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Stochastic collocation methods for aeroelastic system with uncertainty

  • Author / Creator
    Deng, Jian
  • Computation methods based on the Wiener chaos expansion have been developed to study the behaviors of the aeroelastic system with randomparameters. It is proven that the discrete wavelet transformation is one ofthe most accurate and efficient numerical schemes for this uncertainty quantizationproblem. In this thesis, we propose the stochastic collocation methods(SCM), whichis a type of sampling method combining the strength of the MonteCarlo simulation and the stochastic Galerkin method. The convergence with respect to the number of the nodal points is investigated, and simulation results to aeroelastic models in the presence of uncertainty in the system parameter and due to the initial condition are reported. It is demonstrated that the accuracy of the SCM is comparable to those achieved by using the wavelet chaos expansion. However, the SCM is more straightforward, efficient and easy to implement.

  • Subjects / Keywords
  • Graduation date
    2009-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3Q60F
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Mathematical and Statistical Sciences
  • Supervisor / co-supervisor and their department(s)
    • Dr. Yau Shu Wong, Department of Mathematical and Statistical Sciences
    • Dr. Christina Adela Popescu, Department of Mathematical and Statistical Sciences
  • Examining committee members and their departments
    • Dr. Christina Adela Popescu, Department of Mathematical and Statistical Sciences
    • Dr. Van Roessel, Henry, Department of Mathematical and Statistical Sciences
    • Dr. Zihui, Xia, Department of Mechanical Engineering
    • Dr. Yau Shu Wong, Department of Mathematical and Statistical Sciences