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Optimization of drag embedment anchors applying multi-objective evolutionary algorithm NSGA-II
Citation Link: https://doi.org/10.15480/882.15792
Publikationstyp
Journal Article
Date Issued
2025-12
Sprache
English
TORE-DOI
Volume
10
Issue
6
Start Page
1019
End Page
1030
Citation
Journal of Ocean Engineering and Science 10 (6): 1019-1030 (2025)
Publisher DOI
Scopus ID
Publisher
Elsevier
Establishing renewables on a floating platform in the deep sea needs secure anchoring to the seabed, commonly achieved with drag embedment anchors (DEAs). The conventional design process relies heavily on empirical testing and is often time and resource-intensive, potentially leading to suboptimal designs. This research aims to overcome these limitations by applying an evolutionary optimization algorithm to existing analytical solutions for DEAs, identifying optimal anchor fluke and shank lengths. By leveraging an optimization strategy, we aim to enhance the design process while diminishing the dependency on exhaustive physical testing and high computational cost. We employ the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to optimize anchor shapes, with a focus on three key objectives: maximizing embedment depth and bearing capacity, and minimizing anchor volume. The methodology presents a Pareto front, encompassing all optimal solutions based on the formulated objectives, and demonstrates the efficiency of NSGA-II as a tool for optimizing anchor shapes.
Subjects
Drag embedment anchor
Floating renewables
Multi-objective optimization
NSGA-II
Sustainable design
DDC Class
333: Economics of Land and Energy
629: Other Branches
620: Engineering
Publication version
publishedVersion
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1-s2.0-S2468013325000464-main.pdf
Type
Main Article
Size
1.94 MB
Format
Adobe PDF