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Keyword: data science

paper

Extreme nonlinear ship response estimations by active learning reliability method and dimensionality reduction for ocean wave

Tomoki Takami*, Masaru Kitahara, Jørgen Juncher Jensen, Sadaoki Matsui

An efficient extreme ship response prediction approach in a given short-term sea state is devised in the paper. The present approach employs an active learning reliability method, named as the active learning Kriging + Markov Chain Monte Carlo (AK-MCMC), to predict the exceedance probability of extreme ship response. Apart from that, the Karhunen-Loève (KL) expansion of stochastic ocean wave is adopted to reduce the number of stochastic variables and to expedite the AK-MCMC computations. Weakly and strongly nonlinear vertical bending moments (VBMs) in a container ship, where the former only accounts for the nonlinearities in the hydrostatic and Froude-Krylov forces, while the latter also accounts for the nonlinearities in the radiation and diffraction forces together with slamming and hydroelastic effects, are studied to demonstrate the efficiency and accuracy of the present approach. The nonlinear strip theory is used for time domain VBM computations. Validation and comparison against the crude Monte Carlo Simulation (MCS) and the First Order Reliability Method (FORM) are made. The present approach demonstrates superior efficiency and accuracy compared to FORM. Moreover, methods for estimating the Mean-out-crossing rate of VBM based on reliability indices derived from the present approach are proposed and are validated against long-time numerical simulations.

Marine Structures / 2024
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Modelling decarbonization of the maritime and aviation sectors

Sebastian Marco Franz

Global climate change, which is largely attributed to human activity, is one of the foremost challenges of the 21st century. In recent times, there have been notable alterations in the Earth's climate, resulting in profound impacts on ecosystems and biodiversity. These alterations are caused by greenhouse gas, such as carbon dioxide, methane, and nitrous oxide. Greenhouse gas emissions are caused by practices such as deforestation, industrial operations, and the combustion of fossil fuels in vehicles, vessels, aircraft, and manufacturing facilities. The maritime and aviation industry is currently responsible for approximately 6% of global greenhouse gas emissions. Due to logistical and economic constraints, these industries are heavily reliant on liquid fuels, making direct electrification options unavailable for large parts of these sectors. As a result, these sectors are considered ‘hard to abate’. Understanding the future climate mitigation challenges associated with the maritime and aviation sectors is crucial in shaping effective policy measures, avoiding stranded assets, and preserving the chance to meet Paris Agreement-compatible emission reduction pathways.

This thesis identifies three main challenges and proposes modelling approaches to address them when modelling decarbonization pathways for the aviation and maritime sectors. From these challenges, research gaps have been identified that this PhD thesis aims to fill. Three models have been developed for the thesis: a maritime optimization model, a maritime demand model, and an aviation demand model. The modelling landscape and methodology vary across models, ranging from econometrics and data science to mathematical optimization.

To overcome the challenges and fill in the research gaps, three corresponding modelling approaches have been successfully applied:

1. Developing a holistic decarbonization modelling landscape. This includes life-cycle representations of technology costs and emissions, the upscaling of bottleneck technologies, the availability of sustainable biomass, and consideration of competing demand from other industries, as well as representations of policy levers such as carbon pricing or improvements to fuel efficiency.

2. Developing demand models that interpret the underlying scenario narrative consistently (SSP framework).

3. Improving the representation of technological learning for low-carbon technologies in energy system models.

The findings acquired by applying these three modelling approaches are valuable for energy modellers, climate scientists, and policymakers and offer unique insights into the inherent system dynamics associated with decarbonization of hard-to-abate sectors. Utilizing this modelling landscape reveals that current decarbonization efforts for hard-to-abate sectors are insufficient.

Technical University of Denmark / 2024
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paper

Identification of Ships in Satellite Images

Peder Heiselberg, Hasse B. Pedersen, Kristian A. Sorensen, Henning Heiselberg

Satellite imagery has become a fundamental part for maritime monitoring and safety. Correctly estimating a ship's identity is a vital tool. We present a method based on facial recognition for identifying ships in satellite images. A large ship dataset is constructed from Sentinel-2 multispectral images and annotated by matching to the Automatic Identification System. Our dataset contains 7.000 unique ships, for which a total of 16.000 images are acquired. The method uses a convolutional neural network to extract a feature vector from the ship images and embed it on a hypersphere. Distances between ships can then be calculated via the embedding vectors. The network is trained using a triplet loss function, such that minimum distances are achieved for identical ships and maximum distances to different ships. Comparing a ship image to a reference set of ship images yields a set of distances. Ranking the distances provides a list of the most similar ships. The method correctly identifies a ship on average 60 % of the time as the first in the list. Larger ships are easier to identify than small ships, where the image resolution is a limitation.

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing / 2024
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Predicting Underwater Radiated Noise from Ship Propellers

Joseph Praful Tomy

Underwater radiated noise (URN) from ship propellers has attracted increasing interest in recent years due to its adverse environmental effects on marine life and their communication channels. The environmental concern to reduce shipping noise and the industrial requirements for faster computational tools are driving factors that promote research in the specialized domain of hydroacoustics. This thesis deals with the development of such a computationally efficient numerical tool, which can be used in the prediction of underwater radiated noise in the early design phase of propellers.

The numerical model is developed with two major objectives – versatility in assessing the relative contributions from the major propeller-noise generating mechanisms, and rapidity in prediction of overall noise behaviour. It uses the Farassat-1A solid-FWH formulation of the Ffowcs-Williams- Hawkings equation by defining equivalent acoustic sources on the propeller blade, sheet cavity and tip vortex cavity surfaces. In particular, the application of the solid-FWH formulation to the tip vortex cavity model is the major novelty in this thesis.

The hydrodynamic flow solution is obtained from a potential flow based solver ESPPRO, which includes analytical models of sheet cavitation and tip vortex cavitation. The hydroacoustic numerical model developed within this thesis, DoLPHiN, is a Python-based code that is primarily designed to accept input from ESPPRO; but during the research, the code has also been adapted to read input from the commercial, finite-volume-based Navier-Stokes solver, STAR-CCM+.

The numerical model implementations are verified through analytical case studies for simple geometrical shapes, such as a pulsating sphere and an oscillating cylindrical cavity. The verification study is further extended for propeller geometries by identifying approximate reference solutions in simplified operating conditions. The numerical tool is validated for industrial application through comparison of its noise prediction with model-scale and full-scale noise measurements. Specific characteristics of the propeller noise spectrum are identified in order to evaluate its noise prediction capabilities. The uncertainty factors involved when validating with experimental measurements are also explored in detail. Furthermore, a design study is presented, which shows potential use of the numerical tool in practical propeller design and optimization applications.

Technical University of Denmark / 2024
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Social fidelity in cooperative virtual reality maritime training

Pernille Bjørn*, Maja Ling Han, Andrea Parezanovic, Per Larsen

Each year maritime accidents occur at sea causing human casualties. Training facilities serve to reduce the risk of human error by allowing maritime teams to train safety procedures in cooperative real-size immersive simulators. However, they are expensive and only few maritime professionals have access to such simulators. Virtual Reality (VR) can provide a digital all-immersive learning environment at a reduced cost allowing for increased access. However, a key ingredient of what makes all-immersive physical simulators effective is that they allow for multiple participants to engage in cooperative social interaction. Social interaction which allows trainees to develop skills and competencies in navigating situational awareness essential for safety training. Social interaction requires social fidelity. Moving from physical simulators into digital simulators based upon VR technology thus challenges us as HCI researchers to figure out how to design social fidelity into immersive training simulators. We explore social fidelity theoretically and technically by combining core conceptual work from CSCW research to the design experimentation of social fidelity for maritime safety training. We argue that designing for social fidelity in VR simulators requires designers to contextualize the VR experience in location, artifacts, and actors structured through dependencies in work allowing trainees to perform situational awareness, coordination, and communication which are all features of social fidelity. Further, we identify the risk of breaking the social fidelity immersion related to the intent and social state of the participants entering the simulation. Finally, we suggest that future designs of social fidelity should consider not only trainees in the design, but also the social relations created by the instructors’ guidance as part of the social fidelity immersion.

Human-Computer Interaction / 2024
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PiracyAnalyzer: Spatial Temporal Patterns Analysis of Global Piracy Incidents

Maohan Liang, Huanhuan Li, Ryan Wen Liu, Jasmine Siu Lee Lam*, Zaili Yang

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.

Reliability Engineering and System Safety / 2024
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Method for Identification of Aberrations in Operational Data of Maritime Vessels and Sources Investigation

Jie Cai, Marie Lützen, Adeline Crystal John, Jakob Buus Petersen , Niels Rytter

Sensing data from vessel operations are of great importance in reflecting operational performance and facilitating proper decision-making. In this paper, statistical analyses of vessel operational data are first conducted to compare manual noon reports and autolog data from sensors. Then, new indicators to identify data aberrations are proposed, which are the errors between the reported values from operational data and the expected values of different parameters based on baseline models and relevant sailing conditions. A method to detect aberrations based on the new indicators in terms of the reported power is then investigated, as there are two independent measured power values. In this method, a sliding window that moves forward along time is implemented, and the coefficient of variation (CV) is calculated for comparison. Case studies are carried out to detect aberrations in autolog and noon data from a commercial vessel using the new indicator. An analysis to explore the source of the deviation is also conducted, aiming to find the most reliable value in operations. The method is shown to be effective for practical use in detecting aberrations, having been initially tested on both autolog and noon report from four different commercial vessels in 14 vessel years. Approximately one triggered period per vessel per year with a conclusive deviation source is diagnosed by the proposed method. The investigation of this research will facilitate a better evaluation of operational performance, which is beneficial to both the vessel operators and crew.

Sensors (Switzerland) / 2024
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paper

Port selection by container ships: A big AIS data analytics approach

Hongxiang Feng, Qin Lin, Xinyu Zhang, Jasmine Lam*, Wei Yim Yap

Port selection is of vital importance for both port operators and shipping lines. In this contribution, an Automatic Identification System (AIS) big data approach is developed. This approach allows identifying container ships using only AIS data without the need for supplementary information from commercial databases. This approach is applied to investigate the port selection statistics of container ships between Shanghai and Ningbo Zhoushan Port, two of the largest ports in the world in terms of calling frequency, to generate practical insights. Results show that: i) the ratios among large ships, medium ships and small ships of these two ports are both approximately 1: 4: 5; ii) these two ports both have an exclusive (i.e., more feeder ports covered in geographical coverage) and intensive (i.e., more feeder ships deployed in shipping service frequency) collection and distribution network mainly consisting of small ships, but that of Shanghai is more intensive; iii) in terms of ultra-large ships over 380 m, Shanghai has accommodated an extra 18.5% compared to that of Ningbo Zhoushan, this indicates Shanghai's attraction for such vessels in global fleet deployment; iv) the feeder network between Shanghai and Ningbo Zhoushan is weak, and their relationship is actually in competition; v) Ningbo Zhoushan could offer more choices for ultra-large container ships (over 380 m), which implies its greater potential in future port competition; vi) when the depth of channels and berths is sufficient, the distance to hinterland and the convenience of a collection and distribution network begin to get more important in port selection. The empirical findings unveil the decision-making of container lines, competition between ports and implications for shipping policy.

Research in Transportation Business and Management / 2024
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Uncertainty-associated directional wave spectrum estimation from wave-induced ship responses using Machine Learning methods

Ulrik D. Nielsen, Kazuma Iwase, Raphaël E.G. Mounet, Gaute Storhaug

This paper presents an assessment of three methods used for sea state estimation via the wave buoy analogy, where measured ship responses are processed. The three methods all rely on Machine Learning exclusively but they have different output; Method 1 provides bulk parameters, Method 2 yields a point wave spectrum and the wave direction, while Method 3 gives the directional wave spectrum in non-parametric form. The assessment is made using full-scale data from an in-service container ship in cross-Atlantic service. Training and testing of the methods are made using data from a wave radar, and the three methods perform well. An uncertainty measure, equivalently, a trust level indicator, based on the variation between the post-processed outputs of the methods is proposed, and this facilitates determination of estimates with small errors; without knowing the ground truth.

Ocean Engineering / 2024
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Solving for hydroelastic ship response using a high-order finite difference method on overlapping grids at zero speed

Baoshun Zhou, Mostafa Amini-Afshar, Harry B. Bingham, Yanlin Shao, Šime Malenica, Matilde H. Andersen

This work extends an existing seakeeping tool (OceanWave3D-seakeeping) to allow for the efficient and accurate evaluation of the hydroelastic response of large flexible ships sailing in waves. OceanWave3D-seakeeping solves the linearized potential flow problem using high-order finite differences on overlapping curvilinear body-fitted grids. Generalized modes are introduced to capture the flexural responses at both zero and non-zero forward speed, but we focus on the zero speed case here. The implementation of the hydroelastic solution is validated against experimental measurements and reference numerical solutions for three test cases. The ship girder is approximated by an Euler–Bernoulli beam, so only elastic bending deformation is considered and sheer effects are neglected. Some controversy has long existed in the literature about the correct form of the linearized hydrostatic stiffness terms for flexible modes, with Newman (1994) and Malenica and Bigot (2020) arriving at different forms. We provide here a complete derivation of both forms (including the gravitational terms) and demonstrate the equivalence of the buoyancy terms for pure elastic motions.

Marine Structures / 2024
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