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- 01HG7RRT3YAVBNHHY5N2YSF5JE classification C3.
- 01HG7RRT3YAVBNHHY5N2YSF5JE date "2023".
- 01HG7RRT3YAVBNHHY5N2YSF5JE language "eng".
- 01HG7RRT3YAVBNHHY5N2YSF5JE type conference.
- 01HG7RRT3YAVBNHHY5N2YSF5JE hasPart 01HG7SZRJJN5W65MWBSHNTC4X1.pdf.
- 01HG7RRT3YAVBNHHY5N2YSF5JE subject "Technology and Engineering".
- 01HG7RRT3YAVBNHHY5N2YSF5JE presentedAt urn:uuid:cc7cfdd2-7f2a-4edb-86ef-15aab02dd3ae.
- 01HG7RRT3YAVBNHHY5N2YSF5JE abstract "Wind Assisted Propulsion Systems (WAPS) play a pivotal role in achieving maritime decarbonization goals. In January 2023, the EU Horizon Europe funding programme approved funding for the Orcelle Wind project, which aims to develop and build a wind-powered roll-on/roll-off (ro-ro) cargo vessel to be commissioned in late 2026 or early 2027. ORCELLE employs Wing System (WS) technology to generate aerodynamic thrust forces for propulsion. The motivation behind this research stems from the need for early detection and identification of malfunctions and degradation in critical mechanical components of the wing system. Such pre-emptive measures are essential for ensuring the safe operation of wind-assisted vessels. Furthermore, the study aims to contribute to the advancement of safety standards for wind power technology by implementing condition-based inspection and maintenance strategies. The primary objective of this project is to develop numerical tools for data-driven structural integrity assessment, focusing on fatigue degradation and the lifetime of the wing system. Additionally, a sensor suite will be designed and implemented for load and condition monitoring of the wing system, further enhancing safety protocols. The methodology employed in this study includes the development of a Stochastic Finite Element Model (SFEM) for structural assessment under various operating conditions. Surrogate models, trained on SFEM results, will predict the structural responses of the Wing System. Stress-life methods will be applied to determine the fatigue life of components. The structural reliability of the Wing System will then be evaluated using Bayesian Networks based on the failure contributions of critical components. Real-time data will be continuously streamed from field sensors, facilitating dynamic system-level failure analysis. This data will be supported by Dynamic Bayesian Network (DBN) predictive models. The integration of these approaches aims to enhance the safety and reliability of wind-assisted propulsion systems by enabling early detection of issues and implementing proactive maintenance strategies.".
- 01HG7RRT3YAVBNHHY5N2YSF5JE author 6D6BB08A-E4F5-11E2-B0A2-B95A10BDE39D.
- 01HG7RRT3YAVBNHHY5N2YSF5JE author 7aa443c6-8af1-11ed-a5b1-ff22c1d98bea.
- 01HG7RRT3YAVBNHHY5N2YSF5JE author F52E8652-F0ED-11E1-A9DE-61C894A0A6B4.
- 01HG7RRT3YAVBNHHY5N2YSF5JE dateCreated "2023-11-27T06:51:49Z".
- 01HG7RRT3YAVBNHHY5N2YSF5JE dateModified "2024-10-29T18:32:13Z".
- 01HG7RRT3YAVBNHHY5N2YSF5JE name "Data-driven lifetime assessment of a wing sail system".
- 01HG7RRT3YAVBNHHY5N2YSF5JE publisher urn:uuid:4bdce361-b357-4948-bd7e-440cfe336017.
- 01HG7RRT3YAVBNHHY5N2YSF5JE sameAs LU-01HG7RRT3YAVBNHHY5N2YSF5JE.
- 01HG7RRT3YAVBNHHY5N2YSF5JE sourceOrganization urn:uuid:ef214ff5-108a-4229-82f8-3afff57d15a0.
- 01HG7RRT3YAVBNHHY5N2YSF5JE type C3.