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M. Winter, C. Breitsamter
In the present work, an efficient surrogate-based framework is developed for the prediction of motion-induced surface pressure fluctuations. The model construction is realized by performing forced-motion computational fluid dynamics (CFD) simulations, while the result is processed via the proper orthogonal decomposition (POD) to obtain the most relevant flow modes. Subsequently, a nonlinear system identification is carried out with respect to the applied excitation signal and the corresponding POD coefficients. For the input/output model identification task, a recurrent local linear neuro-fuzzy approach is employed in order to capture the linear and nonlinear characteristics of the dynamic system. Once the reduced-order model (ROM) is trained, it can substitute the flow solver within an aeroelastic simulation framework for a given configuration at a fixed set of free-stream conditions. For demonstration purposes, the ROM approach is applied to the LANN wing in transonic flow. Due to the characteristic lambda-shock system, the unsteady aerodynamic surface pressure distribution is dominated by nonlinear effects. Preliminary numerical investigations show a good correlation between the results obtained by the ROM methodology in comparison to the full-order CFD solution. In addition, the surrogate approach yields a significant speed-up regarding unsteady aerodynamic calculations, which is beneficial for aeroelastic applications, local load estimation, and multidisciplinary computations.
Deutscher Luft- und Raumfahrtkongress 2015, Rostock
Verlag, Ort:
Deutsche Gesellschaft für Luft- und Raumfahrt - Lilienthal-Oberth e.V., Bonn, 2015
Conference Paper
21,0 x 29,7 cm, 11 Seiten
Stichworte zum Inhalt:
Aeroelastik, Vereinfachte Strömungsmodelle
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