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Optimisation of tidal windows for deep-drafted vessels by means of a probabilistic approach policy for access channels with depth limitations
Vantorre, M.; Candries, M.; Verwilligen, J. (2014). Optimisation of tidal windows for deep-drafted vessels by means of a probabilistic approach policy for access channels with depth limitations, in: 33rd PIANC World Congress - Navigating the new millenium, San Francisco, June 1 to 5, 2014: papers. pp. [1-18]
In: (2014). 33rd PIANC World Congress - Navigating the new millenium, San Francisco, June 1 to 5, 2014: papers. PIANC: [s.l.]. , more

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Document type: Conference paper

Author keywords
    Ports and maritime navigation; Deep-draft navigation and waterways; Probabilistic approach policy; Tidal windows; Access channels

Authors  Top 
  • Vantorre, M., more
  • Candries, M., more
  • Verwilligen, J., more

Abstract
    The access policy to ports for deep-drafted ships making use of channels which are subject to waves, tides, currents and other complicating factors can be based on either deterministic or probabilistic principles. For determining tidal windows for deep-drafted vessels arriving at and departing from the ports located at the Belgian coast and the Western Scheldt estuary, a software tool ProToel has been developed, which can take account of several criteria including gross under keel clearance, probability of bottom touch, manoeuvrability margin, current restrictions, penetration into mud layers. After an overview of the software and some typical applications, a recent study of the feasibility of the introduction of a probabilistic access policy for container and bulk traffic to/from Flushing, Antwerp and Terneuzen will be discussed. In general, the introduction of a probabilistic access policy would have a favourable effect on the accessibility of the ports in terms of maximum allowable draft and/or length of the tidal window. Compared to a deterministic approach, however, the decision-making algorithm for a probabilistic access policy appears to depend on a significantly larger number of parameters, which moreover often induce a greater degree of uncertainty in the results.

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