Analysis of Full-scale Riser Responses in Field Conditions Based on Gaussian Mixture Model

TL;DR

This paper applies a Gaussian mixture model to field data of offshore riser responses, successfully grouping complex responses into clusters to enhance understanding and prediction accuracy of structural behavior under various environmental conditions.

Contribution

It introduces a domain knowledge-guided Gaussian mixture model for clustering riser response data, revealing key environmental parameters influencing complex structural responses.

Findings

Riser responses can be grouped into 12 clusters.

Cluster analysis improves understanding of complex responses.

Enhanced evaluation of response prediction accuracy.

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-minute 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.