[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122775-en":3,"doc-seo-122775-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122775,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Marine propeller optimisation through user interaction and machine learning for advanced blade design scenarios - research paper","Marine propeller design faces demanding trade-offs driven by competing stakeholder requirements, complex hydrodynamics, and strict deadlines that make high-fidelity experiments and simulations impractical. An interactive optimisation approach combines designer input with an optimisation algorithm, where cavitation assessments provided by the blade designer are fed back to guide the search. To address fatigue in repeated interactive evaluations, a machine learning pipeline predicts cavitation, enabling faster decision-making. The methodology is tested on two advanced controllable-pitch propeller case studies for ROPAX vessels with realistic multi-variable objectives and constraints, including suction- and pressure-side cavitation.","Marine propeller optimisation through user interaction and machine learning for advanced blade design scenarios  \nDownloaded from: [https://research.chalmers.se](https://research.chalmers.se), 2023-10-28 14:02 UTC  \nCitation for the original published paper (version of record):  \nGypa, I., Jansson, M., Bensow, R. (2023) . Marine propeller optimisation through user interaction and machine learning for advanced blade  \ndesign scenarios. Ships and Offshore Structures, In Press.  \n[http://dx.doi.org/10.1080/17445302.2023.2265118](http://dx.doi.org/10.1080/17445302.2023.2265118)  \nN. B. When citing this work, cite the original published paper.  \nresearch.chalmers.se offers the possibility of retrieving research publications produced at Chalmers University of Technology. It covers all kind of research output: articles, dissertations, conference papers, reports etc. since 2004.  \nresearch.chalmers.se is administrated and maintained by Chalmers Library  \n(article starts on next page)  \nShips and Offshore Structures  \nISSN: (Print) (Online) Journal homepage: [https://www.tandfonline.com/loi/tsos20](https://www.tandfonline.com/loi/tsos20)  \nMarine propeller optimisation through user interaction and machine learning for advanced blade design scenarios  \nIoli Gypa, Marcus Jansson & Rickard Bensow  \nTo cite this article: Ioli Gypa, Marcus Jansson & Rickard Bensow (11 Oct 2023): Marine propeller optimisation through user interaction and machine learning for advanced blade design scenarios, Ships and Offshore Structures, DOI: 10.1080/17445302.2023.2265118  \nTo link to this article: [https://doi.org/10.1080/17445302.2023.22651](https://doi.org/10.1080/17445302.2023.22651)18  \n© 2023 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group  \n\n|  Published online: 11 Oct 2023. |\n| --- |\n|  Submit your article to this journal  |\n|  Article views: 154 |\n|  View related articles  |\n|  View Crossmark data |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=tsos20](https://www.tandfonline.com/action/journalInformation?journalCode=tsos20)  \nSHIPS AND OFFSHORE STRUCTURES  \n[https://doi.org/10.1080/17445302.2023.2265118](https://doi.org/10.1080/17445302.2023.2265118)  \nMarine propeller optimisation through user interaction and machine learning for advanced blade design scenarios  \nIoli Gypaa, Marcus Janssonb and Rickard Bensowa  \naMechanics and Maritime Sciences, Chalmers University of Technology, Gothenburg, Sweden; bKongsberg Maritime Sweden AB, Kristinehamn, Sweden  \nABSTRACT  \nThe complexity of the marine propeller design process is well recognised and is related to contradicting requirements of the stakeholders, complex physical phenomena, and fast analysis tools, where the latter are preferred due to the strict time limitations under which the entire process is carried out. With all this in mind, an optimisation methodology has been proposed and presented earlier that combines user interactivity with machine learning and proved to be useful for a simple blade design scenario. More specifically, the blade designer manually evaluates the cavitation of the designs during the optimisation and this information is systematically returned into the optimisation algorithm, a process called interactive optimisation. As part of the optimisation, a machine learning pipeline has been implemented in this study, which is used for cavitation evaluation prediction in order to solve the user fatigue problem that is connected to interactive optimisation processes. The proposed methodology is investigated for two case studies of advanced design scenarios, relevant for a real commercial situation, that regard controllable-pitch propellers for ROPAX vessels, and the aim is to obtain a set of optimal, competent blade designs. Both cases represent scenarios with several design variables, objectives and constraints and with conditions that have either suction side or pres","cbCaig9cOR8EzxLC","https://ap.wps.com/l/cbCaig9cOR8EzxLC","pdf",5194746,1,19,"English","en",105,"# Abstract\n# Introduction\n## Background and time constraints\n## Automated optimisation and objectives\n# Interactive optimisation and machine learning approach","[{\"question\":\"Why is marine propeller optimisation challenging in practice?\",\"answer\":\"It must satisfy conflicting stakeholder requirements while accounting for complex physical effects under strict time limits, making detailed experiments and high-fidelity simulations difficult to use systematically.\"},{\"question\":\"How does interactive optimisation work in this methodology?\",\"answer\":\"The blade designer manually evaluates cavitation during optimisation, and those evaluations are returned into the optimisation algorithm to guide subsequent design iterations.\"},{\"question\":\"What role does machine learning play?\",\"answer\":\"A machine learning pipeline predicts cavitation to reduce user fatigue and address the burden of repeated interactive cavitation evaluations.\"}]","Marine propeller optimisation through user interaction and machine learning for advanced blade design scenarios - 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