Veröffentlichungen / Dokumentationen

CEAS Special Issue zum FOR 2895 Symposium
CEAS Aeronautical Journal, Volume 15, Issue 1, January 2024
Special Issue: Numerical and experimental studies on high speed stall phenomena
Issue Editors: Thorsten Lutz, Andrea Beck, Lars Koop
https://link.springer.com/journal/13272/volumes-and-issues/15-1?utm_source=toc&utm_medium=email&utm_campaign=toc_13272_15_1&utm_content=etoc_springer_20240323

A. Waldmann, M. Ehrle, J. Kleinert, D. Yorita and T. Lutz: "Mach and Reynolds number effects on transonic buffet on the XRF-1 transport aircraft wing at flight Reynolds number", Exp Fluids 64, 102, 2023.
doi: 10.1007/s00348-023-03642-7

Blind, M., Kleinert, J., Lutz, T., & Beck, A. (2023). A Time-Accurate Inflow Coupling for Zonal LES. Accepted by CEAS Aeronautical Journal. https://doi.org/https://doi.org/10.48550/arXiv.2301.03192

Kleinert, J., Ehrle, M., Waldmann, A. Lutz, T.: “Wake Tail Plane Interactions for a Tandem Wing Configuration in High-Speed Stall Conditions”, submitted to CEAS Aeronaut J, https://doi.org/10.48550/arXiv.2301.05760, preprint, 2023.

Herr, M., Probst, A., & Radespiel, R.: Grey area in Embedded WMLES on a transonic nacelle-aircraft configuration, submitted to CEAS Aeronautical Journal, Preprint, https://doi.org/10.48550/arXiv.2301.05299

Herr, M., Radespiel, R., & Probst, A. (2023). Improved Delayed Detached Eddy Simulation with Reynolds-Stress Background Modeling, submitted to Computers & Fluids, Preprint https://doi.org/10.48550/arXiv.2301.07223

Christopher Julian Schauerte and Anne-Marie Schreyer: Experimental analysis of transonic buffet conditions on a two-dimensional supercritical airfoil, AIAA Journal, under revision (2023)

Christopher Julian Schauerte and Anne-Marie Schreyer: Experimental investigation on the turbulent wake flow in fully-established transonic buffet conditions, submitted to CEAS Aeronaut J, Published 02 November 2023; DOI: https://doi.org/10.1007/s13272-023-00690-x

Zahn, R., Weiner, A. and Breitsamter, C.: Prediction of Wing Buffet Pressure Loads Using a Convolutional and Recurrent Neural Network Framework, accepted for publication in CEAS Aeronautical Journal, CANJ-D-22-00126, 2022.

Blind, M., Kopper, P., Kempf, D., Kurz, M., Schwarz, A., Beck, A., & Munz, C.-D. (2022). Performance Improvements for Large Scale Simulations Using the Discontinuous Galerkin Framework FLEXI. Accepted by High Performance Computing in Science and Engineering'22. Springer.

Blind, M., Kahraman, A. B., Larsson, J., & Beck, A. (2022). Residual Estimation for Grid Modification in Wall-Modeled Large Eddy Simulation Using Unstructured High-Order Methods. Submitted to Computers & Fluids. https://doi.org/https://doi.org/10.48550/arXiv.2301.03199

Kleinert, J., Stober, J., Lutz, T.: “Numerical Simulation of Wake Interactions on a Tandem Wing Configuration in High-Speed Stall Conditions”, CEAS Aeronaut J Dez. 2022, https://doi.org/10.1007/s13272-022-00634-x

Spinner, S., Rudnik, R., “Experimental Assessment of Wing Lower Side Buffet Effects Induced by the Installation of a UHBR Nacelle”, CEAS Aeronautical J. (2022), https://doi.org/10.1007/s13272-022-00632-z.

Zahn, R., and Breitsamter, C.: Airfoil Buffet Aerodynamics at Plunge and Pitch Excitation Based on Long Short-Term Memory Neural Network Prediction, CEAS Aeronautical Journal, Vol. 13, pp. 45-55, 2022; https://doi.org/10.1007/s13272-021-00550-6.

Zahn, R., and Breitsamter, C.: Prediction of Transonic Wing Buffet Pressure Based on Deep Learning, CEAS Aeronautical Journal, 2022; https://doi.org/10.1007/s13272-022-00619-w.

Lutz, T. Kleinert, J., Waldmann, A., Koop, L., Yorita, D., Dietz, G.: “Research Initiative for Numerical and Experimental Studies on High Speed Stall of Civil Aircraft“, Journal of Aircraft, Nov. 2022, https://doi.org/10.2514/1.C036829

Herr, M., & Probst, A. (2021). Efficient Modelling of Near-Wall Turbulence in Hybrid RANS-LES Simulations. In Notes on Numerical Fluid Mechanics and Multidisciplinary Design (Vol. 151, pp. 615–624). https://doi.org/10.1007/978-3-030-79561-0_58

Rozov, V., and Breitsamter, C.: Data-Driven Prediction of Unsteady Pressure Distributions Based on Deep Learning, Journal of Fluids and Structures, Vol. 104, 2021; https://doi.org/10.1016/j.jfluidstructs.2021.103316

Zahn, R., Linke, T., and Breitsamter, C.: Neural Network Modeling of Transonic Buffet on the NASA Common Research Model, NNFM XIII, Springer Verlag, Vol. 151, pp. 697-706, 2021.
https://www.springerprofessional.de/en/neural-network-modeling-of-transonic-buffet-on-the-nasa-common-r/19357828

Andre Weiner and Richard Semaan: flowTorch - a Python library for analysis and reduced-order modeling of fluid flows, Journal of Open Source Software, 6(68), 3860, 2021, https://doi.org/10.21105/joss.03860

Spinner, S., Rudnik R., „Vortex Interaction in Transonic Flow for Wing-Mounted UHBR Nacelles”, 2023, AIAA SciTech Forum, 23–27 January 2023, National Harbor, MD, AIAA Paper to be published.

Herr, M., Spinner, S., Probst, A., Rudnik R., & Radespiel R., Embedded WMLES of transonic buffet on a nacelle-aircraft configuration AIAA SciTech Forum, 23–27 January 2023, National Harbor, MD, AIAA Paper 2023-0246, https://doi.org/10.2514/6.2023-0246, 2023.

Christopher Julian Schauerte and Anne-Marie Schreyer: Influence of Reynolds Number on Transonic Buffet Conditions on a Supercritical Airfoil, AIAA 2023-0431, AIAA SCITECH 2023 Forum, January 2023, https://doi.org/10.2514/6.2023-0431

Zahn, R., Weiner, A. and Breitsamter, C.: Wing Buffet Pressure Load Prediction Based on a Hybrid Deep Learning Model, 33rd Congress of the International Council of the Aeronautical Sciences (ICAS), Paper ICAS2022-0043, Stockholm, Sweden, 2022 (Paper & Presentation awarded with McCarthy Student Award).

Christopher Julian Schauerte, Johannes Bosbach, Robert Konrath, Reinhard Geisler, Florian Philipp, Anne-Marie Schreyer: Characterization of Transonic Buffet on a Swept Wing by Means of Cryogenic PIV, Deutsche Gesellschaft Für Luft- Und Raumfahrt - Lilienthal-Oberth E.V., DLRK, September 2022, https://doi.org/10.25967/570369

Christopher Julian Schauerte and Anne-Marie Schreyer: Characterization of shock buffet on a supercritical 2D airfoil in transonic flow, 20th International Symposium on Application of Laser and Imaging Techniques to Fluid Mechanics, Lisbon, July 2022.

Zahn, R., and Breitsamter, C.: Prediction of Transonic Wing Buffet Pressure Based on Deep Learning, 70. Deutscher Luft-  und Raumfahrtkongress (DLRK), Paper 0033, Bremen, 2021.

Spinner, S., Rudnik. R., “Design of a UHBR Through Flow Nacelle for High Speed Stall Wind Tunnel Investigations”, Deutscher Luft- und Raumfahrt Kongress 2021, Deutsche Gesellschaft für Luft- und Raumfahrt-Lilienthal-Oberth e.V., https://doi.org/10.25967/550043. urn:nbn:de:101:1-2021100112224829096665 (2021).

Herr, M., Radespiel, R., & Probst, A. (2021). Erweiterung einer skalenauflösenden RSM-IDDES Methodik zur Erforschung von Verkehrsflugzeugkonfigurationen. In 20. STAB-Workshop-Jahresbericht 2021 (pp. 79-80).

Zahn, R., and Breitsamter, C.: High-Speed Buffet Aerodynamics Modeling Based on a Long Short-Term Memory Neural Network, 69. Deutscher Luft- und Raumfahrtkongress (DLRK), Paper 0027, 2020.

Daniel Fernex, Andre Weiner, Bernd Noack and Richard Semaan: Sparse Spatial Sampling: A mesh sampling algorithm for efficient processing of big simulation data, AIAA Best Paper Award, AIAA 2021-1484, AIAA Scitech 2021 Forum, January 2021, https://doi.org/10.2514/6.2021-1484

Andre Weiner and Richard Semaan: Simulation and modal analysis of transonic shock buffets on a NACA-0012 airfoil, AIAA 2022-2591, AIAA SCITECH 2022 Forum, January 2022, https://doi.org/10.2514/6.2022-2591

flowTorch - a Python library for analysis and reduced-order modeling of fluid flows

Die flowTorch-Bibliothek [1, 2] ermöglicht es Anwendern auf Strömungsdaten aus Experimenten oder numerischen Simulationen zuzugreifen, sowie diese zu analysieren und zu modellieren. Anstelle einer Black-Box End-to-End-Lösung bietet flowTorch modulare Komponenten, die es ermöglichen, transparente, automatisierte und reproduzierbare Datenverarbeitungsabläufe mit Leichtigkeit zusammenzustellen. Strömungsdaten aus Experimenten (PIV, iPSP, Schlieren) oder Simulationen (TAU, Flexi, OpenFOAM) können über eine gemeinsame Schnittstelle in wenigen Zeilen Python-Code verfügbar gemacht werden. Intern sind die Daten als PyTorch-Tensoren organisiert. Die Verwendung von PyTorch-Tensoren als primäre Datenstruktur ermöglicht schnelle Array-Operationen, parallele Verarbeitung auf CPU und GPU sowie die Entwicklung neuartiger Deep-Learning-basierter Analyse- und Modellierungsansätze. Die flowTorch Dokumentation enthält auch eine umfangreiche Sammlung von Jupyter-Notebooks [3], die zeigen, wie die Bibliothekskomponenten in einer Vielzahl von verschiedenen Anwendungsfällen eingesetzt werden können, z. B. bei der Suche nach kohärenten Strukturen mittels modaler Analyse oder bei der Erstellung von ordnungsreduzierten Modellen.