The application of reduced-order modeling (ROM) techniques in the context of aerodynamic nonlinear system identification of realistic aircraft configurations gained increasing attention in recent years. Therefore, in the present study the application of a recurrent neuro-fuzzy model (NFM) that is serial connected with a multilayer perceptron (MLP) neural network is introduced concerning the computation of transonic buffet aerodynamics. In particular, the intention of the ROM is the prediction of coefficient time-series trends in contrast to a precise resolution of detailed flow effects. Further, a reduction of computational time compared to a full-order reference Computational Fluid Dynamics (CFD) solution is pursued. The training of the ROM is accomplished based on a data set computed by means of unsteady Reynolds-averaged Navier-Stokes (URANS) simulations. The performance of the trained ROM is demonstrated by predicting the buffet flow characteristics of the NASA Common Research Model (CRM) investigated at transonic flow conditions. Therefore, the wing of the configuration is excited by an external pitching motion beyond buffet onset. By comparing the ROM result with a reference URANS solution, a precise prediction capability of the aerodynamic characteristics as well as a reduction in computational time is demonstrated. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.     «

The application of reduced-order modeling (ROM) techniques in the context of aerodynamic nonlinear system identification of realistic aircraft configurations gained increasing attention in recent years. Therefore, in the present study the application of a recurrent neuro-fuzzy model (NFM) that is serial connected with a multilayer perceptron (MLP) neural network is introduced concerning the computation of transonic buffet aerodynamics. In particular, the intention of the ROM is the prediction of...     »

The authors gratefully acknowledge the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for funding this work in the framework of the research unit FOR 2895 (Unsteady flow and interaction phenomena at high speed stall conditions), subproject TP7, grant number BR1511/14-1. Further, the authors thank the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing licences and computing time on the GCS Supercomputer SuperMUC-NG at Leibniz Supercomputing Center (www.lrz.de). Also, the authors would like to thank the Institute of Aerodynamics and Gas dynamics (IAG) at the University of Stuttgart for providing the computational grid of the CRM configuration.     «

The authors gratefully acknowledge the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) for funding this work in the framework of the research unit FOR 2895 (Unsteady flow and interaction phenomena at high speed stall conditions), subproject TP7, grant number BR1511/14-1. Further, the authors thank the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding this project by providing computing licences and computing time on the GCS Supercomputer SuperMUC-NG at Leib...     »