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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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 70cm 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. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: 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 4h–5h 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. [Copyright &y& Elsevier]

Merchant vessels often require icebreaker (IB) assistance to create safe pathways and improve efficiency when navigating in the Baltic Sea. Since IB resources are limited, an accurate estimation on the need for IB assistance is important. Whether IB assistance is needed depends on multiple factors. While practical experience from captains is naturally a source of valuable information for the decision on the need for IB assistance, systematic analysis of the reasoning is limited. The primary aim of this paper is to holistically investigate the influencing factors and their effect on estimating the need for IB assistance through data-driven techniques. Based on a comprehensive list of potential factors, different of data such as traffic history, environmental conditions, and ship specifications are gathered to present complex navigational scenarios. Each scenario is labeled by different navigation modes (independent navigation or IB assistance), laying the foundation for influencing factor identification and effect quantification. Logistic regression is applied to evaluate the effect of the factors on the need for IB assistance. The results show that the impact of the factors is diverse, and ridged ice concentration has the most significant impact. The effectiveness of identified factors is measured by comparing it to that of the factors that have been implemented by the existing studies (e.g., the combination of ice concentration, thickness, and ship ice class, or only ship speed). By considering the factors in this study, the classification performance can be improved by at least 5.6%. The findings in this paper can provide insights for predicting IB workloads and optimizing IB resources and have the potential to support the development of an intelligent decision-support system for winter navigation. • A data-driven model was proposed to analyze navigation mode determination in ice-covered waters. • Influencing factors for the need for icebreaker assistance in ice were quantitively identified and assessed. • The impact of various influencing factors on ships with different ice classes was examined. • The innovation of influencing factor analysis was highlighted through model performance comparison. [ABSTRACT FROM AUTHOR]

Abstract 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. Highlights • Simulated natural air-cooled freezing is implemented with aqueous solutions. • Separation efficiencies were evaluated by quantifying impurities of ice. • The differences of ice structure is visualized. • Mechanical strength and natural frequencies of ice were measured. • The effects of impurities on mechanical properties of ice are deduced. [ABSTRACT FROM AUTHOR]

Transportation in ice prone waters is a timely topic due to the pursuit for arctic natural resources and sea routes. One important safety aspect in designing ships that enter ice prone waters is to determine the ice-induced loads on ships. However, ice is a particularly inconsistent material; therefore it is difficult to predict the occurring loads when the ship hull breaks the ice. We propose a novel probabilistic, Bayesian, method for modeling and predicting ice load distributions in different ice and operational conditions. We assume the ice loads to be generated from a random process whose parameters change as a function of ice thickness and ship speed. We test four alternative hierarchical Gaussian Process models. The best model shows good performance in predictive validation tests. According to the results the probability of high ice loads increases with increasing ice thickness and increasing speed. The model can be used to predict continuously ice loads in different ice thickness and speed conditions and, with further development, has potential to be utilized in determining the safe way to operate ships in different conditions. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Ships operating in ice-infested Arctic waters are exposed to a range of ship-ice interaction related hazards. One of the most dangerous of these is the possibility of a ship becoming beset in ice, meaning that a ship is surrounded by ice preventing it from maneuvering under its own power. Such a besetting event may not only result in severe operational disruption, but also expose a ship to severe ice loading or cause it to drift towards shallow water. This may cause significant structural damage to a ship and potentially jeopardize its safety. To support safe and sustainable Arctic shipping operations, this article presents a probabilistic approach to assess the probability of a ship becoming beset in ice. To this end, the proposed approach combines different types of data, including Automatic Identification System (AIS) data, satellite ice data, as well as data on real-life ship besetting events. Based on this data, using a hierarchical Bayesian model, the proposed approach calculates the probability of a besetting event as a function of the Polar Ship Category of a ship, sea area, and the distance travelled in the prevailing ice concentration. The utility of the proposed approach, e.g. in supporting spatiotemporal risk assessments of Arctic shipping activities as well as Arctic voyage planning, is demonstrated through a case study in which the approach is applied to ships operating in the Northern Sea Route (NSR) area. The outcomes of the case study indicate that the probability of besetting is strongly dependent on the Polar Ship Category of a ship and that the probability increases significantly with higher ice concentrations. The sea area, on the other hand, does not appear to significantly affect the probability of besetting. • An approach to assess the probability of a ship besetting in ice from real-life data. • Analysis of Northern Sea Route besetting events, AIS and ice data from 2013 to 2017. • Probability of besetting depends strongly on Polar Ship Category and ice concentration. • Support for spatiotemporal risk assessments of Arctic shipping and voyage planning. [ABSTRACT FROM AUTHOR]