This study investigates the effect of various debonding conditions on the dynamic vibrational behavior of GFRP-reinforced steel structures through numerical simulations alongside experimental tests for a selected group of debonding conditions. Intact and debonded samples were analyzed in a variety of sizes ranging from 5

Vessel encounters that evolve in unexpected patterns present a critical safety challenge in busy maritime waterways, yet most detection methods assess geometric proximity at a single moment, without tracking how the interaction evolves or what is regionally normal. An encounter is classified as abnormal when its temporal evolution, how two vessels approach over time, deviates from what is behaviorally normal for that part of the waterway.This study proposes an unsupervised framework that uses linear regression to measure how consistently two vessels converge and how their courses evolve over time, combining these into a Behavioral Index of encounter irregularity, a continuous behavioral descriptor against which cluster-specific anomaly thresholds are applied. Rather than applying a single global threshold, which conflates behaviorally distinct regions, the framework partitions the waterway into homogeneous regions via the SKATER algorithm, evaluating each encounter against its local norm.Applied to AIS data from the Strait of Istanbul over 30–92-day periods, the framework identifies 4.5%–11.5% of encounters as anomalous by region, consistently across temporal scales. Unlike prior methods, it simultaneously identifies both erratic and unusually stable encounters, providing vessel traffic service operators with a behavioral context layer that geometric risk metrics alone cannot supply.

Rapid urban growth and increasing climate pressure on coastal zones are driving interest in Floating Architecture (FA) as an extension of urban infrastructure into marine environments. The long-term viability of such developments depends on the operability of maritime transport systems ensuring accessibility under variable environmental conditions.This study proposes a decision-support framework for assessing offshore transport accessibility under environmental and vessel-stability constraints. The developed Floating Architecture Location Adviser (FALA) integrates spatial parameters, long-term sea-state statistics, and operational ship stability assessment within a deterministic screening procedure constrained by selected aspects of the Second Generation Intact Stability Criteria.The approach enables systematic evaluation of transport operability by classifying the feasibility of shuttle-vessel operations and calculating a Navigation Unavailability Index for candidate movement directions. Application to three European coastal regions demonstrates significant directional variability in transport performance driven by vessel response to representative sea-state conditions.The results show that environmental forcing can lead to loss of operability under specific directional and sea-state conditions. The proposed methodology supports preliminary decision-making for Floating Architecture location assessment and contributes to the planning of more resilient offshore infrastructure.

Surf-riding can negatively affect ship’s directional stability, therefore it remains worth investigating. Although surf-riding in regular waves is well understood, studying it in fully irregular seas remains challenging to date. A crucial step towards understanding of ship behavior in irregular waves is an exploration of the ship response in bi-chromatic waves, which bridges the gap between regular and irregular conditions. This study investigates the application of linear and non-linear Froude-Krylov forces modelling for surging and surf-riding response in bi-chromatic following seas. Three modelling approaches have been utilised: 1 DoF strip model, 1 DoF panel model and 3 DoF panel model. All the models proved to produce qualitatively similar results with some relatively limited quantitative variations. The next research step would be comparing the numerical results with towing tank experimental records in order to draw further conclusions on the applicability of the studied modelling methods.

Safety and comfort of crew and personnel in harsh sea conditions are critical aspects of ship design. However, a comprehensive analysis of ship motions or at least a verification of vulnerability criteria from the second-generation intact stability criteria during the initial design stage are too demanding and time-consuming tasks. To address this challenge, we developed a surrogate model utilizing neural networks to streamline that assessment process. The model integrates a variety of parametric hull variants generated using a parametric CAD modeler, extensive acceleration criteria evaluations according to the second-generation intact stability criteria, and artificial neural networks. By training the neural network on data obtained from stability software, the surrogate model can rapidly predict stability characteristics. This approach significantly reduces the time required for exploration of design space and design assessments, enhancing efficiency and contributing to the design of safer, more comfortable vessels for harsh operating conditions.

The aim of this qualitative study was to numerically investigate surging and surf-riding in bi-chromatic following waves in order to facilitate a model test. The demand to evaluate the robustness of the existing theory was identified as a research gap. The focus of this study is the ship's instantaneous surge velocity under bi-chromatic following wave conditions, which is referred to herein as the ship's response. The study, carried out in a bichromatic wave, reveals new phenomena in ship's response that cannot be found in the monochromatic wave conditions and shows the qualitative difference between regular and irregular waves. Characteristic response patterns have been observed and classified with the intention to be investigated in the experimental study on the towing tank in the nearest future. The results obtained, represent progress toward a deeper understanding of the surf-riding phenomenon in bi-chromatic following waves. Future work will include developing the simple single-degree-of-freedom mathematical model used in this study. It will be based on the comparison of the results of towing tank experiment and the numerical study.

This paper examines the impact of production imperfections on the lift and drag characteristics of hydrofoils, in order to ensure equal opportunities for racing athletes. To achieve this research objective, 18 regatta class approved hydrofoils underwent 3D scanning. Measurements of the sweep, anhedral, and angle of attack for each section were conducted based on the scans. Simple load tests were also employed to assess the deformation of the foil under working conditions. Using the data obtained from these measurements, we developed a simplified parametric model of the foil. Subsequently, CFD simulations were conducted for selected parameter ranges, in order to determine the lift and drag of the hydrofoil. The research revealed variations among the foils, with some significantly affecting the lift characteristics. Of these, variations in the angle of attack had the greatest influence on lift.

Surf-riding is a dynamic phenomenon leading to broaching-to that may result in a collision or lead to a large angle of heel, possibly even capsizing. While the phenomenon of surf-riding in regular waves is well understood, the same cannot be said fully for irregular waves. This research conducted at Gdansk University of Technology explores the phenomena that are intended to replicate cases presented in recent theoretical works. The paper includes a comparison of calculations and experimental results carried out mainly in a qualitative manner, though with an attempt to compare them quantitatively as well. First, the experiments run in a 40 m towing tank using a 1/64 scale ITTC A2 purse-seiner have confirmed the feasibility of reproducing surf-riding in monochromatic waves, even in such a short tank. Furthermore, the results obtained in this study for bi-chromatic following waves constitute the first experimental confirmation of a number of characteristic phenomena for the considered two-component irregular wave that contribute to the broader concept of surf-riding in irregular waves.

The rapidly advancing automation of the maritime industry - for instance, through onboard Decision Support Systems (DSS) - can facilitate the introduction of advanced solutions supporting the process of collision avoidance at sea. Nevertheless, relevant solutions that aim to correctly predict a ship's behavior in irregular waves are only available to a limited extent by omitting the impact of wave stochastics on resulting evasive maneuvers. This is mainly due to the complexity of the phenomena, the existing couplings therein, and the time inefficacy in resolving the problem through real-time simulations. Therefore, this paper attempts to fill this knowledge gap by presenting a probabilistic, data-driven meta-model trained using an extensive set of 6DOF numerical simulations of vessel motions in irregular waves. For this purpose, machine learning adopting causal probabilistic modeling with Bayesian Belief Network (BBN) was employed. The latter offers two-way reasoning in the presence of uncertainty and provides insight into the metamodel's outcome. This, in turn, helps estimate a set of safety-critical parameters for a large passenger ship performing an evasive maneuver. This set comprises a huge quantity of ship turning circle parameters as well as the hull's rotational motions and resulting lateral accelerations, all simulated multiple times to consider the stochastic realization of the waves. The proposed meta-model can be used to assist watchkeeping officers' decisions or raise their awareness concerning the possible consequences of evasive maneuvers performed. The achieved accuracy of the meta-model's prediction lies within a range from 81% to 98%, which makes it suitable for this purpose.

This study analyses different debonding defect scenarios on a multi-layered material composed of carbon fibre-reinforced polymer as a composite coating applied to structural steel, with the aim of applying it to marine structures. The study utilises vibration-based experimental non-destructive diagnostics and numerical simulations to thoroughly examine the debonding extent at four different lengths: 0%, 25%, 75%, and 100% of the total length of the material. The theoretical formulation of the free vibration of the proposed material for the fully bonded condition (0%) is also established using classical beam theory and the principles of composite materials. The four initial natural frequencies in the analysis provide indirect observations of the strength and stiffness properties. The results demonstrate that a reduction in the natural frequencies with increasing debonding size is mainly attributed to a loss of stiffness, rather than to the mass and stress distributions between the layers. Although debonding significantly affects the structure at longer lengths, only a small effect is observed when debonding covers 25% of the length. Based on the results, the experimental methods demonstrate strong agreement with the numerical approaches for determining natural frequencies, despite the unexpected results for the fundamental frequency of vibrations in the theoretical approaches. Eventually, we show that the prediction model established for this purpose accurately predicts the impact of debonding defects on the vibration characteristics of a structure with a high coefficient of determination.

In times of progressive automation of the marine industry, accurate modeling of ship maneuvers is of utmost importance to all parties involved in maritime transportation. Despite the existence of modern collision-avoidance algorithms using 6DOF motion models to predict ship trajectories in waves, the impact of stochastic realization of irregular waves is usually neglected and remains under-investigated. Therefore, herein, this phenomenon and its impact were investigated in the case study of the passenger ship's turning. To this end, statistical and spatiotemporal distributions of ship positions and corresponding trajectory parameters were analyzed. This was made using massive 6DOF simulation data with particular attention to the observed extremes. Additionally, the minimum number of wave realizations has been determined using different methods in various simulation scenarios and afterward compared concerning parameters' impact and existing dependencies. The results indicate that for simulated scenarios, the required number of wave realizations should be at least 20, but in rough seas should be greater than 30. These values satisfy an acceptable and operationally reasonable error limit reaching 15% of the ship's length overall. The obtained results may be of interest to autonomous ship developers, scholars, and marine industry representatives working on intelligent collision-avoidance solutions and ship maneuvering models.

Abstract A sailing yacht is a human-centred product which design revolves primarily around the wants and desires of her future owner. These preferences are in most cases immeasurable e.g., the personal aesthetic feeling, a need of comfort, speed, safety etc. The aims of this paper are firstly to demonstrate that these likings can be classified and represented numerically, and secondly to indicate that they correlate with the type of the owned yacht. As a case study, the owners’ preferences regarding deck equipment are considered. They are determined by pairwise comparisons of the grades of importance of features previously defined by the yacht owners, which is a part of the Analytic Hierarchy Process (AHP) method used in this study. In result a quantitative representation of a studied preferences is established. Furthermore, they correlate with the type of the operated yacht. The findings of the current study show that the yacht owners’ preferences can be represented numerically, what leads to a utilitarian conclusion that concerns the support and even some degree of automation of the design process.

A sailing yacht is a human-centred product, the design of which revolves primarily around the wants and desires of the future owner. In most cases, these preferences are not measurable, such as a personal aesthetic feeling, or a need for comfort, speed, safety etc. The aims of this paper are to demonstrate that these preferences can be classified and represented numerically, and to show that they are correlated with the type of yacht owned. As a case study, the owner’s preferences for deck equipment are considered. These are determined by pairwise comparisons of the importance rankings for features previously defined by yacht owners, following the analytic hierarchy process (AHP) method. As a result, a quantitative representation of these preferences is established, and they are shown to be correlated with the type of yacht. The findings of the current study show that the yacht owners’ preferences can be represented numerically, leading to a utilitarian conclusion that concerns the support and even some degree of automation of the design process.

The main contribution of this paper is a numerical ship motion model of NTNU's research vessel Gunnerus, capturing the surge, sway, roll, and yaw dynamics when sailing in uniform and steady currents. The model utilizes a crossflow drag formulation for the transverse viscous loads, and it includes a nonlinear formulation for the propulsion and steering loads provided by two azipod thrusters. A wide range of experimental data obtained from sea trials are used for model calibration and validation. The model is intended for development of Decision Support Systems (DSS) that provide the helmsman with recommendations for safe maneuvers. As a demonstration, the model is used to generate input to a previously proposed DSS solution, which uses offline simulations to create a database of the critical navigation area for different encounter scenarios. Additionally, we propose a DSS solution that uses online simulations to predict the future ship trajectory under guidance of a virtual autopilot. The virtual autopilot is designed using a novel hybrid control barrier function formulation to predict the need of evasive maneuvers for collision avoidance.

The average accident frequency is essential for quantitative risk analysis and is conventionally estimated from accident statistics. This paper has systematically synthesised the knowledge on statistical errors and offered the missing instructions, a framework, for determining the minimum sample size and the margin of error (MOE) when calculating the average accident frequency from an accident database at hand. We have applied this framework to representative accident datasets in the maritime domain and presented the revealing results that can already be used in QRAs based on these datasets. The findings are useful to both QRA analysts and policy makers. Interestingly, the framework application has revealed that the determined minimum sample sizes would exceed the datasets available in existing maritime casualty databases by decades, requiring at least 10% MOE to be factored into pertinent QRAs. By the same token, the earlier notable QRAs (developed as part of formal safety assessments in support of rule making) had to consider the MOE of over 30%, given the sample sizes used, likely shifting the conclusions they arrived at. Other findings of the application have shown that the average accident frequencies for large passenger ships have remained constant over the past 40 years.

Although the safety of prospective Maritime Autonomous Surface Ships will largely depend on their ability to detect potential hazards and react to them, the contemporary scientific literature lacks the analysis of how to achieve this. This could be achieved through an application of leading safety indicators. The aim of the performed study was to identify the research directions of leading safety indicators in three safety-critical operational aspects of Maritime Autonomous Surface Ships: collision avoidance, intact stability, and communication. To achieve this, literature review is performed, taking into account scientific documents including journal and conference papers. The results indicate that the need for establishing operational leading safety indicators is recognized by numerous scholars, who sometimes make suggestions of what the set of indicators shall consist of. Some leading safety indicators for autonomous vessels are readily identifiable in the scientific literature and used in current practice. However, the research effort is lacking a holistic approach to the issue.