Analysis of full-scale riser responses in field conditions based on Gaussian mixture model. (January 2023)

• Record Type: Journal Article
• Authors: Wu, Jie; Eidnes, Sølve; Jin, Jingzhe; Lie, Halvor; Yin, Decao; Passano, Elizabeth; Sævik, Svein; Riemer-Sørensen, Signe
• Is Part Of: Journal of fluids and structures. Volume 116(2023)
• Publication Date: 2023-01
• Subjects: Marine riser -- Field measurement -- Vortex-induced vibrations -- Gaussian mixture model -- Un-supervised learning -- Time domain analysis; Fluid-structure interaction -- Periodicals; Fluid mechanics -- Periodicals; Structural dynamics -- Periodicals; Structural analysis (Engineering) -- Periodicals; 620.106
• DOI: 10.1016/j.jfluidstructs.2022.103793
• Languages: English
• ISSNs: 0889-9746

Abstract

Abstract: Offshore slender marine structures experience complex and combined load conditions from waves, current and vessel motions that may result in both wave frequency and vortex shedding response patterns. Field measurements often consist of records of environmental conditions and riser responses, typically with 30 min intervals. These data can be represented in a high-dimensional parameter space. However, it is difficult to visualize and understand the structural responses, as they are affected by many of these parameters. It becomes easier to identify trends and key parameters if the measurements with the same characteristics can be grouped together. Cluster analysis is an unsupervised learning method, which groups the data based on their relative distance, density of the data space, intervals, or statistical distributions. In the present study, a Gaussian mixture model guided by domain knowledge has been applied to analyze field measurements. Using the 242 measurement events of the Helland-Hansen riser, it is demonstrated that riser responses can be grouped into 12 clusters by the identification of key environmental parameters. This results in an improved understanding of complex structure responses. Furthermore, the cluster results are valuable for evaluating the riser response prediction accuracy. Highlights: Clustering analysis of riser field measurements with a Gaussian mixture model Improved understanding of riser responses from characteristics of identified clusters Condition identification for riser responses dominated by vortex induced vibrations (VIV) Time-domain riser response prediction under various load conditions