Search Results (155)

Reducing hydrodynamic resistance remains a central concern in modern ship design. The Simulation-Based Design technique offers high-fidelity optimization through computational fluid dynamics, but this comes at the cost of computational efficiency. In contrast, surrogate models trained offline can accelerate the process but often compromise on accuracy. To address this issue, this study proposes a hybrid optimization framework connecting a computational fluid dynamics solver and a convolutional neural network surrogate model within a dual-mode mechanism. By comparing selected computational fluid dynamics evaluations with surrogate predictions during each iteration, the system is able to balance the precision and efficiency adaptively. The framework integrates a particle swarm optimizer, a free-form deformation modeler, and a dual-mode solver. Case studies on three benchmark hulls including KCS, KVLCC1, and JBC have shown 3.40%, 3.95%, and 2.74% resistance reduction, respectively, with computation efficiency gains exceeding 44% compared to the traditional Simulation-Based Design process using full computational fluid dynamics. This study provides a practical attempt to enhance the efficiency of hull form optimization while maintaining accuracy. Full article

In response to the urgent need for reducing greenhouse gas emissions, the Hybrid Contra Rotating Propeller (HCRP) system, which combines a main engine-driven propeller with an electrically driven Podded propeller, has become a promising propulsion system for CO₂ reduction. This paper presents a new power estimation method for ships with HCRP and outlines the required model test procedures. This study proposes a power estimation method tailored for ships equipped with HCRP and outlines towing tank test procedures required for validation. The method separately evaluates open water characteristics of each propeller and accounts for interactions between the propellers, pod, and hull. Sea trials on an actual vessel were conducted, including speed trials at constant rotational speed ratios and variation tests. These trials confirmed the method’s ability to predict propulsion performance across a wide range of ship speeds. The estimated error in total output from the main engine and generator was within 5% at low output and more accurate near the design speed for the tested case. Furthermore, the method accurately estimates the relationship between rotational speed ratio and power distribution between the main engine and generator for the pod motor, demonstrating its effectiveness for performance prediction and design optimization of HCRP-equipped vessels. Full article

This study addresses the limitations of buoyancy factor and compensation capacity in pressure hulls for full-ocean-depth underwater gliders operating in extreme deep-sea conditions. A novel lightweight multifunctional composite structure pressure hull (CSPH) is proposed, utilizing a carbon fiber cylindrical shell as the primary load-bearing structure and silicone oil as the buoyancy compensation medium. A mechanical model of the carbon fiber cylindrical shell under hydrostatic pressure was developed based on three-dimensional elastic mechanics theory. Furthermore, a comprehensive performance evaluation model for the CSPH was created, incorporating both the buoyancy factor (B_f) and buoyancy fluctuation coefficient (f_B). The NSGA-II optimization algorithm was employed to simultaneously minimize B_f and f_B by co-optimizing the carbon fiber ply parameters and the silicone oil volume (V_C). This optimization resulted in a Pareto optimal solution balancing buoyancy and compensation performance. The accuracy of the mechanical model and optimization results was validated through finite element analysis and pressure testing. The results show that, compared to traditional metallic pressure hull designs, the CSPH reduces the buoyancy factor by 48% and enhances buoyancy compensation performance by 2.5 times. The developed CSPH has been successfully deployed on the “Sea-Wing 11000” full-ocean-depth underwater glider, significantly improving its endurance and motion stability for long-term deep-sea observation missions. Full article

Artificial Intelligence (AI) is increasingly integrated into ship design workflows, offering enhanced capabilities for hydrodynamic and structural optimization. This review focuses on AI-based methods applied to key design tasks such as hull resistance prediction, structural weight reduction, and performance-driven form optimization. Techniques examined include deep neural networks (DNNs), support vector machines (SVMs), tree-based ensemble models, genetic algorithms (GAs), and surrogate modeling approaches. Comparative analyses from the literature indicate that ensemble tree methods, such as XGBoost, have achieved predictive accuracies up to $R^2$ = 0.995 in speed–power modeling, marginally surpassing DNN performance, while GA-based structural optimization studies have reported weight reductions exceeding 10%. The findings confirm that no single method is universally superior; rather, effectiveness depends on the problem definition, data quality, and computational resources available. Hybrid strategies that combine physics-based modeling with data-driven learning have demonstrated improved generalization, reduced data requirements, and enhanced interpretability. Practical challenges remain, including limited access to open high-fidelity datasets, the computational demands of complex models, and balancing predictive accuracy with explainability. The review concludes that AI should be employed as a complementary toolkit to augment human expertise, with method selection guided by design objectives, constraints, and integration within the broader ship design process. Full article

Meeting the International Maritime Organization’s (IMO) 2050 targets for reducing greenhouse gas (GHG) emissions requires cost-effective solutions that minimize wind resistance without compromising safety, particularly for medium-sized multipurpose vessels (MPVs), which have been underrepresented in prior research. This study numerically evaluates 20 bow-mounted NaviScreen configurations using a coupled high-fidelity computational fluid dynamics (CFD) and finite element analysis (FEA) approach. Key design variables—including contact angle (35–50°), lower-edge height (1.2–2.0 m), and horn position (3.2–5.3 m)—were systematically varied. The sloped Type-15 shield reduced aerodynamic resistance by 17.1% in headwinds and 24.5% at a 30° yaw, lowering total hull resistance by up to 8.9%. Nonlinear FEA under combined dead weight, wind loads, and Korean Register (KR) green-water pressure revealed local buckling risks, which were mitigated by adding carling stiffeners and increasing plate thickness from 6 mm to 8 mm. The reinforced design satisfied KR yield limits, ABS buckling factors (>1.0), and NORSOK displacement criteria (L/100), confirming structural robustness. This dual-framework approach demonstrates the viability of NaviScreens as passive aerodynamic devices that enhance fuel efficiency and reduce GHG emissions, aligning with global efforts to address climate change by targeting not only CO2 but also other harmful emissions (e.g., NOx, SOx) regulated under MARPOL. The study delivers a validated CFD-FEA workflow to optimize performance and safety, offering shipbuilders a scalable solution for MPVs and related vessel classes to meet IMO’s GHG reduction goals. Full article

Disc-shaped Autonomous Underwater Helicopters (AUHs) offer superior maneuverability but suffer from high hydrodynamic drag, which limits their operational endurance. To address this challenge, this study proposes a robust optimization framework for a novel sea-turtle-inspired AUH. A parametric hull, governed by two dimensionless shape factors based on modified Myring equations, was established to facilitate systematic exploration. To reduce the high computational cost of direct CFD evaluations, a high-precision Gaussian Process Regression (GPR) surrogate model was constructed from a small dataset of 24 samples. The core methodological innovation is T-NSGA-II, an algorithm featuring hyperparameters that are systematically optimized by the Optuna framework. In comparative evaluations, the T-NSGA-II-generated Pareto front demonstrated clear superiority over the standard NSGA-II, identifying designs with significantly lower drag for an equivalent vertical force. A key scientific contribution of this research is the identification of a distinct performance gap on the Pareto front. This phenomenon is interpreted not as an algorithmic artifact but as a ‘natural gap’, reflecting a deep physical trade-off, with potential underlying causes including a critical transition in flow physics or a topological shift in the optimal hull geometries. This work not only delivers a suite of optimized, practical AUH designs but also presents a powerful, intelligent optimization methodology that is capable of revealing fundamental physical trade-offs in complex engineering problems. Full article

This study applies a deep neural network (DNN) to optimize the 22.5 m pontoon hull form of a small waterplane area twin hull (SWATH) vessel with fin stabilizers, aiming to reduce calm water resistance at a Froude number of 0.8 under even keel conditions. The vessel’s resistance is simplified into three components: pontoon, strut, and fin stabilizer. Four design parameters define the pontoon geometry: fore-body length, aft-body length, fore-body angle, and aft-body angle. Computational fluid dynamics (CFD) simulations using STAR-CCM+ 2302 provide 1400 resistance data points, including fin stabilizer lift and drag forces at varying angles of attack. These are used to train a DNN in MATLAB 2018a with five hidden layers containing six, eight, nine, eight, and seven neurons. K-fold cross-validation ensures model stability and aids in identifying optimal design parameters. The optimized hull has a 7.8 m fore-body, 6.8 m aft-body, 10$°$ fore-body angle, and 35$°$ aft-body angle. It achieves a 2.2% resistance reduction compared to the baseline. The improvement is mainly due to a reduced Munk moment, which lowers the angle of attack needed by the fin stabilizer, thereby reducing drag. The optimized design provides cost-efficient construction and enhanced payload capacity. This study demonstrates the effectiveness of combining CFD and deep learning for hull form optimization. Full article

To improve collision risk prediction in high-traffic coastal waters and support real-time decision-making in maritime navigation, this study proposes a regional collision risk prediction system integrating the Computed Distance at Collision (CDC) method with an Adaptive Neuro-Fuzzy Inference System (ANFIS). Unlike Distance at Closest Point of Approach (DCPA), which depends on the position of Global Positioning System (GPS) antennas, Computed Distance at Collision (CDC) directly reflects the actual hull shape and potential collision point. This enables a more realistic assessment of collision risk by accounting for the hull geometry and boundary conditions specific to different ship types. The system was designed and validated using ship motion simulations involving bulk and container ships across varying speeds and crossing angles. The CDC method was used to define collision, almost-collision, and near-collision situations based on geometric and hydrodynamic criteria. Subsequently, the FIS–CDC model was constructed using the ANFIS by learning patterns in collision time and distance under each condition. A total of four input variables—ship speed, crossing angle, remaining time, and remaining distance—were used to infer the collision risk index (CRI), allowing for a more nuanced and vessel-specific assessment than traditional CPA-based indicators. Simulation results show that the time to collision decreases with higher speeds and increases with wider crossing angles. The bulk carrier exhibited a wider collision-prone angle range and a greater sensitivity to speed changes than the container ship, highlighting differences in maneuverability and risk response. The proposed system demonstrated real-time applicability and accurate risk differentiation across scenarios. This research contributes to enhancing situational awareness and proactive risk mitigation in Maritime Autonomous Surface Ship (MASS) and Vessel Traffic System (VTS) environments. Future work will focus on real-time CDC optimization and extending the model to accommodate diverse ship types and encounter geometries. Full article

This research develops a dual physics-based machine learning system to forecast fuel consumption and CO₂ emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article

This study investigated the effect of KOH activation on biochar, with a focus on how porosity and potassium content influence microwave-assisted catalytic biodiesel production, using experimental design approaches. Activated biochar was synthesized from oat hull waste through KOH activation, followed by pyrolysis under controlled conditions. The biochar was characterized through chemical, morphological, and physical analyses, and its catalytic performance in converting used waste cooking oil (WCO) into biodiesel was evaluated using methanol as the acyl acceptor and microwave irradiation to optimize the reaction via experimental design. Results revealed that increasing the KOH/biomass ratio significantly enhanced the specific surface area (SSA) of the catalyst, achieving a maximum SSA of 637.28 m²/g under optimal pyrolysis conditions: 600 °C for 3 h with a KOH/biomass ratio of 2. A maximum fatty acid methyl ester (FAME) yield of 100% was achieved within 1 min of microwave-assisted reaction using an optimized catalyst dosage of 2.5%, a WCO/MeOH molar ratio of 1/12, and a reaction temperature of 150 °C, with the catalyst being successfully recycled across three cycles. An economic and energy evaluation estimated a catalyst production cost of USD 176.97/kg and a biodiesel production cost of USD 8.9/kg of FAMEs. This research provides a straightforward and cost-effective approach for biofuel production. Full article

As an innovative underwater propulsion technology, the rim-driven thruster (RDT) has garnered increasing attention due to its advantages over conventional diesel or gas turbine propulsion systems, including reduced noise, higher efficiency, and a compact structure. However, traditional hull–engine–propeller matching theories are not directly applicable to RDTs because of their unique shaftless and ducted characteristics. Based on conventional hull–engine–propeller matching theory and propeller design methodology, this study proposes a novel hull–engine–propeller matching approach tailored specifically to RDTs. The method enables rapid matching by using open-water characteristics for hull–engine–propeller matching. In the absence of open-water test data for shaftless propellers, key parameters derived from ducted propeller tests are used for matching based on open-water characteristics to design the shaftless propeller. The propeller is then optimized through computational fluid dynamics (CFD) simulations to achieve the required thrust performance, effectively enabling an equivalent replacement. The proposed method provides a practical framework for selecting and designing RDTs, improves overall propulsion efficiency, and offers specific guidelines for determining optimal motor design parameters. Full article

With the natural evolution of the Arctic route and advancements in related technologies, the development of new green ice-class ships is becoming a key technological breakthrough for the global shipbuilding industry. As a special vessel form that must perform icebreaking operations and undertake long-distance ocean voyages, an ice-class ship requires sufficient icebreaking capacity to navigate ice-covered water areas. However, since such ships operate for most of their time under open water conditions, it is also crucial to consider their resistance characteristics in these environments. Firstly, this paper employs linear interpolation to extract wind, wave, and sea ice data along the route and calculates the proportion of ice-covered and open water area in the overall voyage. This provides data support for hull form optimization based on real sea state conditions. Then, a resistance optimization platform for ice-class ships is established by integrating hull surface mixed deformation control within a scenario analysis framework. Based on the optimization results, comparative analysis is conducted between the parent hull and the optimized hull under various environmental resistance scenarios. Finally, the optimization results are evaluated in terms of energy consumption using a fuel consumption model of the ship’s main engine. The optimized hull achieves a 16.921% reduction in total resistance, with calm water resistance and wave-added resistance reduced by 5.92% and 27.6%, respectively. Additionally, the optimized hull shows significant resistance reductions under multiple wave and floating ice conditions. At the design speed, calm water power and hourly fuel consumption are reduced by 7.1% and 7.02%, respectively. The experimental results show that the hull form optimization process in this paper can take into account both ice-region navigation and ice-free navigation. The design ideas and solution methods can provide a reference for the design of ice-class ships. Full article

The design process for motor yachts primarily relies on the experience of designers, who draw upon their knowledge gained from working on similar hull forms. However, when a new concept is to be developed, the experience garnered from standard platforms may not suffice for achieving a successful design within a short timeframe. Designing a motor yacht involves considering multiple aspects of ship hydrodynamics, including resistance, propulsion, seakeeping, and maneuverability. While these factors have been extensively discussed for different types of ships, a comprehensive joint investigation of hulls, such as those of motor yachts, is noticeably absent in the available literature. This paper aims to fill that gap by providing guidelines for the design of motor yachts with lengths ranging from 20 to 40 m. As part of a preliminary study, a series of 15 yacht hulls were developed, starting from a reference hull form. The resistance, seakeeping and maneuverability performance of these hulls were assessed under specified environmental conditions and speeds, following the ISO 22834:2022 guidelines for comfort assessment. The calculations produced response surfaces detailing the hydrodynamic properties for this series of yachts as functions of the main dimensions of the hulls. Ultimately, these responses assist in identifying optimal design solutions for the main dimensions of a new motor yacht within the 20 to 40 m length range. Full article

The winch traction system for shipboard aircraft, when operating in a marine environment, is subjected to additional forces and moments due to the complex motion of the hull. These loads pose significant threats to the safety of the aircraft during the traction process. To address the safety issues under complex sea conditions, this paper adopts harmonic functions to describe the rolling, pitching, and heaving motions of the hull. A theoretical analytical model of the three-winch traction system, considering the intricate coupling motions of the ship, is established. Unlike previous studies that often simplify ship motion or focus on single-component modeling, this work develops a complete, whole-system dynamic model integrating the winch system, rope, aircraft structure, and ship interaction. The dynamic characteristics of the small-deck winch traction system are investigated, with particular focus on the influence of the rear winch position, driving trajectory, and ship motion on the system’s dynamics and safety. This research is innovative in systematically exploring the dynamic safety behavior of a three-winch traction system operating under small-deck conditions and complex sea states. The results show that as the distance between the two rear winches increases, the lateral force on the tire decreases. Additionally, as the aircraft’s turning angle increases, the front winch rope force also increases. Moreover, with higher sea condition levels and wind scales, the maximum lateral force on the tires increases, leading to a significant reduction in the stability and safety of the winch traction system. This is particularly critical when the sea condition level exceeds 3 and the wind scale exceeds 6, as it increases the risk of tire sideslip or off-ground events. This research has substantial value for enhancing the safety and stability of winch traction systems on small decks, and also provides a theoretical basis for traction path design, winch position optimization, and the extension of the service life of key system components, demonstrating strong engineering applicability. Full article

In this paper, we develop technology to improve the stability and quality of boat equipment manufacturing through vacuum injection process fusion to increase the safety of boats. Safe mold design and fabrication are carried out to determine the resin flow rate and water flow rate of a boat, and the performance of vacuum maintenance work is guaranteed through the tensile and compressive strength of the manufactured hull and deck. When manufacturing the boat air mechanism (Aerostat), the adhesion between equipment materials and the deformation of the joints are very important factors for safety. Due to the nature of equipment manufacturing, process fusion to minimize manual process minimizes deformation after manufacturing through accurate manufacturing ratio. Accordingly, it is possible to accurately control the mixing ratio of resin and hardener as optimal conditions for boat drying and securing safety, and to convert optimal information into a database by analyzing working conditions over time such as resin flow rate and flow rate, thereby improving durability and quality. Through this, it is expected that production efficiency and safety design will be improved by enabling efficient production process management with a small number of personnel. Full article