Verification and validation (V&V) is a helpful tool for evaluating simulation errors, but its application in unsteady cavitating flow remains a challenging issue due to the difficulty in meeting the requirement of an asymptotic range. Hence, a new V&V approach for large eddy simulation (LES) is proposed. This approach offers a viable solution for the error estimation of simulation data that are unable to satisfy the asymptotic range. The simulation errors of cavitating flow around a projectile near the free surface are assessed using the new V&V method. The evident error values are primarily dispersed around the cavity region and free surface. The increasingly intense cavitating flow increases the error magnitudes. In addition, the modeling error magnitudes of the Dynamic Smagorinsky–Lilly model are substantially smaller than that of the Smagorinsky–Lilly model. The present V&V method can capture the decrease in the modeling errors due to model enhancements, further exhibiting its applicability in cavitating flow simulations. Moreover, the monitoring points where the simulation data are beyond the asymptotic range are primarily dispersed near the cavity region, and the number of such points grows as the cavitating flow intensifies. The simulation outcomes also suggest that the re-entrant jet and shedding cavity collapse are the chief sources of vorticity motions, which remarkably affect the simulation accuracy. The results of this study provide a valuable reference for V&V research.

Multiphase flows widely exist in various scientific and engineering fields, and strongly compressible multiphase flows commonly occur in practical applications, which makes them an important part of computational fluid dynamics. In this study, an axisymmetric adaptive multiresolution smooth particle hydrodynamics (SPH) model is proposed to solve various strongly compressible multiphase flow problems. In the present model, the governing equations are discretized in cylindrical polar coordinates, and an improved volume adaptive scheme is developed to better solve the problem of excessive volume change in strongly compressible multiphase flows. On this basis, combined with the adaptive particle refinement technique, an adaptive multiresolution scheme is proposed in this study. In addition, the high-order differential operator and diffusion correction term are utilized to improve the accuracy and stability. The effectiveness of the model is verified by testing four typical strongly compressible multiphase flow problems. By comparing the results of adaptive multiresolution SPH with other numerical results or experimental data, we can conclude that the present SPH method effectively models strongly compressible multiphase flows.

For a generalized radiation problem, an infinitely long submerged horizontal cylinder is forced to vibrate periodically in the transverse direction, with a described elastic harmonic motion along its longitudinal direction. A critical frequency corresponds to the described wave number of elastic vibration, and the generalized hydrodynamic coefficients abruptly change in the vicinity of critical frequency. In this work, a numerical examination is carried out to study the characteristics of wave profiles and wave propagation in the vicinity of the critical frequency. Results show that below the critical frequency, the real parts of complex wave profiles have large values in the vicinity of the cylinder and decay to zero with the increasing distance from the cylinder. Meanwhile, the imaginary parts of complex wave profiles are all zero, which explains why the generalized radiation damping is zero when the vibration is less than the critical frequency. At far distances, no radiation wave is observed. When the vibration exceeds the critical frequency, the real and imaginary parts of the wave profiles oscillate harmonically and keep steady amplitudes. In addition, the generated radiation wave propagates obliquely outward. The influence of the cylinder’s submergence depth on the wave profile is also studied, and the results indicate that the amplitude of the wave profile decreases as the submergence depth of the cylinder increases. The 3D wave profiles are graphically presented to show the wave propagation characteristics in the vicinity of the critical frequency for this generalized radiation problem. This study provides a good reference for the interaction between fluid and slender elastic structures.

Demand for faster vessels continues to grow, various high speed vessels have been designed and constructed for military, recreational, and passenger use. Planing vessels, specifically engineered for high-speed travel, require optimization to improve their hydrodynamic performance and stability during design. Reducing resistance and improving longitudinal stability are key challenges in the design of high-speed vessels. Various methods are employed to overcome these challenges, with the use of a transverse step being one of the most common approaches. This study explores the effect of changing the angle of the aft-wise step and incorporates these changes into existing analytical formulas, resulting in new formulas specifically for high-speed vessels equipped with aft-wise steps. This research investigates how the angle of the transverse step affects the hydrodynamic performance and longitudinal stability of high-speed vessels. Based on the results, analytical formulas have been developed to calculate the wetted surface parameters of vessels equipped with an aft-wise transverse step. The study used experimental methods to analyze the vessel’s behavior with six different aft-wise transverse step angles of 0°, 9°, 11°, 13°, 15°, and 17° at three speeds of 8, 10, and 12 m/s. In the experimental tests, the hydrodynamic components of resistance, trim angle, and wetted surface of the vessel were measured. Results indicate that creating an angle in the transverse step substantially improves the hydrodynamic components and longitudinal stability of the vessel. At the optimal angle, the resistance and trim angle of the vessel were reduced by 7.8% and 12.8%, respectively, compared to the base vessel. Additionally, the existing analytical methods for calculating the wetted surface area are more accurate than similar methods

This work investigates the potential of low-pressure, medium-speed dual-fuel engines for cleaner maritime transportation. The thermodynamic performance of these engines is explored using three alternative fuels: liquefied natural gas (LNG), methanol, and ammonia. A parametric analysis examines the effect of adjustments to key engine parameters (compression ratio, boost pressure, and air–fuel ratio) on performance. Results show an initial improvement in performance with an increase in compression ratio, which reaches a peak and then declines. Similarly, increases in boost pressure and air–fuel ratio lead to linear performance gains. However, insufficient cooling reduces the amount of fuel burned, which hinders performance. Exergy analysis reveals significant exergy destruction within the engine, which ranges from 69.96% (methanol) to 78.48% (LNG). Notably, the combustion process is the leading cause of exergy loss. Among the fuels tested, methanol exhibits the lowest combustion-related exergy destruction (56.41%), followed by ammonia (62.12%) and LNG (73.77%). These findings suggest that methanol is a promising near-term alternative to LNG for marine fuel applications.

Numerical simulations were conducted on a 10-blade Sevik rotor ingesting wake downstream of two turbulence-generating grids. These simulations were based on implicit large-eddy simulation (ILES) and the boundary data immersion method (BDIM) for compressible flows, which were solved using a fully self-programmed Fortran code. Results show that the predicted thrust spectrum aligns closely with the experimental measurements. In addition, it captures the thrust dipole directivity of the noise around the rotating propeller due to random pressure pulsations on the blades, as well as the flow structures simultaneously. Furthermore, the differences in the statistical characteristics, flow structures, and low-frequency broadband thrust spectra due to different turbulence levels were investigated. This analysis indicates that the interaction between the upstream, which is characterized by a lower turbulence level and a higher turbulent length of scale, and the rotating propeller results in a lower amplitude in force spectra and a slight increase in the scale of tip vortices.

New renewable energy exploitation technologies in offshore structures are vital for future energy production systems. Offshore hybrid wind–wave power generation (HWWPG) systems face increased component failure rates because of harsh weather, significantly affecting the maintenance procedures and reliability. Different types of failure rates of the wind turbine (WT) and wave energy converter (WEC), e.g., the degradation and failure rates during regular wind speed fluctuation, the degradation and failure rates during intense wind speed fluctuation are considered. By incorporating both WT and WEC, the HWWPG system is designed to enhance the overall amount of electrical energy produced by the system over a given period under varying weather conditions. The universal generating function technique is used to calculate the HWWPG system dependability measures in a structured and efficient manner. This research highlights that intense weather conditions increase the failure rates of both WT and WEC, resulting in higher maintenance costs and more frequent downtimes, thus impacting the HWWPG system’s reliability. Although the HWWPG system can meet the energy demands in the presence of high failure rates, the reliance of the hybrid system on both WT and WEC helps maintain a relatively stable demand satisfaction during periods of high energy demand despite adverse weather conditions. To confirm the added value and applicability of the developed model, a case study of an offshore hybrid platform is conducted. The findings underscore the system’s robustness in maintaining energy production under varied weather conditions, though higher failure rates and maintenance costs arise in intense scenarios.

Wigner–Ville distribution (WVD) is widely used in the field of signal processing due to its excellent time–frequency (TF) concentration. However, WVD is severely limited by the cross-term when working with multicomponent signals. In this paper, we analyze the property differences between auto-term and cross-term in the one-dimensional sequence and the two-dimensional plane and approximate entropy and Rényi entropy are employed to describe them, respectively. Based on this information, we propose a new method to achieve adaptive cross-term removal by combining seeded region growing. Compared to other methods, the new method can achieve cross-term removal without decreasing the TF concentration of the auto-term. Simulation and experimental data processing results show that the method is adaptive and is not constrained by the type or distribution of signals. And it performs well in low signal-to-noise ratio environments.

The deep seabed is known for its abundant reserves of various mineral resources. Notably, the Clarion Clipperton (C–C) mining area in the northeast Pacific Ocean, where China holds exploration rights, is particularly rich in deep-sea polymetallic nodules. These nodules, which are nodular and unevenly distributed in seafloor sediments, have significant industrial exploitation value. Over the decades, the deep-sea mining industry has increasingly adopted systems that combine rigid and flexible risers supported by large surface mining vessels. However, current systems face economic and structural stability challenges, hindering the development of deep-sea mining technology. This paper proposes a new structural design for a deep-sea mining system based on flexible risers, validated through numerical simulations and experimental research. The system composition, function and operational characteristics are comprehensively introduced. Detailed calculations determine the production capacity of the deep-sea mining system and the dimensions of the seabed mining subsystem. Finite element numerical simulations analyze the morphological changes of flexible risers and the stress conditions at key connection points under different ocean current incident angles. Experimental research verifies the feasibility of collaborative movement between two tethered underwater devices. The proposed deep-sea mining system, utilizing flexible risers, significantly advances the establishment of a commercial deep-sea mining system. The production calculations and parameter determinations provide essential references for the system’s future detailed design. Furthermore, the finite element simulation model established in this paper provides a research basis, and the method established in this paper offers a foundation for subsequent research under more complex ocean conditions. The control strategy for the collaborative movement between two tethered underwater devices provides an effective solution for deep-sea mining control systems.

The transportation of oil and gas through pipelines is crucial for sustaining energy supply in industrial and civil sectors. However, the issue of pitting corrosion during pipeline operation poses an important threat to the structural integrity and safety of pipelines. This problem not only affects the longevity of pipelines but also has the potential to cause secondary disasters, such as oil and gas leaks, leading to environmental pollution and endangering public safety. Therefore, the development of a highly stable, accurate, and reliable model for predicting pipeline pitting corrosion is of paramount importance. In this study, a novel prediction model for pipeline pitting corrosion depth that integrates the sparrow search algorithm (SSA), regularized extreme learning machine (RELM), principal component analysis (PCA), and residual correction is proposed. Initially, RELM is utilized to forecast pipeline pitting corrosion depth, and SSA is employed for optimizing RELM’s hyperparameters to enhance the model’s predictive capabilities. Subsequently, the residuals of the SSA-RELM model are obtained by subtracting the prediction results of the model from actual measurements. Moreover, PCA is applied to reduce the dimensionality of the original 10 features, yielding 7 new features with enhanced information content. Finally, residuals are predicted by using the seven features obtained by PCA, and the prediction result is combined with the output of the SSA-RELM model to derive the predicted pipeline pitting corrosion depth by incorporating multiple feature selection and residual correction. Case study demonstrates that the proposed model reduces mean squared error, mean absolute percentage error, and mean absolute error by 66.80%, 42.71%, and 42.64%, respectively, compared with the SSA-RELM model. Research findings underscore the exceptional performance of the proposed integrated approach in predicting the depth of pipeline pitting corrosion.

An efficient algorithm for path planning is crucial for guiding autonomous surface vehicles (ASVs) through designated waypoints. However, current evaluations of ASV path planning mainly focus on comparing total path lengths, using temporal models to estimate travel time, idealized integration of global and local motion planners, and omission of external environmental disturbances. These rudimentary criteria cannot adequately capture real-world operations. To address these shortcomings, this study introduces a simulation framework for evaluating navigation modules designed for ASVs. The proposed framework is implemented on a prototype ASV using the Robot Operating System (ROS) and the Gazebo simulation platform. The implementation processes replicated satellite images with the extended Kalman filter technique to acquire localized location data. Cost minimization for global trajectories is achieved through the application of Dijkstra and A* algorithms, while local obstacle avoidance is managed by the dynamic window approach algorithm. The results demonstrate the distinctions and intricacies of the metrics provided by the proposed simulation framework compared with the rudimentary criteria commonly utilized in conventional path planning works.

Advancements in artificial intelligence and big data technologies have led to the gradual emergence of intelligent ships, which are expected to dominate the future of maritime transportation. Supporting the navigation of intelligent ships, route planning technologies have developed many route planning algorithms that prioritize economy and safety. This paper conducts an in-depth study of algorithm efficiency for a route planning problem, proposing an intelligent ship route planning algorithm based on the adaptive step size Informed-RRT*. This algorithm can quickly plan a short route according to automatic obstacle avoidance and is suitable for planning the routes of intelligent ships. Results show that the adaptive step size Informed-RRT* algorithm can shorten the optimal route length by approximately 13.05% while ensuring the running time of the planning algorithm and avoiding approximately 23.64% of redundant sampling nodes. The improved algorithm effectively circumvents unnecessary calculations and reduces a large amount of redundant sampling data, thus improving the efficiency of route planning. In a complex water environment, the unique adaptive step size mechanism enables this algorithm to prevent restricted search tree expansion, showing strong search ability and robustness, which is of practical significance for the development of intelligent ships.

During normal de-ballasting operations for floating docks, each ballast pump independently manages a specific group of ballast tanks. However, when a pump malfunctions, a connection valve between the two groups of ballast water systems is opened. This allows the adjacent pump to serve as a helper pump, simultaneously controlling two groups of ballast water systems. This study explores a full-scale floating dock’s dynamic behaviours during the de-ballasting operations under this situation through a numerical model. In the developed numerical model, the dock is described as a six-degree-of-freedom rigid body which is subjected to hydrostatic, hydrodynamic, and mooring loads. A hydraulic model of the piping network of the malfunctioning pump and the helper pump is proposed. A modified P-controller regulates opening angles of all tank valves for minimal pitch and roll. Two configurations of the floating dock, i. e., a single floating dock and a floating dock with an onboard vessel, are considered. The numerical results show that the optimal helper pumps can be identified regarding the pumps’ total de-ballasting capacity and the dock’s stability. The most severe scenarios can be determined in term of the dock’s maximum draught differences caused by its roll and pitch. The observed maximum draught differences remain small relative to the dock’s width, indicating the effectiveness of employing helper pumps and the proposed automatic ballast control strategy for one-pump malfunction scenarios.

The need to transport goods across countries and islands has resulted in a high demand for commercial vessels. Owing to such trends, shipyards must efficiently produce ships to reduce production costs. Layout and material flow are among the crucial aspects determining the efficiency of the production at a shipyard. This paper presents the initial design optimization of a shipyard layout using Nondominated Sorting Algorithm-II (NSGA-II) to find the optimal configuration of workstations in a shipyard layout. The proposed method focuses on simultaneously minimizing two material handling costs, namely work-based material handling and duration-based material handling. NSGA-II determines the order of workstations in the shipyard layout. The semiflexible bay structure is then used in the workstation placement process from the sequence formed in NSGA-II into a complete design. Considering that this study is a case of multiobjective optimization, the performance for both objectives at each iteration is presented in a 3D graph. Results indicate that after 500 iterations, the optimal configuration yields a work-based MHC of 163 670.0 WBM-units and a duration-based MHC of 34 750 DBM-units. Starting from a random solution, the efficiency of NSGA-II demonstrates significant improvements, achieving a 50.19% reduction in work-based MHC and a 48.58% reduction in duration-based MHC.

Underwater wireless sensor networks (UWSNs) have emerged as a new paradigm of real-time organized systems, which are utilized in a diverse array of scenarios to manage the underwater environment surrounding them. One of the major challenges that these systems confront is topology control via clustering, which reduces the overload of wireless communications within a network and ensures low energy consumption and good scalability. This study aimed to present a clustering technique in which the clustering process and cluster head (CH) selection are performed based on the Markov decision process and deep reinforcement learning (DRL). DRL algorithm selects the CH by maximizing the defined reward function. Subsequently, the sensed data are collected by the CHs and then sent to the autonomous underwater vehicles. In the final phase, the consumed energy by each sensor is calculated, and its residual energy is updated. Then, the autonomous underwater vehicle performs all clustering and CH selection operations. This procedure persists until the point of cessation when the sensor’s power has been reduced to such an extent that no node can become a CH. Through analysis of the findings from this investigation and their comparison with alternative frameworks, the implementation of this method can be used to control the cluster size and the number of CHs, which ultimately augments the energy usage of nodes and prolongs the lifespan of the network. Our simulation results illustrate that the suggested methodology surpasses the conventional low-energy adaptive clustering hierarchy, the distance- and energy-constrained K-means clustering scheme, and the vector-based forward protocol and is viable for deployment in an actual operational environment.