In this paper, we focus on investigating ship activities in the United States’ Arctic waters and developing new viscoelastic materials that can mimic specific ice behavior. This is a significant challenge, and we discuss potential positive outcomes and how the acquired knowledge can contribute to understanding ice behavior in Arctic and Sub-Arctic regions. We first define ice and ship statistics, providing a foundational understanding of potential ice–ship interactions. We then describe the development of thought experiments for wave–ice interactions and the creation of a numerical environment for modeling purposes. This step is crucial for simulating various scenarios related to ice and wave dynamics, ultimately contributing to the design of ships capable of navigating safely in diverse Arctic conditions. Finally, stakeholder and community engagement is addressed, recognizing the importance of involving local perspectives and insights to ensure practical, socially responsible, and effective solutions.

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Climate change is affecting global weather patterns, but nowhere is this more apparent than in the Arctic. The Arctic is an extreme environment going through rapid climate change, resulting in the opening of new shipping lanes and leading to less multiyear ice formation. This increases the risk of collision with the sea ice making it difficult to make long term voyage plans based on sea ice predictions. The Automatic Identification System (AIS) provides important services for marine domain awareness. For safe and effective ship traffic management and the development of new artificial intelligence (AI) marine algorithms, precise AIS readings for hull size and type are typically required. In this paper, we provide a summary of ship activities as well as relevant ice information in Alaskan waters that may result in ice-ship interactions and discuss them. AIS ship data was used to determine the ship speed distribution in different areas in the Alaskan waters. Ship movement in ice infested regions was compared with sea ice data from the Copernicus Reanalysis Products and sea ice thickness was estimated using empirical equations to identify potential ship-sea ice interactions. Data from 2015 to 2020 was analyzed to determine if there is an increase in maritime activity that can be linked to a decline in sea ice extent in Alaskan waters. The AIS data was also sorted by ship hull types to see how the different sectors of the Alaskan maritime industry are changing over time in ice-covered areas.@en

In the FATICE project we have addressed the fatigue damage on fixed offshore structures exposed to drifting ice. This is an important challenge in the development of energy production from offshore wind in the Baltic and involves at least five element: a) define ice statistics, b) predict the structural response (ice-structure interaction simulations), c) estimate the fatigue damage and d) carry out scale-model tests. We have used the Copernicus database and simple analytical equations to define the large-scale ice statistics and studied down-scaling to structural scale by comparing with ice load data on the Norströmsgrund lighthouse (LOLEIF and STRICE data). The VANILLA model allows for ice-structure interaction simulations and has been validated against the full-scale LOLEIF and STRICE data and against the model-scale ice in HSVA. The fully coupled and the traditional methods are compared. In the fatigue estimations studies the assumption of linear damage accumulation is challenged and load combinations from wave, wind and ice studied by assessing simulated time-series of the different loads. The main results is that sea ice cause the higher loads than wind and waves do , but the cumulative frequency of ice loads is much smaller than for wind and waves. The traditional model-scale ice tends to be too soft and/or too viscous so that a realistic breaking pattern combined with realistic force-time series is not been obtained for large aspect ratios. HVA has developed a crushing model ice (ICMI) in which the ice crystals are larger and the texture more uniform.@en

Pressures at the ice-structure interface during model-scale ice-structure interaction are often measured with tactile sensors. Resulting datasets usually include large volume of data along with some measurement error and noise; therefore, it is inherently hard to extract the hidden fluctuating pressures in the system. Identifying the deterministic pressure fluctuation in ice-induced vibrations is essential to understand this complex phenomenon better. In this paper, we discuss the use of two different multivariate analysis techniques to decompose an ensemble of measured pressure data into spatiotemporal modes that gives insights into pressure distributions in ice-induced vibrations. In particular, we use proper-orthogonal decomposition (POD) and inexact robust principal component analysis (IRPCA) in conjunction with measurements of intermittent crushing at different ice speeds. Both decompositions show that most of the energy is captured in a ten-dimensional space; however, the corresponding eigenvalues are different between the decompositions. While POD-based modes have low energy contributions at the first subspace dimensions, IRPCA-based modes have larger energy contributions. This result is consistent with the reconstruction of the time history of the pressure sum using first three empirical modes, where POD and IRPCA-based modes yield similar accuracy at the same subspace dimension. Although both methods successfully illustrate the dominant pressure modes that are active in the system, IRPCA method is found to be more effective than POD in terms of differentiating the contribution of each mode because of its ability to better separate low-rank and sparse components (measurement error and/or noise) in the dataset.@en