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Modeling Multibody Systems with Uncertainties. Part I: Theoretical and Computational Aspects

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Abstract

This study explores the use of generalized polynomial chaos theory for modeling complex nonlinear multibody dynamic systems in the presence of parametric and external uncertainty. The polynomial chaos framework has been chosen because it offers an efficient computational approach for the large, nonlinear multibody models of engineering systems of interest, where the number of uncertain parameters is relatively small, while the magnitude of uncertainties can be very large (e.g., vehicle-soil interaction). The proposed methodology allows the quantification of uncertainty distributions in both time and frequency domains, and enables the simulations of multibody systems to produce results with “error bars”. The first part of this study presents the theoretical and computational aspects of the polynomial chaos methodology. Both unconstrained and constrained formulations of multibody dynamics are considered. Direct stochastic collocation is proposed as less expensive alternative to the traditional Galerkin approach. It is established that stochastic collocation is equivalent to a stochastic response surface approach. We show that multi-dimensional basis functions are constructed as tensor products of one-dimensional basis functions and discuss the treatment of polynomial and trigonometric nonlinearities. Parametric uncertainties are modeled by finite-support probability densities. Stochastic forcings are discretized using truncated Karhunen-Loeve expansions. The companion paper “Modeling Multibody Dynamic Systems With Uncertainties. Part II: Numerical Applications” illustrates the use of the proposed methodology on a selected set of test problems. The overall conclusion is that despite its limitations, polynomial chaos is a powerful approach for the simulation of multibody systems with uncertainties.

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References

  1. Sandu, A., Sandu, C., Ahmadian, M.: Modeling multibody systems with uncertainties. Part II: Numerical applications. Multibody Syst. Dyn. #(#), #–# (2006)

  2. Dorf, R.C., Bishop, R.H.: Modern Control Systems, 9th edn. Prentice Hall, New Jersey (2001)

  3. Haug, E.J.: Computer Aided Kinematics and Dynamics. Vol I: Basic Methods. Allyn and Bacon, Boston (1989)

  4. Hairer, E., Warner, G.: Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems, 2nd revised edn. Springer, Berlin (1996)

    MATH  Google Scholar 

  5. Caughey, T.K.: Nonlinear theory of random vibrations. In: Advances in Applied Mechanics 11, Academic Press, New York (1971)

    Google Scholar 

  6. Dimentberg, M.F.: An exact solution to a certain non-linear random vibration problem. Int. J. Non-linear Mech. 17(4), 231–234 (1982)

    Google Scholar 

  7. Rubinstein, R.Y.: Simulation and the Monte Carlo Method. John Wiley, New York (1981)

    Google Scholar 

  8. Spanos, P.D., Mignolet, M.D.: Arma Monte Carlo Simulation in Probabilistic Structural Analysis. Shock and Vibration Digest 21, 3–10 (1989)

    Article  Google Scholar 

  9. Evensen, G.: Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte-Carlo methods to forecast error statistics. J. Geophys. Res. 99(C5), (1994)

  10. McKay, M.D., Beckman, R.J., Conover, W.J.: A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics 21(2), 237–240 (1979)

    Article  MathSciNet  Google Scholar 

  11. Bergin, M.S., Noblet, G.S., Petrini, K., Dhieux, J.R., Milford, J.B., Harley, R.A.: Formal uncertainty analysis of a lagrangian photochemical air pollution. Model. Environ. Sci. Technol. 32, 1116–1126 (1999)

    Article  Google Scholar 

  12. Bergin, M., Milford, J.B.: Application of bayesian Monte Carlo analysis to a lagrangian photochemical air quality Model. Atmos. Env. 33(5), 781–792 (2000)

    Article  Google Scholar 

  13. Butler, D.M.: The uncertainty in ozone calculations by a stratospheric photochemistry Model. Geophys. Res. Lett. 5, 769–772 (1978)

    Article  Google Scholar 

  14. Stolarski, R.S.: Uncertainty and sensitivity studies of stratospheric photochemistry. In: Aikin, A.C. (Eds.), Proc. of the NATO Advanced Study Institute on Atmospheric Ozone: Its Variations and Human Influences, pp. 865–876. Rep. FAA-EE-80-20, U.S.D.O.T., Washington D.C. (1980)

  15. Dunker, A.M.: The decoupled direct method for calculating sensitivity coefficients in chemical kinetics. J. Chem. Phys. 81, 2365 (1984)

    Google Scholar 

  16. Nayfeh, A.H.: Perturbation Methods. Wiely-Interscience, London (1973)

    MATH  Google Scholar 

  17. Nayfeh, A.H.: Introduction to Perturbation Techniques. Wiely-Interscience, London (1981)

    MATH  Google Scholar 

  18. Adomian, G.: Stochastic Systems. Academic Press, New York (1983).

    MATH  Google Scholar 

  19. Roberts, J.B., Spanos, P.D.: Random Vibration and Statistical Linearization. John Wiley and Sons, New York (1990)

    MATH  Google Scholar 

  20. Falsone, G.: Stochastic linearization of MDOF systems under parametric excitations. Int. J. Non-linear Mech. 27(6), 1025–1035 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  21. Spanos, P., Ghanem, R.: Boundary element method analysis for random vibration problems. J. Eng. Mech., ASCE 117(2), 389–393 (1991)

    Article  Google Scholar 

  22. Van de Wouw, N., Nijmeijer, H., van Campen, D.H.: A volterra series approach to the approximation of stochastic nonlinear dynamics. Non-linear Dynam. 27, 377–389 (2002)

    MathSciNet  Google Scholar 

  23. Cai, G.Q., Lin, Y.K., Elishakoff, I.: A new approximate solution technique for randomly excited non-linear oscillators. Int. J. Non-linear Mech. 27(6), 969–979 (1992)

    Article  MATH  MathSciNet  Google Scholar 

  24. Tatang, M.A., Pan, W., Prinn, R.G., McRae, G.J.: An efficient method for parametric uncertainty analysis of numerical geophysical models. J. Geophy. Res. 102, 21925–21931 (1997)

    Article  Google Scholar 

  25. McRae, G.: New Directions in Model Based Data Assimilation. MIT Chemical Engineering course 10 (2000)

  26. Isukapalli, S.S., Georgopoulos, P.G.: Propagation of uncertainties in photochemical mechanisms through urban/regional scale grid-based air pollution models. In: Proc. of the A&WMA 90th Annual Meeting. Toronto, Canada (1997)

  27. Isukapalli, S.S., Georgopoulos, P.G.: Computationally efficient methods for uncertainty analysis of environmental models. In: Proc. of the A&WMA Specialty Conference on Computing in Environmental Resource Management, pp. 656–665. A&WMA VIP-68 (1997)

  28. Isukapalli, S.S., Roy, A., Georgopoulos, P.G.: Stochastic response surface methods (SRSMs) for uncertainty propoagation: application to environmental and biological systems. http://www.ccl.rutgers.edu/~ssi/srsmreport/srsm.html (1998)

  29. Isukapalli, S.S., Georgopoulos, P.G.: Development and application of methods for assessing uncertainty in photochemical air quality problems. Interim Report, prepared for the U.S.EPA National Exposure Research Laboratory, under Cooperative Agreement CR 823467 (1998)

  30. Stratonovich, R.L.: Topics in the Theory of Random Noise. Gordon and Breach, New York (1963)

    Google Scholar 

  31. Roberts, J.B., Spanos, P.D.: Stochastic averaging: an approximate method of solving random vibration problems. Int. J. Non-linear Mech. 21(4), 111–133 (1986)

    Article  MATH  MathSciNet  Google Scholar 

  32. Zhu, W.Q.: Stochastic averaging methods in random vibration. Appl. Mech. Rev. 38(5), 189–199 (1988)

    Article  Google Scholar 

  33. Wu, W.F., Lin, Y.K.: Cumulant-neglect closure for non-linear oscillators under random parametric and external excitation. Int. J. Non-linear Mech. 19(4), 337–362 (1994)

    MathSciNet  Google Scholar 

  34. Crandall, S.H.: Non-Gaussian closure techniques for stationary random vibration. Int. J. Non-linear Mech. 20(1), 1–8 (1985)

    Article  MATH  MathSciNet  Google Scholar 

  35. Ghanem, R., Spanos, P.: Stochastic Finite Elements: A Spectral Approach. Springer Verlag (1991)

  36. Ghanem, R., Spanos, P.: A spectral stochastic finite element formulation for reliability analysis. J. Eng. Mech., ASCE 117(10), 2338–2349 (1991)

    Article  Google Scholar 

  37. Wiener, N.: The homogeneous chaos. Amer. J. Math. 60, 897–936 (1936)

    Article  MathSciNet  Google Scholar 

  38. Soize, C., Ghanem, R.: Physical systems with random uncertainties: chaos representations with arbitrary probability measure. SIAM J. Sci. Comp. submitted (2003)

  39. Spanos, P., Ghanem, R.: Stochastic finite element expansion for random media. J. Eng. Mech. ASCE 115(5), 1034–1053 (1989)

    Article  Google Scholar 

  40. Ghanem, R., Spanos, P.: Polynomial chaos in stochastic finite element. J. Appl. Mech. ASME 57(1), 197–202 (1990)

    Article  MATH  Google Scholar 

  41. Ghanem, R., Spanos, P.: A stochastic Galerkin expansion for nonlinear random vibration analysis. Probabilistic Eng. Mech. 8, 255–264 (1993)

    Article  Google Scholar 

  42. Ghanem, R., Spanos, P., Swerdon, S.: Coupled in-line and transverse flow-induced vibration: higher order harmonic solutions. Sadhana, J. Indian Acad. Sci. 20(2–4), 691–707 (1995)

    Google Scholar 

  43. Ghanem, R., Sarkar, A.: Reduced models for the medium-frequency dynamics of stochastic systems. J. Acoust. Soc. Am. 113(2), (2003)

  44. Karniadakis, G.E.: Towards a numerical error bar in CFD, Editorial Article. J. Fluids Eng. (1995)

  45. Jardak, M., Su, C.-H., Karniadakis, G.E.: Spectral polynomial chaos solutions of the stochastic advection equation. J. Sci. Comp. 17(1–4), 319–328 (2002)

    Google Scholar 

  46. Xiu, D., Karniadakis, G.E.: The wiener-askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 24(2), 619–639 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  47. Xiu, D., Karniadakis, G.E.: Modeling uncertainty in steady state diffusion problems via generalized polynomial chaos. Comput. Meth. Appl. Mech. Eng. 191, 4427–4443 (2002)

    Article  MathSciNet  Google Scholar 

  48. Xiu, D., Karniadakis, G.E.: On the well-posedness of generalized polynomial chaos expansions for the stochastic diffusion equation. SIAM J. Numer. Anal., submitted (2003)

  49. Xiu, D., Karniadakis, G.E.: A new stochastic approach to transient heat conduction modeling with uncertainty. Int. J. Heat Mass Tranfer 41, 4181–4193 (2003)

    Google Scholar 

  50. Xiu, D., Karniadakis, G.E.: Uncertainty modeling of burgers equation by generalized polynomial chaos. Computational Stochastic Mechanics. In: Spanos, P.D., Deodatis, G. (eds.) Proc. of the 4th Int. Conf. on Computational Stochastic Mechanics, pp. 655–661. Corfu, Greece (2002), Millpress, Rotterdam (2003)

  51. Xiu, D., Karniadakis, G.E.: Modeling uncertainty in flow simulations via generalized polynomial chaos. J. Comp. Phys. 187, 135–167 (2003)

    MathSciNet  Google Scholar 

  52. Xiu, D., Karniadakis, G.E.: Supersensitivity due to uncertain boundary conditions. Int. J. Numer. Methods Eng., submitted (2003)

  53. Xiu, D., Lucor, D., Su, C.-H., Karniadakis, G.E.: Stochastic modeling of flow-structure interactions using generalized polynomial chaos. J. Fluids Eng. 124, 46–59 (2002)

    Article  Google Scholar 

  54. Lucor, D., Su, C-H., and Karniadakis, G.E.: Generalized polynomial chaos and random oscillators. Int. J. Numer. Methods Eng. submitted (2002)

  55. Lucor, D., Xiu, D., Su, C.-H., Karniadakis, G.E.: Predictability and uncertainty in CFD. Int. J. Num. Meth. Fluids 38(5), 435–455 (2003)

    Google Scholar 

  56. Keese, A.: A review of recent developments in the numerical solution of stochastic partial differential equations (stochastic finite elements). Insitute für Wissenschaftliches Rechnen, Technische Universität Braunschweig (2003)

  57. Cameron, R.H., Martin, W.T.: The orthogonal development of non-linear functionals in series of fourier-hermite functionals. Ann. Math. 43(2), (1942)

  58. Trefethen, L.N., Baw, D., III: Spectral Methods in MATLAB. SIAM (1998)

  59. Mathelin, L., Hussaini, M.Y.: A Stochastic collocation algorithm for uncertainty analysis, NASA/CR-2003-212153 (2003)

  60. Mathelin, L., Hussaini, M.Y., Zang, T.A., Bataille, F.: Uncertainty propagation for turbulent, compressible flow in a quasi-1d nozzle using stochastic methods. In: AIAA 2003–3938, 16th AIAA Computational Fluid Dynamics Conf., June 23–26 Orlando, FL (2003)

  61. Atkinson, K.: An Introduction to Numerical Analysis, 2nd edn. John Wiley & Sons, New York (1989).

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Sandu, A., Sandu, C. & Ahmadian, M. Modeling Multibody Systems with Uncertainties. Part I: Theoretical and Computational Aspects. Multibody Syst Dyn 15, 369–391 (2006). https://doi.org/10.1007/s11044-006-9007-5

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