In this paper, we applied an artificial neural network (ANN) to the short-term prediction of the Arctic sea ice concentration (SIC). The prediction was performed using encoding and decoding processes, in which a gated recurrent unit encodes sequential sea ice data, and a feed-forward neural network model decodes the encoded input data. Because of the large volume of Arctic sea ice data, the ANN predicts the future SIC of each cell individually. The limitation of these singular predictions is that they do not use information from other cells. This results in low accuracy, particularly when there are drastic changes during melting and freezing seasons. To address this issue, we present a new data scheme including global and local SIC information, where the global information is represented by sea ice statistics. We trained ANNs using different data schemes and network architectures, and then compared their performances quantitatively and visually. The results show that, compared with a data scheme that uses only local sea ice information, the newly proposed scheme leads to a significant improvement in prediction accuracy.
Modular adaptable ships have received growing attention in recent decades as a promising approach to handling uncertainty in future operating contexts. A modular adaptable ship can be used for multiple purposes by changing its module configuration. This configuration change is based on the ship's operation platform, which is used as a common basis for multiple module configurations. The design of an operation platform is a multi-objective problem in which designers have to deal with the conflicting requirements of multiple missions and carefully determine the interfaces that affect the configurability and flexibility of the modules. In this paper, we present an optimization model for the design of an operation platform. This determines the optimal platform design that best meets the desired capabilities of multiple missions while considering its expected lifecycle cost. A platform's capabilities are evaluated based on its multiple module configurations for individual missions. The evaluation of lifecycle cost uses operation scenarios due to its sensitivity. We implemented the model in a case study involving an offshore support vessel, for which an operation platform was designed to compete with inflexible multi-purpose ships. The results give insights into the platform design problem with opportunities for further improvement of the design.
For the design of an ice-going ship, determining its ice-capability is one of the key design aspects. Excessive ice-capability increases the ship’s acquisition cost and reduces its deadweight capacity. On the other hand, less ice-capability limits its serviceable area and it decreases the probability for the ship to complete its given/expected missions successfully. The ice conditions, which the ship would encounter during its operations, are dependent on its route planning, and they become a basis for the determination of its ice-capability. For the design of an ice-going ship, which is going to be operated under constant operational conditions, static route analysis or use of historical voyage data is sufficient to estimate its required ice-capability. However, if the operational conditions change dynamically, like the Arctic sea ice conditions, a dynamic route analysis is needed. Otherwise, the required ice-capability tends to be over-estimated by the static analysis. Sea ice conditions in the Arctic change dynamically from hour-to-hour. In addition, the forecast of its operational conditions has a high uncertainty due to lack of understanding of the Arctic sea ice. Thus, for the design of a ship for Arctic operation, we carry out transit simulations in a dynamic and stochastic manner in this paper and estimate the required ice-capability from the simulations’ result.
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