Effect of randomness on multi-frequency aeroelastic responses resolved by Unsteady Adaptive Stochastic Finite Elements

The Unsteady Adaptive Stochastic Finite Elements (UASFE) method resolves the effect of randomness in numerical simulations of single-mode aeroelastic responses with a constant accuracy in time for a constant number of samples. In this paper, the UASFE framework is extended to multi-frequency responses and continuous structures by employing a wavelet decomposition pre-processing step to decompose the sampled multi-frequency signals into single-frequency components. The effect of the randomness on the multi-frequency response is then obtained by summing the results of the UASFE interpolation at constant phase for the different frequency components. Results for multi-frequency responses and continuous structures show a three orders of magnitude reduction of computational costs compared to crude Monte Carlo simulations in a harmonically forced oscillator, a flutter panel problem, and the three-dimensional transonic AGARD 445.6 wing aeroelastic benchmark subject to random fields and random parameters with various probability distributions.

[1]  R. Ghanem,et al.  Multi-resolution analysis of wiener-type uncertainty propagation schemes , 2004 .

[2]  R. Ghanem,et al.  Stochastic Finite Elements: A Spectral Approach , 1990 .

[3]  J. Hammersley,et al.  Monte Carlo Methods , 1965 .

[4]  Chris L. Pettit,et al.  Spectral and multiresolution Wiener expansions of oscillatory stochastic processes , 2006 .

[5]  Charbel Farhat,et al.  Second-order time-accurate and geometrically conservative implicit schemes for flow computations on unstructured dynamic meshes , 1999 .

[6]  Jeroen A. S. Witteveen,et al.  An alternative unsteady adaptive stochastic finite elements formulation based on interpolation at constant phase , 2008 .

[7]  Fabio Nobile,et al.  A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data , 2007, SIAM Rev..

[8]  Jeroen A. S. Witteveen,et al.  Efficient Quantification of the Effect of Uncertainties in Advection-Diffusion Problems Using Polynomial Chaos , 2008 .

[9]  BabuskaIvo,et al.  A Stochastic Collocation Method for Elliptic Partial Differential Equations with Random Input Data , 2007 .

[10]  M. Carpenter,et al.  Additive Runge-Kutta Schemes for Convection-Diffusion-Reaction Equations , 2003 .

[11]  Thomas A. Zang,et al.  Stochastic approaches to uncertainty quantification in CFD simulations , 2005, Numerical Algorithms.

[12]  H. Bijl,et al.  Mesh deformation based on radial basis function interpolation , 2007 .

[13]  Menner A Tatang,et al.  Direct incorporation of uncertainty in chemical and environmental engineering systems , 1995 .

[14]  Jeroen A. S. Witteveen,et al.  A Monomial Chaos Approach for Efficient Uncertainty Quantification in Nonlinear Problems , 2008, SIAM J. Sci. Comput..

[15]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[16]  Jeroen A. S. Witteveen,et al.  An unsteady adaptive stochastic finite elements formulation for rigid-body fluid-structure interaction , 2008 .

[17]  Chris L. Pettit,et al.  investigated aeroelastic behaviors arising from variability in three input variables , the initial pitch angle and two stiffness coefficients , 2006 .

[18]  Jeroen A. S. Witteveen,et al.  A TVD uncertainty quantification method with bounded error applied to transonic airfoil flutter , 2009 .

[19]  R. Ghanem,et al.  Uncertainty propagation using Wiener-Haar expansions , 2004 .

[20]  Niels C. Lind,et al.  Methods of structural safety , 2006 .

[21]  Jeroen A. S. Witteveen,et al.  Probabilistic collocation for period-1 limit cycle oscillations , 2008 .

[22]  Philip S. Beran,et al.  Airfoil pitch-and-plunge bifurcation behavior with Fourier chaos expansions , 2005 .

[23]  Jeroen A. S. Witteveen,et al.  An adaptive Stochastic Finite Elements approach based on Newton–Cotes quadrature in simplex elements , 2009 .

[24]  Hester Bijl,et al.  Implicit and Explicit Higher-Order Time Integration Schemes for Fluid-Structure Interaction Computations , 2006 .

[25]  Dongbin Xiu,et al.  The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations , 2002, SIAM J. Sci. Comput..

[26]  Raúl Tempone,et al.  Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations , 2004, SIAM J. Numer. Anal..

[27]  Fabio Nobile,et al.  A Sparse Grid Stochastic Collocation Method for Partial Differential Equations with Random Input Data , 2008, SIAM J. Numer. Anal..

[28]  J. Marsden,et al.  A mathematical introduction to fluid mechanics , 1979 .

[29]  Hester Bijl,et al.  Higher-order time integration through smooth mesh deformation for 3D fluid-structure interaction simulations , 2007, J. Comput. Phys..

[30]  E. Dowell,et al.  Aeroelasticity of Plates and Shells , 1974 .

[31]  I. Babuska,et al.  Solution of stochastic partial differential equations using Galerkin finite element techniques , 2001 .

[32]  H. Bijl,et al.  Implicit and explicit higher order time integration schemes for structural dynamics and fluid-structure interaction computations , 2005 .

[33]  Cv Clemens Verhoosel,et al.  Iterative solution of the random eigenvalue problem with application to spectral stochastic finite element systems , 2006 .

[34]  Khaled H. Hamed,et al.  Time-frequency analysis , 2003 .

[35]  Michel Loève,et al.  Probability Theory I , 1977 .

[36]  Fabio Casciati,et al.  Mathematical Models for Structural Reliability Analysis , 1996 .

[37]  A. Harten High Resolution Schemes for Hyperbolic Conservation Laws , 2017 .

[38]  G. Karniadakis,et al.  An adaptive multi-element generalized polynomial chaos method for stochastic differential equations , 2005 .

[39]  G. Walter Wavelets and other orthogonal systems with applications , 1994 .

[40]  E. Carson Yates,et al.  AGARD standard aeroelastic configurations for dynamic response. Candidate configuration I.-wing 445.6 , 1987 .

[41]  H. Najm,et al.  Uncertainty quantification in reacting-flow simulations through non-intrusive spectral projection , 2003 .