[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121964-en":3,"doc-seo-121964-105":30,"detail-sidebar-cat-0-en-105":90},{"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},121964,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",6,"Technology","Machine learning to expedite concept monopile design","Offshore wind turbine generators supported by monopile foundations require early-stage assessment of natural frequency analysis and fatigue limit state to drive feasible diameters and wall thickness. Industry typically uses rule-based soil reaction curves in simplified models, but location-specific 3D finite element analysis is increasingly adopted for accuracy, despite calibration costs and long runtimes. This work introduces a concept-stage surrogate model trained on 2000 3D FEA simulations to predict NFA and FLS results closely to 3D FEA while enabling over 10,000× faster estimations. Validation with published lateral response data supports reduced risk and improved site feasibility and cost estimates.","Proceedings of the XVIII ECSMGE 2024  \nGEOTECHNICAL ENGINEERING CHALLENGES  \nTO MEET CURRENT AND EMERGING NEEDS OF SOCIETY © 2024 the Authors  \nISBN 978-1-032-54816-6  \nDOI 10. 1201/9781003431749-537  \n[Open Access: www.taylorfrancis.com](Open Access: www.taylorfrancis.com), CC BY-NC-ND 4.0 license   \nMachine learning to expedite concept monopile design Apprentissage automatique pour accélérer la conception de monopieux  \nconceptuels  \nJ. S. Alexander*  \nGeowynd/University of Glasgow, Glasgow, UK  \nR.M. Buckley  \nUniversity of Glasgow, Glasgow, UK  \nS.A. Whyte  \nGeowynd, London, UK  \n*[jal@geowynd.com](jal@geowynd.com)  \nABSTRACT: Offshore wind turbine generators (WTG)s are typically supported by monopile foundations in shallow water (\u003C50m) . The design of monopile foundations is usually conducted using rule-based soil reaction curves (SRC)s in a 1D or 0D model. For the derivation of SRCs, WTG location-specific 3D finite element analysis (FEA) is now typically adopted in industry to ensure optimised foundation design. However, this approach is time consuming to calibrate to new sites and hence not well suited for early-stage structural design and load simulations where thousands of calculations are performed to finalise the offshore wind farm layout. This paper presents a novel approach for early stage monopile design (i.e. concept or pre-FEED), in which a concept design surrogate model was trained on a database of 2000 3D FEA simulations using a machine learning algorithm. The surrogate model provides rapid estimations of monopile design for Natural Frequency Analysis (NFA) and Fatigue Limit State (FLS) conditions, while also intrinsically incorporating the fidelity of the 3D FEA simulations. The surrogate model predicts results close to that of 3D FEA, over 10000 times faster, for layered soil profilesand a wide range of monopile dimensions inside the training space. Published monopile lateral response results from field testing were used to further validate the model. Use of such a tool in practice will speed up the process of preliminary monopile design and load simulations, reducing risks and time for developers, whilst improving site feasibility studies and cost estimates.  \nRÉSUMÉ: Les générateurs d'éoliennes offshore (WTG) sont généralement soutenus par des fondations monopieux. La conception des fondations monopieux est généralement réalisée à l'aide de courbes de réaction du sol (SRC) basées sur des règles dans un modèle 1D ou 0D. Pour la dérivation des SRC, la FEA 3D spécifique à l'emplacement WTG est désormaisgénéralement adoptée dans l'industrie pour garantir une conception optimisée des fondations. Cependant, cette approche prend du temps à calibrer sur de nouveaux sites et n'est donc pas bien adaptée à la conception structurelle et aux simulations de charges à un stade précoce, où des milliers de calculs sont effectués pour finaliser la configuration du parc éolien offshore. Cet article présente une nouvelle approche de la conception de monopieux à un stade précoce (c'est-à-dire concept ou préFEED), dans laquelle un modèle de substitution de conception de concept a été formé sur une base de données de 2000 simulations FEA 3D à l'aide d'un algorithme d'apprentissage automatique. Le modèle de substitution fournit des estimations rapides de la conception de monopieux pour les conditions d'analyse de fréquence naturelle (NFA) et d'état limite de fatigue (FLS), tout en intégrant intrinsèquement la fidélité des simulations FEA 3D. Le modèle de substitution prédit des résultats proches de ceux de la FEA 3D, plus de 10 000 fois plus rapides, pour des profils de sol en couches et une large gamme de dimensions de monopieux à l'intérieur de l'espace d'entraînement. Les résultats publiés de la réponse latérale du monopile du PISA JIP ont été utilisés pour valider davantage le modèle. L'utilisation pratique d'un tel outil accélérera le processus de conception préliminaire du monopieu et de simulations de charge, réduisant ai","cbCaifAD63Wsz2vk","https://ap.wps.com/l/cbCaifAD63Wsz2vk","pdf",730730,1,4,"English","en",105,"# Introduction\n# Background\n## Current methods for NFA and FLS design","[{\"question\":\"Why is early-stage NFA and FLS assessment important for concept monopile design?\",\"answer\":\"NFA and FLS results typically determine the feasible monopile diameter and wall thickness, so they must be evaluated early to support design decisions.\"},{\"question\":\"What problem does the proposed approach address with current industry practice?\",\"answer\":\"Location-specific 3D finite element analysis used for deriving soil reaction behavior is accurate but slow and computationally expensive to calibrate, making it unsuitable when thousands of calculations are needed.\"},{\"question\":\"How does the surrogate model improve speed while maintaining accuracy?\",\"answer\":\"A concept design surrogate model trained on 2000 3D FEA simulations provides rapid NFA and FLS estimations and retains the fidelity of the original 3D FEA, achieving more than a 10,000× speedup compared with 3D FEA.\"}]","Machine learning to expedite concept monopile design | 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is early-stage NFA and FLS assessment important for concept monopile design?","Question",{"text":74,"@type":75},"NFA and FLS results typically determine the feasible monopile diameter and wall thickness, so they must be evaluated early to support design decisions.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What problem does the proposed approach address with current industry practice?",{"text":79,"@type":75},"Location-specific 3D finite element analysis used for deriving soil reaction behavior is accurate but slow and computationally expensive to calibrate, making it unsuitable when thousands of calculations are needed.",{"name":81,"@type":72,"acceptedAnswer":82},"How does the surrogate model improve speed while maintaining accuracy?",{"text":83,"@type":75},"A concept design surrogate model trained on 2000 3D FEA simulations provides rapid NFA and FLS estimations and retains the fidelity of the original 3D FEA, achieving more than a 10,000× speedup compared with 3D 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