Performance data from ships is subject to distributional shifts, sometimes referred to as concept drift. In this study, synthetic monitoring data is simulated for the KVLCC2, considering publicly available reference data and a semi-empirical simulation framework. Neural networks are trained to predict the required shaft power and to overcome the deterioration in model accuracy due to concept drift, three methods of incremental learning are applied and compared: (1) Layer freezing, (2) regularization, and (3) elastic weight consolidation. Furthermore, an implicit methodology for quantifying the changing hull and propeller performance is presented. In addition, a generic feature engineering framework is used for eliminating insignificant features. In two investigations, sudden and incremental concept drift scenarios are examined, and the effect of different uncertainty categories on model performance is studied in parallel based on three different datasets. As a main finding, it is confirmed that data quality is of great importance for accurate machine learning-driven performance monitoring — even in simulated environments. Furthermore, the study shows that freezing layers during incremental learning proves to be most robust and accurate, but it will be part of future work to examine this on actual sensor data.
A practical estimation methodology of the mean added resistance in irregular waves is shown, and the present paper provides statistical analyses of estimates for ships in actual conditions. The study merges telemetry data of more than 200 in-service container vessels with ocean re-analysis data from ERA5. Theoretical estimates relying on spectral calculations of added resistance are made for both long- and short-crested waves and are based on a combination of a parametric expression for the wave spectrum and a semi-empirical formula for the added resistance transfer function. The theoretical estimates are compared to predictions from an indirect calculation of added resistance relying on shaft power measurements and empirical estimates of the remaining resistance components. Overall, the comparison reveals a bias in bow oblique waves and higher sea states of the spectral estimates as well as the large variance of the empirically derived predictions — particularly in beam-to-following waves. One of the study’s main findings, confirming previous studies but based on a much larger dataset than in earlier similar studies, is that added resistance assessment based on in-service data is complex due to significant associated uncertainties.
A practical estimation methodology of the mean added resistance in irregular waves is shown, and the present paper provides statistical analyses of estimates for ships in actual conditions. The study merges telemetry data of more than 200 in-service container vessels with ocean re-analysis data from ERA5. Theoretical estimates relying on spectral calculations of added resistance are made for both long- and short-crested waves and are based on a combination of a parametric expression for the wave spectrum and a semi-empirical formula for the added resistance transfer function. The theoretical estimates are compared to predictions from an indirect calculation of added resistance relying on shaft power measurements and empirical estimates of the remaining resistance components. Overall, the comparison reveals a bias in bow oblique waves and higher sea states of the spectral estimates as well as the large variance of the empirically derived predictions — particularly in beam-to-following waves. One of the study’s main findings, confirming previous studies but based on a much larger dataset than in earlier similar studies, is that added resistance assessment based on in-service data is complex due to significant associated uncertainties.
An adaptive machine learning framework is established for an implicit determination of the performance degradation of a ship due to marine growth, i.e., biofouling. The framework is applied in a case study considering telemetry data of a cruise ship operating predominantly in the Caribbean Sea. The dataset encompasses seven years including three dry-docking intervals and several in-water cleaning events. The COVID-19 period receives special focus due to the drastic change in the operational profile. A main outcome of the study is a comparison of the derived performance estimate to the corresponding results of the industry standard ISO 19030. Additional aspects of the present study include the use of special regularization techniques for incremental machine learning and the increase of transparency through the implementation of prediction intervals indicating model uncertainty. Overall, it is found that the developed machine learning framework shows good agreement with the industry standard underlining its plausibility.
Just as containerized goods appear to flow seamlessly across the planet's oceans, internationalized and standardized certificates present seafaring labor as uniform and seamless. But underneath these certificates are the intimate and unequal entanglements of local masculinity norms, age, and kinship ties that sustain the maritime labor supply chain. In this article, we follow how three young, male seafarers from eastern India find ways to contain piracy risks at work and poverty risks at home, and their sense of obligation as men, sons, husbands, and fathers. By delving into the unequal conditions for industrial male workers from the Global South, this article demonstrates how containerized maritime labor commodities are not uniform but are dependent upon economic inequality and intimate kinship ties to be productive.
Maritime piracy incidents present significant threats to maritime security, resulting in material damages and jeopardizing the safety of crews. Despite the scope of the issue, existing research has not adequately explored the diverse risks and theoretical implications involved. To fill that gap, this paper aims to develop a comprehensive framework for analyzing global piracy incidents. The framework assesses risk levels and identifies patterns from spatial, temporal, and spatio-temporal dimensions, which facilitates the development of informed anti-piracy policy decisions. Firstly, the paper introduces a novel risk assessment mechanism for piracy incidents and constructs a dataset encompassing 3,716 recorded incidents from 2010 to 2021. Secondly, this study has developed a visualization and analysis framework capable of examining piracy incidents through the identification of clusters, outliers, and hot spots. Thirdly, a number of experiments are conducted on the constructed dataset to scrutinize current spatial-temporal patterns of piracy accidents. In experiments, we analyze the current trends in piracy incidents on temporal, spatial, and spatio-temporal dimensions to provide a detailed examination of piracy incidents. The paper contributes new understandings of piracy distribution and patterns, thereby enhancing the effectiveness of anti-piracy measures.
Antifouling properties of unmodified kraft lignin for potential use in marine coatings were investigated. The study was based on preliminary findings that pointed toward lignin’s efficacy against seawater organisms during laboratory tests. Coatings were formulated that contained lignin as a filler and had a pigment volume concentration above the critical pigment volume concentration. This ensured direct interaction between lignin and seawater organisms, as the lignin particles remained incompletely wetted by the binder. Moreover, all formulations were waterborne to mitigate the release of volatile organic compounds. Despite the initial promise, the antifouling performance of the formulated lignin coatings during field experiments at the CoaST Maritime Test Center was limited, and the anticipated mechanism must be reconsidered. Additionally, it was found that high lignin concentrations, while facilitating organism interaction, compromised the coating's mechanical properties. Nevertheless, the waterborne coating formulation introduced here might provide a foundation for other researchers to further investigate lignin’s potential as a bio-based pigment or a filler in coatings.
This study explored xanthan gum hydrogel coatings as an approach to more environmental friendly fouling control strategies. Xanthan gum served as a filler in a conventional rosin/acrylic coating matrix, leading to the formation of a 150 μm thick gel layer on the coating surface upon seawater exposure. While biocide-free xanthan hydrogel coatings did not have significant antifouling capabilities, a synergistic effect between hydrogel and cuprous oxide was observed. During field tests at the CoaST Maritime Test Center (CMTC), it was found that the cuprous oxide concentration could be reduced by at least 50 wt% for the hydrogel coating without compromising the antifouling performance. Two possible causes were identified. First, the hydrogel coating could maintain a higher release rate over a prolonged period and second, the hydrogel was able to accumulate Cu2+, increasing retention time on the surface, creating a hostile environment. A synergistic enhancement in gel strength, yield point, and flow point was observed when xanthan was combined with konjac mannan. While promising for static applications, the rheological assessments of the different gels highlighted challenges for the application in dynamic settings like moving ships.
The sea ice in the Arctic has shrunk significantly in the last decades. The transport pattern has as a result partly changed with more traffic in remote areas. This change may influence on the risk pattern. The critical factors are harsh weather, ice conditions, remoteness and vulnerability of nature. In this paper, we look into the risk of accidents in Atlantic Arctic based on previous ship accidents and the changes in maritime activity. The risk has to be assessed to ensure a proper level of emergency response. The consequences of incidents depend on the incident type, scale and location. As accidents are rare, there are limited statistics available for Arctic maritime accidents. Hence, this study offers a qualitative analysis and an expert-based risk assessment. Implications for the emergency preparedness system of the Arctic region are discussed.