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"Tankers"
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Limit hypersurface state of art Gaidai reliability approach for oil tankers Arctic operational safety
Due to climate change, commercial vessels may pass now through Arctic pack ice during summer, when ice beginning to melt. While Arctic ice is melting, there are floating broken-ice pieces, impeding navigation. Complex process of vessels and ice interaction includes analysis of areal stochastic ice loadings, acting on the vessel’s hull. Accurate statistical extrapolation methods need to be utilized, to accurately assess critical bow stresses, for the sake of safe Arctic ship design. Numerical analysis has been done in 2 steps. First, oil-tanker bow areal stress distribution has been simulated, using software ANSYS/LS-DYNA. Second, extreme bow pressures are predicted to assess return levels related to long return periods, using the novel reliability approach. This study is focused on oil-tanker bow stress distribution, taking into consideration in situ Arctic Ocean ice-thickness distribution. Vessel’s route being typically chosen to take advantage of summer thinning ice. In terms of ice-thickness statistics in the Arctic region, the onboard dataset being obviously route biased, but it is accurate in terms of the ice-thickness data, specific to the vessel’s route. This study proposed an accurate yet practical methodology for calculating high bow stresses for oil-tankers, voyaging along certain Arctic routes. Primary goal of this study was to validate novel methodology, making it possible to extract pertinent information regarding vessel hull areal pressure system’s extreme dynamics, from either numerically or experimentally recorded time-histories. Methodology presented in this study provides capability to efficiently, yet accurately predict failure or damage risks for a wide range of nonlinear multidimensional vessel hull pressure systems.
Journal Article
Unveiling individual and collective temporal patterns in the tanker shipping network
The global oil tanker shipping network emerges from individual ship and fleet decisions driven by economic, environmental, and operational factors. However, most existing shipping network analysis rely on static, time-aggregated representations, overlooking critical temporality connecting individual vessel routing strategies with both operational efficiency and global cargo flows. To address this gap, we introduce a dual-scale framework complementing sequential motif analysis—capturing recurrent patterns in vessel movement sequences—with Dynamic Mode Decomposition (DMD), extracting temporal dynamics from vessel trajectories to global cargo flows. Using tanker movement data across four vessel classes, we demonstrate that vessels exhibiting diverse regional exploration patterns spend up to 50% more time transporting rather than seeking cargo, indicating greater economic and environmental efficiency. At the system scale, DMD analysis reveals distinct seasonality with an average peak-to-trough amplitude of 16%. Major import regions show synchronous annual demand cycles, while export regions exhibit anti-synchronicity. These temporal patterns, invisible to static analysis, reveal performance differences that enable route optimization for both economic and environmental benefits.
Temporal patterns in global oil shipping reveal tankers exploring varied routes spend up to 50% more time carrying instead of seeking cargo. Tracking cargo flows uncovers annual cycles with 16% swings-insights that could optimize costs and emissions.
Journal Article