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.
A Predictive Analytics Method for the Avoidance of Ship Grounding in Real Operational Conditions
Taimuri, Ghalib, Zhang, Mingyang, and Spyros Hirdaris. "A Predictive Analytics Method for the Avoidance of Ship Grounding in Real Operational Conditions." Paper presented at the SNAME Maritime Convention, Houston, Texas, USA, September 2022. doi: https://doi.org/10.5957/SMC-2022-012
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