[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128231-en":3,"doc-seo-128231-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128231,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Advancing Reynolds-Averaged Navier–Stokes Turbulence Models with Machine Learning and Data-Driven Methods - Doctoral Thesis","Reynolds-averaged Navier–Stokes (RANS) methods form the basis of turbulent-flow simulations in industrial and technical contexts because they deliver computational efficiency while retaining engineering usefulness. This doctoral work advances RANS turbulence modeling by integrating machine-learning ideas and data-driven methods, targeting improved prediction quality for key flow phenomena. The thesis connects methodological developments with results reported in peer-reviewed venues and conference proceedings, culminating in a coherent set of contributions evaluated through the presented research outcomes and discussions.","Technische Universität Darmstadt Fachbereich Maschinenbau Darmstadt 2025  \nAdvancing Reynolds-Averaged Navier–Stokes Turbulence Models with Machine Learning and Data-Driven Methods  \nZur Erlangung des akademischen Grades Doktor-Ingenieur (Dr.-Ing.)  \ngenehmigte Dissertation von  \nFelix Rüdiger Köhler M.Sc .  \nReferent: Prof. Dr. rer. nat. Michael Schäfer  \nKorreferent: Apl. Prof. Dr.-Ing. habil. Suad Jakirlić  \nTag der Einreichung: 24.02.2025  \nTag der Disputation: 28.05.2025  \nAdvancing Reynolds-Averaged Navier–Stokes Turbulence Models with Machine Learning and Data-Driven Methods  \nSubmitted doctoral thesis by Felix Rüdiger Köhler  \n1. Review: Prof. Dr. rer. nat. Michael Schäfer  \n2. Review: Apl. Prof. Dr.-Ing. habil. Suad Jakirlić  \nDate of submission: 24.02.2025  \nDate of thesis defense: 28.05.2025  \nTechnical University of Darmstadt – D17 Department of Mechanical Engineering  \nDarmstadt 2025  \nPlease cite this document as:  \nURN: urn:nbn:de:tuda-tuprints-306847  \nURL: [https://tuprints.ulb.tu-darmstadt.de/30684](https://tuprints.ulb.tu-darmstadt.de/30684)  \n[Year of publication on TUprints: 2025](Year of publication on TUprints: 2025)  \nThis document is provided by TUprints, the E-Publishing Service of TU Darmstadt [https://tuprints.ulb.tu-darmstadt.de](https://tuprints.ulb.tu-darmstadt.de)[ ](https://tuprints.ulb.tu-darmstadt.de)[tuprints@ulb.tu-darmstadt.de](tuprints@ulb.tu-darmstadt.de)  \nThis work is licensed under a Creative Commons License: CC BY 4.0 International [https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)  \nPreface  \nIt is with great satisfaction that I present this doctoral thesis, representing the culmination of seven years of dedicated research and exploration. The results presented in this thesis go beyond the work published during my tenure as a research assistant at the Institute for Numerical Methods in Mechanical Engineering at the Technical University of Darmstadt. Partial results from this thesis have been published in:  \n1. F. Köhler, J. Munz, and M. Schäfer, \"Data-driven augmentation of RANS turbulence  \nmodels for improved prediction of separation in wall-bounded flows\", In: AIAA Scitech  \n2020 Forum, 2020 .  \n2. F. Köhler, S. Jakirlić, S. Wegt, and M. Schäfer, \"Data-driven Modeling of the Pressure  \nstrain Term in Second-Moment Closure RANS Models\", In: Conference Proceedings:  \n13th International ERCOFTAC symposium on engineering, turbulence, modelling and  \nmeasurements, Greece, Rhodes, pp. 515–520, 2021 .  \nAdditional research published during my time as a research assistant includes:  \n3. F. Köhler, R. Maduta, B. Krumbein, and S. Jakirlić, \"Scrutinizing conventional and  \neddy-resolving unsteady RANS approaches in computing the flow and aeroacoustics past  \na tandem cylinder\", In: Symposium der Deutsche Gesellschaft für Luft-und Raumfahrt,  \nCham: Springer International Publishing, pp. 586–596, 2018 .  \n4. Z. Kraus, F. Köhler, J. Friedrich, and M. Schäfer, \"Deep Learning Approach for Curvature  \nPrediction in Algebraic Volume of Fluid Methods\", In: 9th International Conference on  \nComputational Methods for Coupled Problems in Science and Engineering, 2021 .  \n5. M. Kannapinn, F. Köhler, and M. Schäfer, \"A Validated Thermal Computational Fluid  \nDynamics Model of Wine Warming in a Glass\", Applied Sciences 14, no. 19, 2024 .  \nAcknowledgements  \nThis work would not have been possible without the support of many individuals, to whom Iam deeply grateful. I would like to express my sincere gratitude to Prof. Michael Schäfer forgiving me the opportunity to work as a research assistant and for fostering a straightforward and positive working environment. I am also grateful to him for granting me complete freedom to conduct my research independently, allowing me to explore ideas and complete this thesis. I am also sincerely thankful to Prof. Suad Jakirlić for his continuous support, which began during my master’s studies and extended throughout my doctora","cbCaiuSFqur5XjfD","https://ap.wps.com/l/cbCaiuSFqur5XjfD","pdf",16039219,3,1,186,"English","en",105,"# Preface\n# Acknowledgements\n# Abstract","[{\"question\":\"What is the main subject of the doctoral thesis?\",\"answer\":\"The thesis focuses on advancing Reynolds-averaged Navier–Stokes (RANS) turbulence models using machine learning and data-driven methods to improve turbulent flow predictions.\"},{\"question\":\"Why are RANS methods important for turbulent-flow simulations?\",\"answer\":\"RANS methods are fundamental because they provide computational efficiency for turbulent-flow simulations in industrial and technical applications.\"},{\"question\":\"What kinds of research outputs are referenced in the thesis?\",\"answer\":\"The preface lists prior and related publications and conference papers, indicating that the thesis builds on multiple published partial results and research developed during the author’s tenure.\"}]","Advancing Reynolds-Averaged Navier–Stokes Turbulence Models with Machine Learning and Data-Driven Methods - 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