A Predictive Analytics Method for the Avoidance of Ship Grounding in Real Operational Conditions

Ghalib Taimuri, Mingyang Zhang, Spyros Hirdaris

Research output: Contribution to conferencePaperScientificpeer-review

4 Citations (Scopus)

Abstract

This paper presents a rapid method for the evaluation of ship grounding risk and the estimation of avoidance action in real operational conditions. The approach makes use of big data analytics from Automatic Identification System (AIS), nowcast and General Bathymetric Chart of the Oceans (GEBCO) to generate potential grounding scenarios. Following the identification of potential grounding scenarios, a Fluid Structure Interaction (FSI) model is adopted to simulate grounding avoidance actions that account for the influence of surrounding water and ship controlling devices in 6- DoF. Application for the case of a passenger ship operating under ice free conditions in the Gulf of Finland demonstrates the potential of the method for the development of improved decision support systems and operational practices.
Original languageEnglish
DOIs
Publication statusPublished - 21 Sept 2022
MoE publication typeNot Eligible
EventSNAME Maritime Convention - Houston, United States
Duration: 26 Sept 202229 Sept 2022
https://web.cvent.com/event/742733d5-d310-4259-8003-0d1caacee4f8/summary

Conference

ConferenceSNAME Maritime Convention
Abbreviated titleSMC
Country/TerritoryUnited States
CityHouston
Period26/09/202229/09/2022
Internet address

Keywords

  • 6-DoF maneuvering model
  • big data analytics
  • grounding risk
  • Gulf of Finland
  • machine learning
  • ship safety
  • simplified FSI

Fingerprint

Dive into the research topics of 'A Predictive Analytics Method for the Avoidance of Ship Grounding in Real Operational Conditions'. Together they form a unique fingerprint.

Cite this