Penetration velocity and deceleration of penetrometer stations N1 to N97 during cruise Senckenberg_11_2008

In-situ geotechnical measurements of surface sediments were carried out along large subaqueous dunes in the Knudedyb tidal inlet channel in the Danish Wadden Sea using a small free-falling penetrometer. Vertical profiles showed a typical stratification pattern with a resolution of ~1 cm depicting a thin surface layer of low sediment strength and a stiffer substratum below (quasi-static bearing capacity equivalent: 1-3 kPa in the top layer, 20-140 kPa in the underlying sediment; thickness of the top layer ca. 5-8 cm). Observed variations in the thickness and strength of the surface layer during a tidal cycle were compared to mean current velocities (measured using an acoustic Doppler current profiler, ADCP), high-resolution bathymetry (based on multibeam echo sounding, MBES) and qualitative estimates of suspended sediment distributions in the water column (estimated from ADCP backscatter intensity). The results revealed an ebb dominance in sediment remobilization, and a general accretion of the bed towards low water. A loose top layer occurred throughout the tidal cycle, likely influenced by bedload transport and small events of suspended sediment resettlement (thickness: 6 +-2 cm). Furthermore, this layer showed a significant increase in thickness (e.g. from 8 cm to 16 cm) related to periods of overall deposition. These findings imply that dynamic penetrometers can conveniently serve to (1) quantify potentially mobile sediments by determining the thickness of a loose sediment surface layer, (2) unravel sediment strength development in potentially mobile sediments and (3) identify sediment accumulation. Such data are an important complement and add a new geotechnical perspective during investigations of sediment remobilization processes in highly dynamic coastal environments.

Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.