{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-separation-of-laminar-turbulent","title":"Automatic separation of laminar-turbulent flows on aircraft wings and stabilisers via adaptive attention butterfly network","arxiv_id":null,"date":"2022-10-18","proceeding":"Experiments in Fluids 2022 10","authors":["Rıdvan Salih Kuzu","Philipp Mühlmann","Xiao Xiang Zhu"],"abstract":"Many of the laminar-turbulent flow localisation techniques are strongly dependent upon expert control even-though determining the flow distribution is the prerequisite for analysing the efficiency of wing & stabiliser design in aeronautics. Some recent efforts have dealt with the automatic localisation of laminar-turbulent flow but they are still in infancy and not robust enough in noisy environments. This study investigates whether it is possible to separate flow regions with current deep learning techniques. For this aim, a flow segmentation architecture composed of two consecutive encoder-decoder is proposed, which is called Adaptive Attention Butterfly Network. Contrary to the existing automatic flow localisation techniques in the literature which mostly rely on homogeneous and clean data, the competency of our proposed approach in automatic flow segmentation is examined on the mixture of diverse thermographic observation sets exposed to different levels of noise. Finally, in order to improve the robustness of the proposed architecture, a self-supervised learning strategy is adopted by exploiting 23.468 non-labelled laminar-turbulent flow observations.","url_abs":"https://link.springer.com/article/10.1007/s00348-022-03516-4","url_pdf":"https://link.springer.com/content/pdf/10.1007/s00348-022-03516-4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatic-separation-of-laminar-turbulent","repo_url":"https://github.com/ridvansalihkuzu/butterflynet","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"laminar-turbulent-flow-localisation","task_name":"Laminar-Turbulent Flow Localisation"},{"task_slug":"self-supervised-learning","task_name":"Self-Supervised Learning"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/laminar-turbulent-flow-localisation-on-1","task":"Laminar-Turbulent Flow Localisation","dataset":"Wind Tunnel and Flight Test Experiments","model":"ButterflyNet","rank_in_archive_order":1,"of":1,"metrics":{"Category IoU":"91.17"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}