Abstract
This work provides the theoretical development and simulation results of a novel Galerkin subspace projection scheme for damped dynamic systems with stochastic coefficients and homogeneous Dirichlet boundary conditions. The fundamental idea involved here is to solve the stochastic dynamic system in the frequency domain by projecting the solution into a reduced finite dimensional spatio-random vector basis spanning the stochastic Krylov subspace to approximate the response. Subsequently, Galerkin weighting coefficients have been employed to minimize the error induced due to the use of the reduced basis and a finite order of the spectral functions and hence to explicitly evaluate the stochastic system response. The statistical moments of the solution have been evaluated at all frequencies to illustrate and compare the stochastic system response with the deterministic case. The results have been validated with direct Monte-Carlo simulation for different correlation lengths and variability of randomness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Adhikari, S., Manohar, C.S.: Dynamic analysis of framed structures with statistical uncertainties. Int. J. Numer. Methods Eng. 44(8), 1157–1178 (1999)
Babuska, I., Tempone, R., Zouraris, G.E.: Galerkin finite element approximations of stochastic elliptic partial differential equations. SIAM J. Numer. Anal. 42(2), 800–825 (2004)
Babuska, I., Tempone, R., Zouraris, G.E.: Solving elliptic boundary value problems with uncertain coefficients by the finite element method: the stochastic formulation. Comput. Methods Appl. Mech. Eng. 194(12–16), 1251–1294 (2005)
Blatman, G., Sudret, B.: An adaptive algorithm to build up sparse polynomial chaos expansions for stochastic finite element analysis. Probab. Eng. Mech. 25(2), 183–197 (2010)
Charmpis, D.C., Schuëller, G.I., Pellissetti, M.F.: The need for linking micromechanics of materials with stochastic finite elements: a challenge for materials science. Comput. Mater. Sci. 41(1), 27–37 (2007)
Falsone, G., Impollonia, N.: A new approach for the stochastic analysis of finite element modelled structures with uncertain parameters. Comput. Methods Appl. Mech. Eng. 191(44), 5067–5085 (2002)
Feng, Y.T.: Adaptive preconditioning of linear stochastic algebraic systems of equations. Commun. Numer. Methods Eng. 23(11), 1023–1034 (2007)
Ganapathysubramanian, B., Zabaras, N.: Sparse grid collocation schemes for stochastic natural convection problems. J. Comput. Phys. 225(1), 652–685 (2007)
Ghanem, R.: The nonlinear Gaussian spectrum of log-normal stochastic processes and variables. J. Appl. Mech. 66, 964–973 (1989)
Ghanem, R., Spanos, P.D.: Stochastic Finite Elements: A Spectral Approach. Springer, New York (1991)
Grigoriu, M.: Galerkin solution for linear stochastic algebraic equations. J. Eng. Mech. 132(12), 1277–1289 (2006)
Kerfriden, P., Gosselet, P., Adhikari, S., Bordas, S.: Bridging the proper orthogonal decomposition methods and augmented Newton–Krylov algorithms: an adaptive model order reduction for highly nonlinear mechanical problems. Comput. Methods Appl. Mech. Eng. 200(5–8), 850–866 (2011)
Khalil, M., Adhikari, S., Sarkar, A.: Linear system identification using proper orthogonal decomposition. Mech. Syst. Signal Process. 21(8), 3123–3145 (2007)
Kleiber, M., Hien, T.D.: The Stochastic Finite Element Method. Wiley, Chichester (1992)
Lenaerts, V., Kerschen, G., Golinval, J.C.: Physical interpretation of the proper orthogonal modes using the singular value decomposition. J. Sound Vib. 249(5), 849–865 (2002)
Li, C.C., Kiureghian, A.D.: Optimal discretization of random fields. J. Eng. Mech. 119(6), 1136–1154 (1993)
Li, C.F., Feng, Y.T., Owen, D.R.J.: Explicit solution to the stochastic system of linear algebraic equations (α 1 A 1+α 2 A 2+⋯+α m A m )x=b. Comput. Methods Appl. Mech. Eng. 195(44–47), 6560–6576 (2006)
Liu, W.K., Belytschko, T., Mani, A.: Random field finite-elements. Int. J. Numer. Methods Eng. 23(10), 1831–1845 (1986)
Ma, X., Zabaras, N.: An adaptive hierarchical sparse grid collocation algorithm for the solution of stochastic differential equations. J. Comput. Phys. 228(8), 3084–3113 (2009)
Matthies, H.G., Keese, A.: Galerkin methods for linear and nonlinear elliptic stochastic partial differential equations. Comput. Methods Appl. Mech. Eng. 194(12–16), 1295–1331 (2005)
Matthies, H.G., Brenner, C.E., Bucher, C.G., Soares, C.G.: Uncertainties in probabilistic numerical analysis of structures and solids—stochastic finite elements. Struct. Saf. 19(3), 283–336 (1997)
Nair, P.B., Keane, A.J.: Stochastic reduced basis methods. AIAA J. 40(8), 1653–1664 (2002)
Nouy, A.: A generalized spectral decomposition technique to solve a class of linear stochastic partial differential equations. Comput. Methods Appl. Mech. Eng. 196(45–48), 4521–4537 (2007)
Nouy, A.: Generalized spectral decomposition method for solving stochastic finite element equations: invariant subspace problem and dedicated algorithms. Comput. Methods Appl. Mech. Eng. 197(51–52), 4718–4736 (2008)
Nouy, A.: Recent developments in spectral stochastic methods for the numerical solution of stochastic partial differential equations. Arch. Comput. Methods Eng. 16, 251–285 (2009). doi:10.1007/s11831-009-9034-5
Papadrakakis, M., Papadopoulos, V.: Robust and efficient methods for stochastic finite element analysis using Monte Carlo simulation. Comput. Methods Appl. Mech. Eng. 134(3–4), 325–340 (1996)
Papoulis, A., Pillai, S.U.: Probability, Random Variables and Stochastic Processes, 4th edn. McGraw-Hill, Boston (2002)
Petyt, M.: Introduction to Finite Element Vibration Analysis. Cambridge University Press, Cambridge (1998)
Sachdeva, S.K., Nair, P.B., Keane, A.J.: Comparative study of projection schemes for stochastic finite element analysis. Comput. Methods Appl. Mech. Eng. 195(19–22), 2371–2392 (2006)
Sachdeva, S.K., Nair, P.B., Keane, A.J.: Hybridization of stochastic reduced basis methods with polynomial chaos expansions. Probab. Eng. Mech. 21(2), 182–192 (2006)
Sarkar, A., Benabbou, N., Ghanem, R.: Domain decomposition of stochastic PDEs: theoretical formulations. Int. J. Numer. Methods Eng. 77(5), 689–701 (2009)
Stefanou, G.: The stochastic finite element method: past, present and future. Comput. Methods Appl. Mech. Eng. 198(9–12), 1031–1051 (2009)
Wan, X.L., Karniadakis, G.E.: Beyond Wiener–Askey expansions: handling arbitrary pdfs. J. Sci. Comput. 27(1–3), 455–464 (2006)
Xiu, D.B., Karniadakis, G.E.: The Wiener–Askey polynomial chaos for stochastic differential equations. SIAM J. Sci. Comput. 24(2), 619–644 (2002)
Xiu, D.B., Karniadakis, G.E.: Modeling uncertainty in flow simulations via generalized polynomial chaos. J. Comput. Phys. 187(1), 137–167 (2003)
Yamazaki, F., Shinozuka, M., Dasgupta, G.: Neumann expansion for stochastic finite element analysis. J. Eng. Mech. 114(8), 1335–1354 (1988)
Zienkiewicz, O.C., Taylor, R.L.: The Finite Element Method, 4th edn. McGraw-Hill, London (1991)
Acknowledgements
AK acknowledges the financial support from the Swansea University through the award for Zienkiewicz scholarship. SA acknowledges the financial support from The Royal Society of London through the Wolfson Research Merit Award.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media Dordrecht
About this chapter
Cite this chapter
Kundu, A., Adhikari, S. (2013). A Novel Reduced Spectral Function Approach for Finite Element Analysis of Stochastic Dynamical Systems. In: Papadrakakis, M., Stefanou, G., Papadopoulos, V. (eds) Computational Methods in Stochastic Dynamics. Computational Methods in Applied Sciences, vol 26. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-5134-7_3
Download citation
DOI: https://doi.org/10.1007/978-94-007-5134-7_3
Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-5133-0
Online ISBN: 978-94-007-5134-7
eBook Packages: EngineeringEngineering (R0)