RISK ASSESSMENT OF ARCTIC NAVIGATION BY USING IMPROVED FUZZY-AHP APPROACH

In this study, navigational risk factors of the Arctic Ocean are defined and numerical weights of each risk are obtained by using Improved Fuzzy Analytical Hierarchy Process (IF-AHP) method after conducting expert consultations. The Northern Sea Route shortens the maritime distance approximately 7000 nautical miles comparing to the conventional Suez Canal route. Therefore, it takes a significant role of being economic and time advantage for global logistics. Its geographical position, presence of ice, heavy weather conditions, strong currents and winds are some risks for Arctic transportation. There always have the possibility of unpredictable catastrophes such as a collision, grounding, hull damage and etc. in this region. Reflections of such unwanted incidents might be very costly for economic, political, environmental and safety concerns. Due to there are limited academic studies regarding to analytical and systematical risk identification and determination of risk levels, this study contributes to complete this academic gap.

Revisiting Trans-Arctic Maritime Navigability in 2011–2016 from the Perspective of Sea Ice Thickness

Arctic navigation has become operational in recent decades with the decline in summer sea ice. To assess the navigability of trans-Arctic passages, combined model and satellite sea ice thickness (CMST) data covering both freezing seasons and melting seasons are integrated with the Arctic Transportation Accessibility Model (ATAM). The trans-Arctic navigation window and transit time are thereby obtained daily from modeled sea ice fields constrained by satellite observations. Our results indicate that the poorest navigability conditions for the maritime Arctic occurred in 2013 and 2014, particularly in the Northwest Passage (NWP) with sea ice blockage. The NWP has generally exhibited less favorable navigation conditions and shorter navigable windows than the Northern Sea Route (NSR). For instance, in 2013, Open Water (OW) vessels that can only safely resist ice with a thickness under 15 cm had navigation windows of 47 days along the NSR (45% shorter than the 2011–2016 mean) and only 13 days along the NWP (80% shorter than the 2011–2016 mean). The longest navigation windows were in 2011 and 2015, with lengths of 103 and 107 days, respectively. The minimum transit time occurred in 2012, when more northward routes were accessible, especially in the Laptev Sea and East Siberian Sea with the sea ice edge retreated. The longest navigation windows for Polar Class 6 (PC6) vessels with a resistance to ice thickness up to 120 cm reached more than 200 days. PC6 vessels cost less transit time and exhibit less fluctuation in their navigation windows compared with OW vessels because of their ice-breaking capability. Finally, we found that restricted navigation along the NSR in 2013 and 2014 was related to the shorter periods of navigable days in the East Siberian Sea and Vilkitskogo Strait, with local blockages of thick ice having a disproportionate impact on the total transit. Shorter than usual navigable windows in the Canadian Arctic Archipelago and Beaufort Sea shortened the windows for entire routes of the NWP in 2013 and 2014.

Machine Learning Incorporated With Causal Analysis for Short-Term Prediction of Sea Ice

Accurate and fast prediction of sea ice conditions is the foundation of safety guarantee for Arctic navigation. Aiming at the imperious demand of short-term prediction for sea ice, we develop a new data-driven prediction technique for the sea ice concentration (SIC) combined with causal analysis. Through the causal analysis based on kernel Granger causality (KGC) test, key environmental factors affecting SIC are selected. Then multiple popular machine learning (ML) algorithms, namely self-adaptive differential extreme learning machine (SaD-ELM), classification and regression tree (CART), random forest (RF) and support vector regression (SVR), are employed to predict daily SIC, respectively. The experimental results in the Barents-Kara (B-K) sea show: (1) compared with correlation analysis, the input variables of ML models screened out by causal analysis achieve better prediction; (2) when lead time is short (<3 d), the four ML algorithms are all suitable for short-term prediction of daily SIC, while RF and SaD-ELM have better prediction performance with long lead time (>3 d); (3) RF has the best prediction accuracy and generalization ability but hugely time consuming, while SaD-ELM achieves more favorable performance when taking computational complexity into consideration. In summary, ML is applicable to short-term prediction of daily SIC, which develops a new way of sea ice prediction and provides technical support for Arctic navigation.

Расчет общей предельной прочности корпуса газовоза

На примере газовоза с ледовым классом Arc-7 проводится сравнение метода расчета предельной прочности отношения моментов и метода малоцикловой усталости. Делаются выводы о преимуществах и недостатках каждого метода. Работа сопровождается подробными расчетами предельной прочности по методу отношения моментов и малоцикловой усталости с числовыми значениями. Данное исследование может быть интересно специалистам, работающим в области расчетов прочности. Ключевые слова: предельная прочность, волновой момент, накопление повреждений, малоцикловая усталость This work covers the comparison of two methods of calculating the ultimate strength: one using the ratio of moments, and the other one using the low-cycle fatigue of a maximum stress range and minimum probability of exceedance – with the example of gas carriers operating in Arctic navigation areas. Detailed conclusions on the advantages the advantages and disadvantages of each method are made. The work is accompanied by detailed calculations and numerical values; may be of interest to specialists working in the strength calculations field. Keywords: ultimate strength, hull bending moment, fatigue damge, low-cycle fatigue Contribution of the authors: the authors contributed equally to this article. The authors declare no conflicts of interest