A probabilistic approach for dynamic positioning capability and operability predictions

Mauro, Francesco and Nabergoj, Radoslav (2022) A probabilistic approach for dynamic positioning capability and operability predictions. Ocean Engineering, 262. 112250. ISSN 0029-8018 (https://doi.org/10.1016/j.oceaneng.2022.112250)

[thumbnail of Mauro-Nabergoj-OE-2022-A-probabilistic-approach-for-dynamic-positioning-capability-and-operability-predictions]
Preview
 Text. Filename: Mauro_Nabergoj_OE_2022_A_probabilistic_approach_for_dynamic_positioning_capability_and_operability_predictions.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (4MB)| Preview

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.

ORCID iDs

Mauro, Francesco ORCID logoORCID: https://orcid.org/0000-0003-3471-9411 and Nabergoj, Radoslav;
CORE (COnnecting REpositories)