This paper proposes to utilise a new adaptive kernel density estimation (KDE) methodology based on linear diffusion processes for predicting the probability distribution tails of sea-state parameters. A key conclusion was reached that the proposed new methodology can lead to more accurate prediction results than traditional methods based on fittings to a measured significant wave height data set at National Data Buoy Center station 46014. This proposed methodology was subsequently utilised for deriving an accurate 50-year environmental contour line that was used in the dynamic analysis of a two-body point absorber wave energy converter. After systematically analysing the calculation results, another key conclusion was drawn that it is advantageous to use a more reliable contour line derived using the proposed new methodology for long-term dynamic analysis of wave energy converters. In summary, the proposed new adaptive KDE methodology is recommended to be utilised and to be refined continuously in future research work in the field of long-term reliability analysis of marine sustainable energy systems.
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December 2024
Research Article|
December 03 2024
A new method for analysis of marine sustainable energy systems
Yingguang Wang, PhD
Yingguang Wang, PhD
Associate ProfessorState Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China; School of Naval Architecture, Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai, People’s Republic of China (wyg110@sjtu.edu.cn)
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Publisher: Emerald Publishing
Received:
August 08 2022
Accepted:
February 09 2024
Online ISSN: 1751-7680
Print ISSN: 1478-4629
Emerald Publishing Limited: All rights reserved
2024
Proceedings of the Institution of Civil Engineers - Engineering Sustainability (2024) 177 (6): 353–364.
Article history
Received:
August 08 2022
Accepted:
February 09 2024
Citation
Wang Y (2024), "A new method for analysis of marine sustainable energy systems". Proceedings of the Institution of Civil Engineers - Engineering Sustainability, Vol. 177 No. 6 pp. 353–364, doi: https://doi.org/10.1680/jensu.22.00070
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