A probabilistic approach for dynamic positioning capability and operability predictions

Francesco Mauro, Radoslav Nabergoj

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)
12 Downloads (Pure)

Abstract

Determining Dynamic Positioning capability for an offshore vessel is mandatory to identify the environmental forces the system can counteract, together with the operability in a specific operational area of interest. Conventional predictions evaluate the capability as a maximum sustainable wind speed at a predefined encounter angle for a given wind–wave correlation, not reflecting the effective wind and waves occurrence at the site. In this respect, a step forward is provided by the scatter diagram approach, allowing the evaluation of operability in a specific sea area, using a simplified method to predict wind speed from wave parameters. Here, using known wind–waves joint distributions for the long-term environmental conditions further improves the scatter diagram approach, assessing the operability of a Dynamic Positioning system through a Quasi-Monte Carlo sampling of the joint distribution. Analysing the results of the Quasi-Monte Carlo process, it is possible to obtain a site-specific capability plot, allowing the identification of critical wind speeds in a way that is familiar to operators in the offshore industry. The application of this novel method in the case of quasi-static calculations both to a reference supply vessel and a pipe-lay vessel shows the flexibility of the proposed approach for site-specific Dynamic Positioning capability predictions.

Original languageEnglish
Article number112250
Number of pages17
JournalOcean Engineering
Volume262
Early online date17 Aug 2022
DOIs
Publication statusPublished - 15 Oct 2022

Keywords

  • dynamic positioning
  • joint environmental conditions
  • offshore vessels
  • operability
  • quasi-Monte Carlo method

Fingerprint

Dive into the research topics of 'A probabilistic approach for dynamic positioning capability and operability predictions'. Together they form a unique fingerprint.

Cite this