Abstract The growing demand for lightweight, durable, and environmentally friendly materials has positioned High-Density Polyethylene (HDPE) as a viable alternative in small boat production. Despite its advantages-including recyclability, UV resistance, and low maintenance-HDPE boat production involves a labour-intensive process with significant uncertainty in workforce needs. Existing studies have primarily focused on material performance and production techniques, leaving a gap in workforce optimisation under uncertainty. This study addresses this gap by proposing a two-stage stochastic programming model to forecast and optimise workforce requirements in HDPE boat hull production. The model captures variability in the amount of work and working performance, providing a structured approach to workforce planning. To solve the problem, the Sample Average Approximation (SAA) method is employed, and multiple replications are performed to ensure solution stability and robustness. Computational results indicate that workforce requirements remain relatively consistent across different scenarios, confirming the model’s robustness. Moreover, alternative solutions achieve lower total labour costs without compromising production efficiency. For the case study, the optimised total cost was 38,212 currency units, with workforce needs estimated at 24 man∙days for alignment, 10 man∙days for welding preparation, 10 man∙days for welding, and 29 man∙days for cosmetic ‘touch-up’. The findings provide practical insights into managing labour-related uncertainties in HDPE boat manufacturing, offering both economic and operational benefits. By integrating stochastic optimisation with sustainable material use, the proposed framework contributes to enhancing cost efficiency and production planning in the small boat industry.

Abstract The utilisation of exhaust gas heat from diesel engines is one of the most effective ways to improve the performance of marine power plants. Achieving the same boiler performance, given the reduction in the exhaust gas temperature resulting from increased engine thermal efficiency, has led to an increase in boiler dimensions and has complicated their installation within the exhaust gas ducts of power plants. To reduce these parameters, the efficiency of using elliptical heating surfaces with a controlled flow separation mechanism in the form of a triangular notch has been considered for the intensification of heat transfer processes. It was found that for a main engine power of about 10,000 kW, the steam production rate of the waste heat recovery boiler can reach up to 3,000 kg/h, which is sufficient to fully meet the steam demand under cruising conditions or to significantly reduce the load on the auxiliary boiler. A strength analysis of the elliptical surfaces was carried out to determine the required wall thickness. The wall thickness must ensure that the surface retains its shape under an internal pressure of no less than 0.7 MPa. The analysis was performed using the finite element method. Verification was conducted by comparing the results with available experimental data. The results showed that, depending on the internal pressure, the pipe wall thickness should be within the range of 1 to 2 mm. According to the strength requirements, the pipe material must comply with AISI A 283-C, A516-55, or A182 Grade F12.

Abstract Maritime Automatic Identification System (AIS) monitoring faces critical challenges regarding cost-effectiveness and analytical capabilities. Commercial receivers present significant financial barriers for research and education, while existing software-defined radio (SDR) implementations lack systematic evaluation of advanced analysis tools. This research addresses these gaps by providing comprehensive performance analysis of SDRangel software for AIS signal reception and processing. The research objectives were to evaluate SDRangel’s real-time signal analysis capabilities, assess advanced visualisation tools for maritime traffic monitoring and quantify system reliability through extended acquisition. The experimental setup utilised cost-effective hardware with commercial maritime very high frequency (VHF) antenna. During continuous 24-hour operation, the system successfully demodulated 173,739 AIS messages from both AIS channels. Real-time analysis confirmed high signal stability and demonstrated AIS Time Division Multiple Access (TDMA) structure visualisation; specialised plugins enabled comprehensive vessel tracking and message analysis. Post-processing analysis revealed the detection of a fixed Aid to Navigation (AtoN) station signal at an extreme distance of 193.2 NM, confirming the system’s high sensitivity under specific atmospheric conditions. This research bridges the gap between basic SDR implementations and operational surveillance requirements by demonstrating that SDRangel provides capabilities which were, previously, only available in commercial systems. The software’s modular architecture enables integrated spectral analysis, automated decoding, geographic visualisation, and post-processing, which is essential for research and operational applications. Scientific contributions include empirical validation of open-source SDR reliability, quantitative performance benchmarks and the demonstration of advanced analytical capabilities. Practical advantages encompass significant cost reduction, compared to commercial solutions, flexibility for custom applications, and accessibility for research and education. The findings demonstrate substantial impact potential by democratising access to advanced AIS monitoring, enabling cost-effective research infrastructure for signal propagation studies, and providing comprehensive platforms for maritime communication education.

Abstract With global warming enhancing the navigability of Arctic routes, the accurate prediction of ice resistance during navigation is of great engineering significance for the design and performance evaluation of polar ships. This study proposes a high-fidelity numerical simulation method that combines image recognition with CFD–DEM coupling and a six-degree-of-freedom (6-DOF) dynamic model to predict ship resistance in pack ice conditions. Using ice images from the CEHINAV towing tank experiments in Spain, the watershed image segmentation algorithm was applied to extract the spatial distribution and size information of ice blocks. A digital ice field was then reconstructed by surface injection of ice fragments of various sizes, thereby achieving consistency with the physical ice field. In the fluid–structure interaction simulations, a dynamic overset mesh and 6-DOF motion model were introduced to realistically reproduce the ship’s motion and the ship–ice interactions in the pack ice zone. Numerical simulations under different speeds and ice concentrations show that the average deviation from experimental data remains within 10%, thus confirming the accuracy and reliability of the proposed method. The results indicate that the bow region is the main area of ice loading and resistance concentration, with resistance increasing significantly as the ice concentration rises. The resistance curves exhibit evident nonlinear fluctuations and unloading phenomena. Further regional analysis reveals that the transverse resistance distribution along the hull gradually decreases from the midship toward both sides, while local regions exhibit transient fluctuations, a finding that highlights the complex and unsteady characteristics of ship–ice interactions.

Abstract This study focuses on analysing the effects of wave energy converter (WEC) geometry on the performance of an integrated system comprising a WEC array and a very large floating structure (VLFS). A numerical model of the integrated system is established based on multi-body potential theory, the discrete-module-beam (DMB) method, and the Lagrange multiplier technique. After validating the accuracy of the simulation approach, the present study examines the energy capture efficiency and hydroelastic response of the integrated system with various WEC geometry parameters, including length, width, draft, and shape. Owing to the complexity of the physical model of the integrated system, it is difficult to determine the optimal Power Take-Off (PTO) damping coefficient analytically. Therefore, a numerical search method is employed to obtain the PTO damping coefficients corresponding to different WEC geometry parameters. After a series of numerical simulations, the results reveal that, compared to the other three geometry parameters, the power output of the integrated system is more sensitive to the WEC length. Moreover, the incorporation of the WEC array leads to a significant reduction in the hydroelastic response of the VLFS. In addition, the draft and shape of the WEC exhibit limited influence on the structural response of the VLFS. All in all, the analytical methodology and framework presented in this paper can offer some insights for the design of similar integrated systems.

Abstract Marine oil spills pose a severe and persistent threat to ecosystems and coastal economies. Traditional manual or satellite detection is slow, laborious, and error-prone due to sensor limitations, noise, weather interference, small target sizes, and imbalanced datasets. To address these challenges, this paper proposes a novel, integrated framework for the rapid detection and monitoring of oil spills by using the Internet of Things, unmanned vehicles, and transfer learning. The proposed system uses a multi-layered architecture: a physical layer of visual, infrared, and acoustic sensors deployed on a network of Saildrone unmanned surface vehicles for real-time data acquisition; an edge layer for initial processing and low-latency response; and a cloud layer that uses deep transfer learning for accurate spill identification and classification. We fine-tuned pre-trained ResNet models using a Synthetic Aperture Radar oil spill dataset, achieving a peak accuracy of 97.89% with a three-layer transfer learning configuration, outperforming other tested configurations. The efficiency of the system in real-time data handling and leak localization was validated through a controlled experimental prototype. The results demonstrated a robust solution for minimizing response time and environmental impact. Our framework has been proven to gain about 98.3% model accuracy on drone images for oil spill detection.

Abstract The accuracy of gravimetric measurements is essential in various fields, from the safety of the navigation of unmanned autonomous vessels to searching for natural resources to the level of underground water to the accuracy of geodetic data. Usually, we have to deal with measurements contaminated by environmental noise, as well as noise generated by different mechanical devices, city transportation systems, and human beings; some of those sources have a periodical nature. In the presented article, we consider the problem of the influence of noise on registered data. A gravimeter is, in fact, a vibration analyzer, so most of the artificial noise caused by engines, machines, and other technical systems is included in the final recorded data. By testing a statistical hypothesis, we try to convince the reader that in recorded time series, there is other information that is deterministic in nature and may have an important impact on the analysis of gravimetric data.

Abstract Autonomous underwater vehicles (AUVs) play a critical role in marine resource exploration, maritime patrol, and rescue operations, requiring reliable fault diagnosis for robust operation. This paper proposes a novel AUV fault diagnosis method based on the multidimensional temporal classification transformer (MTC-Transformer) deep learning algorithm. The MTC-Transformer enhances traditional transformer architectures by optimising them for multidimensional time-series data processing and fault classification. It features adaptive automatic feature extraction, superior multi-modal data handling, and improved scalability. Key innovations include gated fusion for combining features from outlier-processed data and kernel principal component analysis-reduced time-series data, a dedicated fault feature amplification mechanism, and a multi-layer perceptron head for classification. Trained and validated on the ‘Haizhe’ small quadrotor AUV fault dataset using double-layer randomised sampling to ensure generalisation, the model achieves exceptional accuracies of 99.51% (training) and 99.44% (validation). Comparative experiments demonstrate its superiority over WDCNN, LSTM-1DCNN, and other benchmarks in handling variable-length sequences and capturing long-range dependencies, confirming its high accuracy, robustness, and practical efficacy for AUV fault diagnosis.

Abstract This paper focuses on assessing the thermal state of a marine gas turbine plant (MGTP), with particular attention being given to the measurement of turbine rotor component temperatures using a specially developed telemetric measurement system (TMS). The study was conducted within the framework of research aimed at evaluating the effects of reduced relative air temperature, which is supplied by the high-pressure compressor (HPC) through nozzle guide vanes (NGV) and a twisting grid (TG) inside the rotor blades (RB) of a high-pressure turbine (HPT), on the thermal state of the HPT rotor. This temperature reduction is achieved via a pre-swirl technique, in which the cooling air is directed toward rotor rotation. The results obtained demonstrate new opportunities for monitoring the thermal condition of turbine components during the modernisation of MGTPs. This approach contributes to improving the overall efficiency of the system by enabling an increase in the gas flow temperature at the inlet to the HPT’s first stage.

Abstract This study investigates the hydrodynamic performance of three distributed propulsion configurations with distinct hull shapes. The influence of duct design and hull geometry on thrust, absorbed power, and the resulting thrust-to-power ratio is explored, and a computational fluid dynamics analysis is conducted with a focus on the relationship between the hull and propulsion system at a constant speed of 3 m/s. The results indicate that a pontoon-shaped hull with matching propulsion configuration yields optimal performance, with superior thrust-to-power ratios and hydrodynamic efficiency. In addition, a comprehensive design graph is presented, with the intention of aiding ship designers in selecting suiTable propulsion configurations for specific vessel types. The findings highlight the importance of integrating hydrodynamic and performance criteria into the design of distributed propulsion systems, and provide insights for the development of next-generation efficient inland vessels. Overall, the study provides practical guidelines for optimising distributed propulsion layouts in shallow-water vessel design.