 19(7):940–947

    Article  Google Scholar 

  33. Meirovitch L, Kwak M (1991) Rayleigh-ritz based substructure synthesis for flexible multibody systems. AIAA J 29(10):1709–1719

    Article  MATH  Google Scholar 

  34. Voormeeren S, van der Valk P, Rixen D (2011) Generalized methodology for assembly and reduction of component models for dynamic substructuring. AIAA J 49(5):1010–1020

    Article  Google Scholar 

  35. Voormeeren S, van der Valk P, Rixen D (1971) Vibration analysis of structures by component mode substitution. AIAA J 9(7):1255–1261

    Article  Google Scholar 

  36. R.MacNeal,Vibration analysis of structures by component mode substitution, Computers & Structures 1 (4)(1971) 581–601

  37. Rubin S (1975) Improved component-mode representation for structural dynamic analysis. AIAA J 13(8):995–1006

    Article  MATH  Google Scholar 

  38. Markovic D, Park K, Ibrahimbegovic A (2007) Reduction of substructural interface degrees of freedom in flexibility-based component mode synthesis. Int J Numer Methods Eng 70(2):163–180

    Article  MATH  Google Scholar 

  39. Ohayon R, Sampaio R, Soize C (1997) Dynamic substructuring of damped structures using singular value decomposition. J Appl Mech ASME 64(2):292–298

    Article  MATH  Google Scholar 

  40. Park K, Park Y (2004) Dynamic substructuring of damped structures using singular value decomposition. AIAA J 42(6):1236–1245. https://doi.org/10.1115/1.2787306

    Article  Google Scholar 

  41. Rixen D (2004) A dual Craig–Bampton method for dynamic substructuring. J Comput Appl Math 168(1–2):383–391

    Article  MathSciNet  MATH  Google Scholar 

  42. Soize C (2017) Uncertainty quantification. An accelerated course with advanced applications in computational engineering. Springer, New York. https://doi.org/10.1007/978-3-319-54339-0

  43. Ibrahim R (1985) Parametric random vibration. Wiley, New York

    MATH  Google Scholar 

  44. Beck J, Katafygiotis L (1998) Updating models and their uncertainties—I: Bayesian statistical framework. J Eng Mech ASCE 124(4):455–461

    Article  Google Scholar 

  45. Mace R, Worden W, Manson G (2005) Uncertainty in structural dynamics. J Sound Vib 288(3):431–790

    Article  Google Scholar 

  46. Schuëller G, Pradlwarter H (2009) Uncertain linear systems in dynamics: retrospective and recent developments by stochastic approaches. Eng Struct 31(11):2507–2517

    Article  Google Scholar 

  47. Soize C (2013) Stochastic modeling of uncertainties in computational structural dynamics—recent theoretical advances. J Sound Vib 332(10):2379–2395. https://doi.org/10.1016/j.jsv.2011.10.010

    Article  Google Scholar 

  48. Schuëller G (2005) Computational methods in stochastic mechanics and reliability analysis. Comput Methods Appl Mech Eng 194(12–16):1251–1795

    Google Scholar 

  49. Schuëller G (2005) Uncertainties in structural mechanics and analysis—computational methods. Comput Struct 83(14):1031–1150

    Article  Google Scholar 

  50. Schuëller G (2006) Developments in stochastic structural mechanics. Arch Appl Mech 75(10–12):755–773

    Article  MATH  Google Scholar 

  51. Deodatis G, Spanos P (2008) Computational stochastic mechanics. Probab Eng Mech 23(2–3):103–346

    Article  Google Scholar 

  52. Ghanem R (1991) Stochastic finite elements: a spectral approach, Revised. Dover Publications, New York

    Book  MATH  Google Scholar 

  53. Soize C, Ghanem R (2004) Physical systems with random uncertainties: chaos representations with arbitrary probability measure. SIAM J Sci Comput 26(2):395–410. https://doi.org/10.1137/S1064827503424505

  54. Le Maitre O, Knio O (2010) Spectral methods for uncerainty quantification with applications to computational fluid dynamics. Springer, Heidelberg

    MATH  Google Scholar 

  55. Ghanem R, Higdon D, Owhadi H (2017) Handbook of uncertainty quantification. Springer, Cham

  56. Bui-Thanh T, Willcox K, Ghattas O (2008) Parametric reduced-order models for probabilistic analysis of unsteady aerodynamic applications. AIAA J 46(10):2520–2529

    Article  Google Scholar 

  57. Degroote J, Vierendeels J, Willcox K (2010) Interpolation among reduced-order matrices to obtain parameterized models for design, optimization and probabilistic analysis. Int J Numer Methods Eng 63(2):207–230

    MathSciNet  MATH  Google Scholar 

  58. Marzouk Y, Najm H, Rahn L (2007) Stochastic spectral methods for efficient Bayesian solution of inverse problems. J Comput Phys 224(2):560–586

    Article  MathSciNet  MATH  Google Scholar 

  59. Galbally D, Fidkowski K, Willcox K, Ghattas O (2010) Non-linear model reduction for uncertainty quantification in large scale inverse problems. Int J Numer Methods Eng 81(12):1581–1608

    MathSciNet  MATH  Google Scholar 

  60. Lieberman C, Willcox K, Ghattas O (2010) Parameter and state model reduction for large scale statistical inverse problems. SIAM J Sci Comput 32(5):2523–2542

    Article  MathSciNet  MATH  Google Scholar 

  61. Nouy A, Soize C (2014) Random field representations for stochastic elliptic boundary value problems and statistical inverse problems. Eur J Appl Math 25(3):339–373. https://doi.org/10.1017/S0956792514000072

    Article  MathSciNet  MATH  Google Scholar 

  62. Cui T, Marzouk Y, Willcox K (2015) Data-driven model reduction for the Bayesian solution of inverse problems. Int J Numer Methods Eng 102(5):966–990

    Article  MathSciNet  MATH  Google Scholar 

  63. Soize C (2017) Random vectors and random fields in high dimension: parametric model-based representation, identification from data, and inverse problems. In: Ghanem R, Higdon D, Owhadi H (eds) Handbook of uncertainty quantification, vol 2, Springer, Cham, Ch. 26, pp 883–936. https://doi.org/10.1007/978-3-319-11259-6_30-1

  64. Soize C (2000) A nonparametric model of random uncertainties for reduced matrix models in structural dynamics. Probab Eng Mech 15(3):277–299. https://doi.org/10.1016/S0266-8920(99)00028-4

    Article  Google Scholar 

  65. Shannon C (1948) A mathematical theory of communication. Bell Syst Tech J 27(3):379–423

    Article  MathSciNet  MATH  Google Scholar 

  66. Jaynes E (1957) Information theory and statistical mechanics. Phys Rev 106(4):620

    Article  MathSciNet  MATH  Google Scholar 

  67. Mignolet M, Soize C (2008) Nonparametric stochastic modeling of linear systems with prescribed variance of several natural frequencies. Probab Eng Mech 23(2–3):267–278. https://doi.org/10.1016/j.probengmech.2007.12.027

    Article  Google Scholar 

  68. Soize C (2017) Random matrix models and nonparametric method for uncertainty quantification. In: Ghanem R, Higdon D, Owhadi H (eds) Handbook of uncertainty quantification, vol 1, Springer, Cham, pp 219–287. https://doi.org/10.1007/978-3-319-11259-6_5-1

  69. Chen C, Duhamel D, Soize C (2006) Probabilistic approach for model and data uncertainties and its experimental identification in structural dynamics: case of composite sandwich panels. J Sound Vib 294(1–2):64–81. https://doi.org/10.1016/j.jsv.2005.10.013

    Article  Google Scholar 

  70. Capillon R, Desceliers C, Soize C (2016) Uncertainty quantification in computational linear structural dynamics for viscoelastic composite structures. Comput Methods Appl Mech Eng 305:154–172

    Article  MathSciNet  MATH  Google Scholar 

  71. Soize C, Chebli H (2003) Random uncertainties model in dynamic substructuring using a nonparametric probabilistic model. J Eng Mech ASCE 129(4):449–457. https://doi.org/10.1061/(ASCE)0733-9399(2003)129:4(449)

    Article  Google Scholar 

  72. Mignolet M, Soize C, Avalos J (2013) Nonparametric stochastic modeling of structures with uncertain boundary conditions/coupling between substructures. AIAA J 51(6):1296–1308. https://doi.org/10.2514/1.J051555

  73. Arnst M, Clouteau D, Chebli H, Othman R, Degrande G (2006) A non-parametric probabilistic model for ground-borne vibrations in buildings. Probab Eng Mech 21(1):18–34

    Article  Google Scholar 

  74. Ohayon RCS (2014) Advanced computational vibroacoustics—reduced-order models and uncertainty quantification. Cambridge University Press, New York

  75. Capiez-Lernout E, Soize C (2008) Robust design optimization in computational mechanics. J Appl Mech ASME 75(2):1–11. https://doi.org/10.1115/1.2775493

    Article  Google Scholar 

  76. Arnst M, Soize C (2019) Identification and sampling of Bayesian posteriors of high-dimensional symmetric positive-definite matrices for data-driven updating of computational models. Comput Methods Appl Mech Eng 352:300–323. https://doi.org/10.1016/j.cma.2019.04.025

    Article  MathSciNet  MATH  Google Scholar 

  77. Mignolet M, Soize C (2008) Stochastic reduced order models for uncertain geometrically nonlinear dynamical systems. Comput Methods Appl Mech Eng 197(45–48):3951–3963. https://doi.org/10.1016/j.cma.2008.03.032

    Article  MathSciNet  MATH  Google Scholar 

  78. Capiez-Lernout E, Soize C, MP M (2014) Post-buckling nonlinear static and dynamical analyses of uncertain cylindrical shells and experimental validation. Comput Methods Appl Mech Eng 271(1):210–230

    Article  MathSciNet  MATH  Google Scholar 

  79. Soize C, Farhat C (2016) Uncertainty quantification of modeling errors for nonlinear reduced-order computational models using a nonparametric probabilistic approach. Int J Numer Methods Eng 30(2016):96

    Google Scholar 

  80. Ezvan O, Batou A, Soize C (2015) Multilevel reduced-order computational model in structural dynamics for the low-and medium-frequency ranges. Comput Struct 160:111–125. https://doi.org/10.1016/j.compstruc.2015.08.007

    Article  Google Scholar 

  81. Ezvan O, Batou A, Soize C, Gagliardini L (2017) Multilevel model reduction for uncertainty quantification in computational structural dynamics. Comput Mech 59(2):219–246. https://doi.org/10.1007/s00466-016-1348-1

    Article  MathSciNet  MATH  Google Scholar 

  82. Ohayon R, Soize C (1998) Structural acoustics and vibration. Academic Press, San Diego

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Desceliers.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reyes, J., Desceliers, C., Soize, C. et al. Multi-frequency model reduction for uncertainty quantification in computational vibroacoutics. Comput Mech 69, 661–682 (2022). https://doi.org/10.1007/s00466-021-02109-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00466-021-02109-y

Keywords

Navigation