PrISM_MSEM System
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

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 ?
Forecast Forecast +EO
PrISM_MSEM
Best Local Sea-State

PrISM_MSEM System
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 :
FORECAST
HYDRODYNAMICS
SOLVER
Sea-State Input Data
(Tow-line and assisted ship dynamics)
PrISM_MSEM
Local Sea-State
Best course/speed options