Transportation in Arctic seas is connected with variability of routes. Availability of those routes is depending on current environmental conditions. In the paper the algorithm for navigation system, which is intended to advice captain the most economically effective and less risky route with the presence of ice chart, is proposed. Smart navigation system for Arctic seas has specific: shortest ways can occur to be impassable or too risky. More long routes through free waters can finally take less fuel comparing to shorter, but covered with ice. Thus, economical profitability of operating in Arctic seas depends on effectiveness of route choosing. To make estimations about most effective route and its length, the method based on graph algorithms is presented in this paper. The ice chart is covered by the graph, which can have form of grid, with neighbor nodes connected by edges. In general, multiple parameters can be assigned to each edge — length, maximal ice thickness on the way, risk, etc. In this paper two separate cost functions are considered: first is responsible for travel expenses, and the second is responsible for ice passability on the route. To find most economically efficient route with minimal possible ice thickness on the way the method with graph modification and Dijkstra algorithm was used. This route provides Pareto-optimal solution for reduced version of the problem. The software, which implements the method, was built. The example of searching for least expensive and Pareto-optimal route is provided. Results are discussed.

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Until recent times researchers who investigated ice loads stochastic processes usually stated the fact of normal distribution for them. In the paper the model of a stationary stochastic process with a lognormal distribution for ice loads is offered. This model relates to the strain gauge transducer ice loads measurements as well as to some examples considered in different papers that were published earlier. For this model dependencies of the autocorrelation function were found that allows to simulate the ice loads process relatively easily. The procedure of such a simulation is described in details and the example of the analysis and simulation ice loads measurements is provided.

Experiments with models of platforms and offshore structures with vertical and inclined panels, which were conducted at Krylov Research Center (St. Petersburg), demonstrated that sometimes ice loads time series registered in these experiments cannot be considered as stationary. At the same time until nowadays methods and algorithms of probabilistic modeling were mainly based on the assumption of ice loads time series stationarity. That is because the analysis and modeling for stationary stochastic process is easier than for those unstationary. In the paper the method for determining the presence of unstationarity in ice loads time series, based on statistical analysis, is described. This method employs sample mean normality. Fuzzy C-means algorithm is used to cluster autocorrelation vectors, which are built for different fragments of time series. In the paper ice loads time series, got in experiments in ice tank with offshore structure columns and basement models, are investigated on their unstationarity. The algorithm of unstationary ice loads time series simulation is offered.

Multi-objective optimization of a vessel route is considered of key importance when creating automatic navigation systems to ensure independent navigation in ice conditions. This is explained by the need to take into account not only the time or fuel expenditures on the route but also the risks. Previously, only a few models for navigation in ice used the multi-objective approach when finding the set of Pareto-optimal solutions. This paper suggests the multi-objective model of ship routing optimization with usage of the ice chart and ship parameters. Risks for a vessel are related to values of ice thickness and ice concentration in regions to travel through, which are specified by the ice chart. In the model, we use the extended version of the wave algorithm to find a set of routes, from which we select solutions of the Pareto-front for the multi-objective problem. The model uses objective functions of route length, maximum ice thickness, and maximum ice concentration. In addition, the travel time calculations are used in the model. Kaj Riska’s model of ship performance in ice is used for calculating travel time; the speed of a vessel is evaluated in each of the graph edges. The computational example provided in the paper is based on the particular ice chart of the Gulf of Finland. The developed method can be easily implemented for assisting a particular ship in independent ice navigation with the presence of a relevant ice chart.