Abstract
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.
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References
Timme, S.: Global instability of wing shock-buffet onset. J. Fluid Mech. 885 (2020)
Iovnovich, M., Raveh, D.E.: Numerical study of shock buffet on three-dimensional wings. AIAA J. 53(2), 449–463 (2014)
Crouch, J.D., Garbaruk, A., Strelets, M.: Global instability analysis of unswept-and swept-wing transonic buffet onset. In: Fluid Dynamics Conference, AIAA-2018-3229, Atlanta, GA (2018)
Koike, S., Ueno, M., Nakakita, K., Hashimoto, A.: Unsteady pressure measurements of transonic buffet on the NASA common research model. In: 34th AIAA Aplied Aerodynamic Conference, AIAA-2016-4044, Washington, D.C. (2016)
Paladini, E., Dandois, J., Sipp, D., Robinet, J.C.: Analysis and comparison of transonic buffet phenomenon over several three-dimensional wings. AIAA J. 57(1), 1–18 (2018)
Sator, F., Timme, S.: Delayed detached-eddy simulation of shock-buffet on half wing-body configuration. AIAA J. 55(4), 1230–1240 (2016)
Ohmichi, Y., Ishida, T., Hashimoto, A.: Modal decomposition analysis of three-dimensional transonic buffet phenomenon on a swept wing. AIAA J. 56(10), 3938–3950 (2018)
Candon, M., Levinski, O., Altaf, A., Carrese, R., Marzocca, P. : Aircraft transonic buffet load prediction using artificial neural networks. In: 59rd AIAA Structures, Structural Dynamics and Materials Conference, AIAA-2019-0763, San Diego, CA (2019)
Candon, M., et al.: On the application of a long short-term memory deep learning architecture for aircraft dynamic loads monitoring. In: 59rd AIAA Structures, Structural Dynamics and Materials Conference, AIAA-2020-0702, Orlando, FL (2020)
Winter, M., Breitsamter, C.: Nonlinear identification via connected neural networks for unsteady aerodynamic analysis. Aerosp. Sci. Technol. 77, 802–818 (2018)
Schwamborn, D., Gerhold, T., Heinrich, R.: The DLR TAU-code, recent applications in research and industry. In: European Conference on Computational Fluid Dynamics ECCOMAS CFD 2006, Netherlands (2006)
Nelles, O.: Nonlinear System Identification - from Classical Approaches to Neural Networks and Fuzzy Models. Springer, Berlin & Heidelberg, Germany (2011)
Winter, M., Breitsamter, C.: Reduced-order modeling of transonic buffet aerodynamics. Notes Num. Fluid Mech. 142, 511–520 (2020)
Ehrle, M., Waldmann, A., Lutz, T., Krämer, E.: Simulation of transonic buffet with an automated zone DES approach. CEAS J. 11, 1025–1036 (2020)
Lutz, T., Gansel, P.P.: Going for experimental and numerical unsteady wake analyses combined with wall interference assessment by using the NASA CRM-model in ETW. In: 51st AIAA Aerospace Sciences Meeting, AIAA-2013-871, Grapevine, TX (2013)
Acknowledgement
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.
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Zahn, R., Linke, T., Breitsamter, C. (2021). Neural Network Modeling of Transonic Buffet on the NASA Common Research Model. In: Dillmann, A., Heller, G., Krämer, E., Wagner, C. (eds) New Results in Numerical and Experimental Fluid Mechanics XIII. STAB/DGLR Symposium 2020. Notes on Numerical Fluid Mechanics and Multidisciplinary Design, vol 151. Springer, Cham. https://doi.org/10.1007/978-3-030-79561-0_66
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DOI: https://doi.org/10.1007/978-3-030-79561-0_66
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