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In geographical regions with sub-zero temperatures, natural freezing can potentially be used as an energy-efficient purification technique for industrial wastewaters. The utilization of this technique requires knowledge of the effect of impurities on the strength of the ice to enable the effective harvesting of produced ice. Previous studies of the mechanical properties of model-scale ice have tended to focus on the initial solution, and little consideration has been given to the amount of impurities trapped inside the ice. The aim of this research is to evaluate the separation efficiency during the natural freezing process by quantifying the impurities trapped inside the ice formed from known concentrations of aqueous solutions and to investigate the effect of impurities on the bulk mechanical properties of the ice. In this study, twenty similar size ice beams were prepared in freezing boxes in a temperature-controlled cold room from five different aqueous solutions – tap water, and ethanol and sodium chloride aqueous solutions of two different molalities, i.e. 0.1 mol/kg and 0.3 mol/kg. The bending strength and natural frequencies of the ice samples were measured in the freezing conditions, and the concentrations of impurities in melted ice samples were determined. The research shows that with the ethanol and sodium chloride solutions, the concentration of impurities inside the ice clearly decreased in comparison to the initial solution. The structure of the ice from the different solutions differed significantly; as the tap water formed columnar ice, ethanol ice grew through large platelets, while the structure of the sodium chloride ice was a mixture of the two forms. The tap water yielded the highest strength ice and the ice formed from the ethanol aqueous solutions the weakest ice. The measurements indicated that the concentration of impurity in the ice reduces the mechanical strength and the magnitude of the effect varies with the chemical characteristic of impurity.

For the design and transit simulation of ice-going ships, a number of methods have been proposed for the prediction of ship resistance and transit speed in various ice conditions. In this paper, selected methods for ship performance in level ice, ridged ice and channel ice are evaluated based on full-scale measurement data of two ships. Uncertainties are identified and evaluated for a better understanding of the deviations in the results. Ice thickness in full-scale data was measured using multiple methods to minimize the uncertainty. The thickness of level ice was measured by a stereo camera system. The ridge profile was identified through measurement with an electromagnetic device. Visual observation was conducted for the description of encountered ice conditions. For a better estimation of ship net thrust through propulsive data, the net thrust model is revised in this paper to take the effect of power and propeller pitch into consideration. The results show that for transit simulation, the selected methods for level ice give acceptable prediction of ship speed with certain underestimation. For ridged ice, the method seems to underestimate the speed, especially when a ship needs to conduct ramming operation. Data acquisition is the most problematic for the investigation of channel ice. The uncertainties due to the modelling of sub-processes and ice properties lead to certain scatter in applying these methods for speed prediction. Possible improvements are given as the conclusion of this work.

A method for estimating ship's resistance caused by sea ice ridge keels is revised and used as a part of a method for predicting performance of ships in ridged ice conditions. The resistance method is based on a continuum plasticity model of ridge rubble and is simple to compute. The performance prediction method combines deterministic simulations of ship motion with probabilistic modelling of ridged ice fields. Performance estimates given by the model are distribution of attainable mean speeds for given ice conditions and probability of the ship being able to operate independently. A comprehensive sensitivity analysis was performed to gain insight into the model and identify possible problematic parameters. The sensitivity analysis covered both the ice conditions and modelling assumptions. Two data-sets were used to test the simulation method. One set included the depth profile of sea ice, machinery data and the speed of a ship operating in ridged ice. The resistance method was able to predict the mean speed over 3km well. The second data-set consisted of a history of ship's speeds and positions from AIS data and ice conditions estimated by a numerical ice model HELMI, developed in the Finnish Meteorological Institute. Observed mean speeds were mostly well within the distributions of mean speeds simulated by the transit simulation model. Predictions of independent operation were also promising.

Ice management enables offshore and marine activities in Arctic waters in the presence of sea ice or icebergs. The presence of ice introduces additional risks to the activities. This paper discusses the risks in the light of novel risk perspectives, emphasizing uncertainties arising from potentially weak background knowledge in ice management operations. An ice management system is defined and a generic risk picture of an ice management operation is created. Risk theory is connected to the results using discussion and a conceptual example. Ice management operations are complex socio-technical systems requiring expertise in various fields. Available data is scarce and uncertainties are high. Still, the main activities need to trust in the ice management system to operate safely. The risks in ice management operations culminate in decision making and physical ice management. These need to be better understood to manage the operational risks.

VK: T20404 For safe and efficient exploitation of ice-covered waters, knowledge about ship performance in ice is crucial. The literature describes numerical and semi-empirical models that characterize ship speed in ice. These however often fail to account for the joint effect of the ice conditions on ship's speed. Moreover, they omit the effect of ice compression. The latter, when combined with the presence of ridges, can significantly limit the capabilities of an ice-strengthened ship, and potentially bring her to a halt, even if the actual ice conditions are within the design range for the given ship. This paper introduces two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is likely 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, two full-scale datasets were utilized. First, the dataset about the performance of a selected ship in ice is acquired from the automatic identification system. Second, the dataset containing numerical description of the ice field is obtained from a numerical ice model HELMI, developed in the Finnish Meteorological Institute. The collected datasets describe 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 relations between ship performance and the ice conditions were established using Bayesian networks and selected learning algorithms. 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.

The ice conditions, together with the physical and mechanical properties of the ice encountered by a ship, can change rapidly. As these affect the ship–ice interaction process, the magnitude of the ice-induced loads changes significantly even in the short term. Therefore the ice-breaking process is known to be a stochastic process, which should be studied statistically. Earlier studies indicate that the exponential, lognormal, and Weibull probability distributions would be the best probability distributions to describe the short-term ice-induced load measurements on the ship's hull. Furthermore, the standard deviation of the measured ice-induced load histograms has been observed to increase in linear fashion as a function of the mean value of the measured ice-induced load histograms. In addition, the inverse coefficient of variation of these histograms has been observed to decrease exponentially as a function of the measured maximum ice-induced loads. The study in this paper focuses on the statistical analysis of ice-induced loads. At first, the effect of the inverse coefficient of variation on the behaviour of the exponential, lognormal, and Weibull probability distributions is presented briefly. Then it is shown that the close-to-linear relation that exists between the mean value and standard deviation of the measured ice-induced load histograms can be explained by analysing the calculation methods for mean values and standard deviations. In addition, it is shown that the exponential-type relation between the inverse coefficient of variation and the measured maximum value can be explained similarly using the defined nature of the standard deviation and mean value. The effect of the threshold used in the full-scale measurements on these values is also studied and discussed in the paper.

This paper is presenting novel model-scale ice property measurements for grain size, elastic strain-modulus, compressive and tensile specimen tests. The testing and analyzing procedure is targeted to define the basic material behavior accurately to understand the material behavior for the future development of a numerical material model. Additionally, the model-scale ice thickness and the bending strength (following ITTC) are determined to classify the ice properties. The experiments consist of systematic in-situ tests to identify the model-scale ice properties in a format suitable for numerical simulations. The elastic strain-modulus is determined on the intact level ice sheet based on the load displacement relationship of the infinite plate deflection. All specimens are cut with a template to minimize dimensional variations. The specimens are loaded with a linear drive at constant speed while displacement and force are recorded. The resulting load–displacement curves indicate good repeatability. The experiments are conducted over a time of 4 h–5 h in the keeping phase , where the cooling system is adjusted to maintain the mechanical ice properties, and the obtained results do not show a dependency on the time of testing. A linear-elastic finite element model is used to reproduce the plate bending measurements for the elastic strain-modulus determination. Therewith, it is found that the actual elastic strain-modulus is 27% larger than in plain stress theory due to stresses in thickness direction. Additionally, the approximate yield strength of the model-scale ice is investigated and is found to be significantly lower than the determined maximum stresses in compression, tension and bending. Consequently, this paper contributes to a deeper understanding of the mechanics of model-scale ice, and a procedure is shown how the mechanical parameters can be determined by systematic experiments and analyses.

Ice bound shipping is considerably more dangerous than shipping under more favourable water conditions. During winter the Baltic Sea can be covered with ice over an average of about 45% of its surface. The ice mostly reaches its maximum extent in late February or early March. The Bay of Bothnia located in the northern basin of the Gulf of Bothnia is prone to extreme frozen ice conditions. During winter the typical ice thickness in the northernmost areas of the Bay of Bothnia is about 70 cm for land-fast sea ice. In this paper the ice crushing pressure on a ship's hull is analysed statistically. A series of tests took place over four days in the Aalto ice tank and involved two different ship models. The peak pressures caused by ice were calculated from four sensor sheets located at different positions on the hull. In the statistical part, the sum of the forces acting on a section of a ship's hull was modelled as a Poisson random process. Analysis of the cumulative distribution function (CDF) was modelled for the total force. The full scale comparisons of pressure-area values and line loads as a function of the load width were analysed by comparing the model scale data with full scale measurements onboard MS Arcturus and IB Sisu. Finally a short comparison is also conducted with the load level obtained from damage statistics gathered in the Baltic Sea.

This paper introduces a concept and a prototype system of using ships and coastal stations as a sensor network to obtain additional information about the ice field. The system collects marine radar images and ship performance observations, forms mosaics of images from multiples radars, calculates ice drift from subsequent radar images, analyzes trafficability in different sea areas using performance observations, and delivers processed images, trafficability estimation, and ice drift information to end-users. The prototype was developed and tested during the winters of 2008 and 2009 in the Baltic Sea. In this paper, we describe the prototype and discuss the usability of a ship sensor network. The concept appears to be feasible, and such a system would provide additional information about prevailing ice conditions.