Efficient Modeling of Generalized Aerodynamic Forces Across Mach Regimes Using Neuro-Fuzzy Approaches

  • Maximilian WinterEmail author
  • Christian Breitsamter
Conference paper
Part of the Notes on Numerical Fluid Mechanics and Multidisciplinary Design book series (NNFM, volume 132)


In the present work, a nonlinear reduced-order modeling (ROM) approach based on dynamic local linear neuro-fuzzy models is presented for predicting unsteady aerodynamic loads in the time domain. In order to train the input-output relationship between the structural motion and the corresponding flow-induced loads, the local linear model tree (LOLIMOT) algorithm has been implemented. Furthermore, the Mach number is incorporated as an additional input parameter to account for different free-stream conditions with a single model. The approach is applied to the AGARD 445.6 configuration in order to demonstrate the efficiency and fidelity of the proposed method. It is indicated that the ROM-based time domain generalized aerodynamic forces (GAFs) show good agreement with the respective full-order CFD solution (AER-Eu). A further comparison in the frequency domain confirms the validity of the approach. Moreover, the potential of the method for reducing the numerical effort of aeroelastic analyses is highlighted.


Computational Fluid Dynamic Mach Number Proper Orthogonal Decomposition Radial Basis Function Neural Network Aeroelastic Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Dowell, E.H., Hall, K.C.: Modeling of fluid-structure interaction. Annu. Rev. Fluid Mech. 33, 445–490 (2001)Google Scholar
  2. 2.
    Fleischer, D., Breitsamter, C.: Efficient computation of unsteady aerodynamic loads using computational-fluid-dynamics linearized methods. J. Aircraft 50(2), 425–440 (2013)Google Scholar
  3. 3.
    Glaz, B., Liu, L., Friedmann, P.P.: Reduced-order nonlinear unsteady aerodynamic modeling using a surrogate-based recurrence framework. AIAA J. 48(10), 2418–2429 (2010)Google Scholar
  4. 4.
    Kreiselmaier, E., Laschka, B.: Small disturbance euler equations: efficient and accurate tool for unsteady load predictions. J. Aircraft 37(5), 770–778 (2000)Google Scholar
  5. 5.
    Ljung, L.: System Identification: Theory for the User. Prentice Hall, Upper Saddle River, NJ (1999)Google Scholar
  6. 6.
    Lucia, D.J., Beran, P.S., Silva, W.A.: Reduced-order modeling: new approaches for computational physics. Prog. Aerosp. Sci. 40, 51–117 (2004)Google Scholar
  7. 7.
    Marques, F.D., Anderson, J.: Identification and prediction of unsteady transonic aerodynamic loads by multi-layer functionals. J. Fluids Struct. 15, 83–106 (2001)Google Scholar
  8. 8.
    Nelles, O.: Nonlinear System Identification - From Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin (2001)zbMATHGoogle Scholar
  9. 9.
    Silva, W.A., Bartels, R.E.: Development of reduced-order models for aeroelastic analysis and flutter prediction using the cfl3dv6.0 code. J. Fluids Struct. 19, 729–745 (2004)Google Scholar
  10. 10.
    Voitcu, O., Wong, Y.S.: Neural network approach for nonlinear aeroelastic analysis. J. Guid. Control Dynam. 26(1), 99–105 (2003)CrossRefGoogle Scholar
  11. 11.
    Winter, M., Breitsamter, C.: Reduced-order modeling of unsteady aerodynamic loads using radial basis function neural networks. 63rd Deutscher Luft- und Raumfahrtkongress (DGLR Paper 340013), Augsburg, Germany (2014)Google Scholar
  12. 12.
    Wright, J.R., Cooper, J.E.: Introduction to Aircraft Aeroelasticity and Loads. Wiley, West Sussex, England, U.K. (2007)CrossRefGoogle Scholar
  13. 13.
    Yates, E.C., Jr.: AGARD standard aeroelastic configurations for dynamic response. Candidate Configuration I.-Wing 445.6. Tech. rep. NASA TM-100492, NASA (1987)Google Scholar
  14. 14.
    Zhang, W., Wang, B., Ye, Z., Quan, J.: Efficient method for limit cycle flutter analysis by nonlinear aerodynamic reduced-order models. AIAA J. 50(5), 1019–1028 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Institute of Aerodynamics and Fluid MechanicsTechnische Universität MünchenGarchingGermany

Personalised recommendations