Serr, Johanna und Wedler, Mathies und Stender, Merten und Fonseca, Nuno und Guedes Soares, Carlos und Hoffmann, Norbert und Ehlers, Sören und Klein, Marco (2025) Data-driven, non-linear ship response prediction based on time series of irregular, long-crested sea states amidships. Ocean Engineering (317). Elsevier. doi: 10.1016/j.oceaneng.2024.119963. ISSN 0029-8018.
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Offizielle URL: https://www.sciencedirect.com/science/article/pii/S0029801824033018
Kurzfassung
The accurate prediction of vessel responses in waves is crucial for decision-making and contribute to the operational safety and risk minimization. Short-term predictions can be carried out by estimating the vessel’s motions and loads based on incident waves. Existing model-based approaches either require computationally intensive simulations that compromise real-time capability or use simplified models affecting the accuracy of the prediction. Therefore, this study explores the feasibility of using neural networks for mapping time signals of surface elevation data and a set of corresponding ship responses, i.e. the heave and pitch motions as well as the vertical bending moment. The approach followed here is built on the assumption that the wave profile amidships is known. A synthetic dataset was generated using a time-domain strip theory solver with considerations of non-linear effects on motions and loads due to large amplitude waves in a variety of irregular, long-crested sea state conditions. We propose two different neural network models, a multi-layer perceptron (MLP) and a fully convolutional neural network (FCNN), and compare their performances on measurement data obtained from model tests in a seakeeping basin. The evaluations also include the freak wave reproduction of the ‘new year wave’. The proposed networks are able to estimate the motions and bending moment accurately for a wide range of sea state conditions, surpassing current state-of-the-art models on the given data sets.
elib-URL des Eintrags: | https://elib.dlr.de/211173/ | ||||||||||||||||||||||||||||||||||||
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Dokumentart: | Zeitschriftenbeitrag | ||||||||||||||||||||||||||||||||||||
Titel: | Data-driven, non-linear ship response prediction based on time series of irregular, long-crested sea states amidships | ||||||||||||||||||||||||||||||||||||
Autoren: |
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Datum: | 2025 | ||||||||||||||||||||||||||||||||||||
Erschienen in: | Ocean Engineering | ||||||||||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||||||||||
Open Access: | Ja | ||||||||||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||||||||||||||
In ISI Web of Science: | Ja | ||||||||||||||||||||||||||||||||||||
DOI: | 10.1016/j.oceaneng.2024.119963 | ||||||||||||||||||||||||||||||||||||
Verlag: | Elsevier | ||||||||||||||||||||||||||||||||||||
ISSN: | 0029-8018 | ||||||||||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||||||||||
Stichwörter: | ML based prediction; MLP; FCNN, LSTM, ship motions and loads in waves | ||||||||||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||||||||||
HGF - Programm: | Verkehr | ||||||||||||||||||||||||||||||||||||
HGF - Programmthema: | keine Zuordnung | ||||||||||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Verkehr | ||||||||||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | V - keine Zuordnung | ||||||||||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | V - keine Zuordnung | ||||||||||||||||||||||||||||||||||||
Standort: | Geesthacht | ||||||||||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Maritime Energiesysteme | ||||||||||||||||||||||||||||||||||||
Hinterlegt von: | Klein, Marco | ||||||||||||||||||||||||||||||||||||
Hinterlegt am: | 07 Jan 2025 11:11 | ||||||||||||||||||||||||||||||||||||
Letzte Änderung: | 07 Jan 2025 11:11 |
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