Optimizing Design Load Case Reduction for Fatigue Assessment of Mooring Lines in Marine Renewable Energy Systems (Proceedings of the European Wave and Tidal Energy Conference, 2025)
The design phase of ocean renewable energy systems necessitates accounting for numerous design load cases to comply with established standards. Long-term fatigue assessment, often the most time-intensive aspect, requires thousands of time-domain simulations to capture the combined effects of environmental conditions. This process is computationally expensive and contributes to the already high Levelized Cost of Energy (LCOE) for marine renewable energy.Additionally, in a context shaped by climate change, marine resources are expected to progressively vary in the coming years, with extreme conditions becoming more frequent. This implies that traditional long-term fatigue assessments, based on historical data, may be inaccurate as they fail to account for potential variations in near-future conditions. Consequently, devices may not be designed to withstand these emerging scenarios.To address both challenges, this study employs the K-means clustering technique in conjunction with Coupled Model Intercomparison Project Phase 6. This approach enables a reduction in the set of metocean conditions by selecting representative environmental states, maintaining accuracy in fatigue damage estimation while significantly decreasing the computational burden. Furthermore, climate models provide future metocean data projections under various socio-economic pathways, enabling this method to incorporate resource trends into damage estimation.The development of this research progresses through three distinct phases:• Data Collection: Wave data, characterized by significant wave height and spectral peak period, is gathered for a specific site location across three forecasting horizons under a high-emissions scenario.• Clustering: The K-means clustering algorithm is applied to the datasets for each forecasting horizon. The clustering quality is assessed using widely recognized clustering metrics and resource-based metrics.• Fatigue Damage Estimation: The clustered data points are utilized to estimate fatigue damage in the mooring lines of a Wave Energy Converter (WEC). A frequency-domain technique is employed for this purpose, and the minimum number of clusters required to accurately estimate fatigue damage is determined through this approach.By streamlining computational efforts and maintaining accuracy in fatigue assessments, this method bolsters resilience to future environmental changes and promotes cost-effective marine renewable energy solutions.