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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)

Abstract

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.

Keywords

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.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

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

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