On-board reassessment of sea conditions for marine operations by Christophe Capitant (presented by Christophe Maisondieu) PrISM_MSEM System

The context : SAR operations at sea  Most of the time in rough sea conditions  Require ability to predict the response of ships and other systems with accuracy and in a reliable way  « Trial and error » : not an option  Quick investigation of many alternatives PrISM_MSEM System

The current maritime situation : Factors that produce maritime incidents :  Diversity of older vessels and state-of-the-art ships  Reduced crew  Areas of high density shipping  Increasing levels of traffic and higher transit speeds  Commercial requirements force ‘ short-cuts ’ PrISM_MSEM System

Increased risk in marine operations due to :  Severely limited ‘ sea sense ’ when in rough sea conditions and/or hazardous situations such as towing, rescue operations, damage control, etc….  Management of multiple and concurrent navigation issues : (security, ship motion, structural stress, transit speed optimisation, etc…)  Difficulty to provide all the relevant information to the final human deciders in a useful form. PrISM_MSEM System

The current requirements : Choice of the best course under meteorological and operational constraints :  Assess the consequences of the various options in extreme situations before taking the decision  Shorten the decision process  Increase navigation accuracy  Limit ship fatigue and risk of failure  Avoid serious damage and possible pollution PrISM_MSEM System

Our option :  Enhance local sea-state forecast.  Use EO enhanced forecast  to predict the ship or system response  to further enhance the local forecast by comparing with observed ship response.  to simulate the ship behaviour even after a change in the course. PrISM_MSEM System

The mean :PrISM a system developed for helicopter deck-landing operations and adapted to other marine operations. Main features :  Implementation of cutting-edge maritime environmental data and ship motion models  Use of Artificial Intelligence :  System adapts automatically to the real time measured sea-state using genetic algorithms  Emulation of the human decision-making process by use of fuzzy logic  Implementation of highly efficient optimisation algorithms (multi-parameters: roll, bending moment, towline tension,...) PrISM_MSEM System

PrISM main results : Operational simulations show :  Significant reduction of the inaccuracies in the local sea-state models  Reduction of the global ‘energy density spectrum’ of the response of the ship when selecting the solution recommended by the system, i.e. :  reduced ship motion  reduced structural stress  Optimisation of the navigation course in terms of safety PrISM_MSEM System

The Ship and Marine Environment Model Interface PrISM_MSEM System

The Self-Adaptation of the Sea-State Interface PrISM_MSEM System

The Motion and Structure Management Interface PrISM_MSEM System

The Route Optimisation Management Interface PrISM_MSEM System

The Navigation Management Interface PrISM_MSEM System

Projects : CAMMEO Co-Ordinated Approach to Markets for Marine Earth Observations  Would assimilation of Earth Observations measurements in forecast enhance the ability of the PrISM_MSEM system to accurately evaluate the local Sea-state ? PrISM_MSEM System ForecastForecast +EO Best Local Sea-State PrISM_MSEM

Projects : CAMMEO  Assessment and validation tests  on-board R/V THALASSA  November 2004  Bay of Biscaye PrISM_MSEM System

Proposal : SARASSEN SAR ASSistant for Enhancement of Navigation  Develop, install and test a full system on-board SAR vessels : PrISM_MSEM System FORECAST Sea-State Input Data HYDRODYNAMICS SOLVER (Tow-line and assisted ship dynamics) PrISM_MSEM Local Sea-State Best course/speed options