Applied Ocean Research最新文献

Considering the fluid-structure impacting failure problems with large size, this paper presents an extended multi-resolution smoothed particle hydrodynamics (SPH)-peridynamics (PD) coupling model. The influence domain and the interpolation smoothing length are innovatively introduced to reconstruct governing equations for interactions between particles of diverse resolutions, ensuring system-wide momentum conservation. Then, an adaptive multi-level cell neighborhood search (AMCNS) algorithm is presented, designed to reduce the time of neighborhood search in multi-resolution simulations. Several 2D and 3D validation tests demonstrate the accuracy of the multi-resolution SPH-PD model in describing solid deformation and fluid impact pressure. The multi-resolution SPH-PD model significantly enhances computational efficiency and can depict the process of structural failure under conditions where a wide difference in scale between waves and structures.

The hydroelastic behavior of a forced circular elastic floating plate is analyzed, while considering the existence of vertical barrier. This study encompasses different edge conditions such as free, simply supported and built-in edge conditions. The eigenfunction matching method is employed with circular symmetry to obtain the solution in frequency-domain. The analysis in time-domain is performed by considering Gaussian forcing at different points on the plate, utilizing the Fourier transform for analysis. The study examines the vertical force acting on the plate and the time-dependent deflection at the point of force application, taking into account different permeable barriers and fluid depths. Additionally, the numerical findings are assessed and compared to the current ones for validation. The findings indicate that the plate encounters maximum force under a built-in edge condition. Furthermore, the time-dependent deflection of the point of forcing decreases with rising values of both the real and imaginary parts of the porous effect parameter $G$ over the time. The outcomes arising from integrating a circular elastic plate with a porous barrier will provide valuable insights into how the plate responds to abrupt forces, similar to those experienced in seismic events or unforeseen disturbances and it also helps in the design of structures capable of enduring unexpected impacts or shocks, among other considerations.

Aquatic animals have evolved diverse swimming techniques. They have demonstrated abilities to harness energy from vortices, particularly the Kármán vortex street, resulting in enhanced thrust. However, gaps remain in comprehensively understanding the factors influencing this increased thrust and the specific hydrodynamic characteristics involved. In this study, we studied an undulating foil downstream a circular cylinder to further understand the flow control mechanism involved in optimizing energy capture from hydrodynamic disturbances. We utilised numerical simulations with a moving adaptive mesh in laminar flow. We found that the leading vortex and secondary vortex at the foil's leading edge, originating from the Kármán vortex, played a crucial role in thrust enhancement. The undulating foil was more efficient in capturing energy from the Kármán vortex street than a stationary foil. When the foil was nearer to the cylinder, the energy capture was more evident, leading to intricate vortex patterns and easier leading vortex and secondary vortex generation. The foil's lift initially rose with closer proximity but decreased with increased distance. Our results showed that for minimal drag and optimal lift, the cylindrical body's position is closely tied to the interaction between the Kármán vortex street and the undulating foil. These insights can be applied in applications of designing efficient propulsion systems for underwater vehicles and optimising energy harnessing mechanisms in marine environments.

This paper investigates the fluid-structure interaction process of high-speed vehicles during water entry using the Arbitrary Lagrangian-Eulerian method. Through mesh independence verification and comparison of numerical simulation results with experimental data and empirical formulas, the reliability and accuracy of the computational method are confirmed. The study comprehensively analyzes flow field pressure distribution, cavity evolution characteristics, and vehicle force features, evaluating the impact of water-entry angle, Froude number (Fr), and cavitator dimension. The results indicate that during water entry, the instantaneous impact forces are mainly concentrated on the wetted surface at the bottom of the vehicle and the plane at the head. The peak stress at the entry point is significantly higher than at other locations, and stress waves propagate along the vehicle body, concentrating at the hollow structure due to the structural characteristics. The change in water-entry angle does not significantly affect the decay of Fr for the vehicle, but increasing the water-entry angle leads to an earlier and larger peak stress at the mid-point monitoring location of the vehicle. In addition, the study also found that the stress level increases with the increase of the Froude number, resulting in larger high-stress areas and peak stresses. However, the cavity evolution at the same water-entry depth is essentially independent of the variation in Fr. With the increase in cavitator dimension, the water-entry load and cavity profile will also significantly increase, and vehicles with larger cavitator dimension will generate larger stress waves upon impact.

This research article explores the application of control co-design methodologies for optimising floating offshore wind turbine systems concurrently. The primary objective is to offer insights into concurrent design approaches employing an advanced genetic optimisation algorithm. To achieve this, a reduced-order dynamic model is employed to minimise computational time requirements, complemented by a modified version of the levelized cost of energy equation serving as the cost function. Furthermore, various optimisation scenarios are investigated under diverse wind and wave conditions to assess the advantages and drawbacks of increasing the complexity of dynamic cases used in evaluating the cost function. The optimised system designs are then compared against baseline floating system designs to underscore the advantages of employing this approach to floating wind turbine design.

In order to improve the utilization ratio of steel plates for parts nesting in ship manufacturing, this paper proposes a new intelligent nesting algorithm for irregular parts based on NFP, which can effectively solve the three difficult problems of selection, locating and collision for irregular parts nesting, and further improve the utilization rate of steel plates. The new method directly uses irregular parts as nested objects, establishes the NFP generating algorithm based on the trajectory and Minkowski method to solve the collision problem, proposes a hybrid strategy of the TOPOS principle and BL principle to solve the locating problem, and builds ISAGA to optimize the sequence and rotation angle of the irregular part to solve the selection problem. Finally, the feasibility and effectiveness of the new method are verified by comparing with the existing nesting softwares of Nest Lib and Sigma Nest based on 10 standard test cases of irregular parts in the ESICUP, the experimental results show that the plate utilization of the proposed new method increased on average by 9.76 % and 13.56 %. And compared with the actual packing results of irregular parts in a shipyard, the plate utilization rate of the new method increased by 5.04 % on average.The new method will play an important role in further improving the utilization rate of steel plate in the shipbuilding industry.

In low-visibility environments, the underwater perception range of optical cameras is severely restricted, and perception operations in ocean engineering often rely on sonar. Acoustic cameras are a type of forward-looking sonar that have attracted considerable attention because of their ability to produce images similar to those of optical cameras. However, owing to the unique imaging mechanism employed by acoustic cameras, the resulting images suffer from insufficient resolution and a loss of feature details. This issue considerably diminishes the precision of downstream visual tasks, limiting the application of acoustic cameras. In this study, we propose a deep-learning-based super-resolution reconstruction approach for acoustic cameras, where the reconstruction process relies only on images, without prior assumptions regarding the detection scenes. We verified the effectiveness of the proposed method for two practical applications: marine debris detection and marine structure inspection. The experimental results show that our proposed method can robustly reconstruct high-resolution sonar images, and the obtained images have superior feature details, which improved the precision of downstream vision tasks. In this study, we aim to provide better solutions for underwater perception in low-visibility marine environments, while exploring the application of acoustic cameras in marine debris detection and structure inspection.

The piers of the sea-crossing bridges have been submerged and eroded for an extended period, presenting a significant safety hazard. Therefore, unmanned monitoring equipment is urgently needed in the waters near the pier. Waves can be harnessed as an abundant renewable energy source. Furthermore, because of the bridge pier's diversion effect, the primary direction of water flow is comparatively stabilized, whereas the pendulum's continuous movement necessitates input excitation in a specific direction. Consequently, this study proposes the development of a self-powered and self-sensing intelligent buoy, employing an inertial pendulum and TENG (Triboelectric nanogenerator) monitoring system to facilitate long-term monitoring of hydrological conditions. The intelligent buoy is divided into two modules based on their respective functionalities: the self-powered module located in the lower section and the self-sensing module positioned in the upper section. The self-powered module comprises three elements, including a wave energy capture part, a motion conversion part, and an electromagnetic energy conversion part. The signal processing part serves as the centerpiece of the self-sensing module. After experimental testing, the maximum power of a single prototype can reach 12.11 mW, and the accuracy of abnormal monitoring can exceed 90%. The Six-DOF (six-degree-of-freedom) shaker experiments and LSTM (Long Short-Term Memory) algorithm-based processing show that the dual-function intelligent buoy is an innovative feasible scheme to realize the IoTs.

Understanding cylinder-induced wake is pivotal in fluid dynamics, providing essential insights for the design and analysis of various structures, including offshore platforms, bridges, and buildings. To achieve fast and accurate modeling, this study introduces a novel reduced-order model (ROM) utilizing dynamic mode decomposition (DMD) and an advanced deep learning framework, specifically an attention-enhanced convolutional neural network-long short-term memory networks model (CNN-LSTM), for predicting cylinder-induced unsteady wake flows. The DMD efficiently simplifies complex fluid systems while retaining key dynamics, thus significantly saving computational costs. By leveraging its combined strengths, the CNN-LSTM with an attention mechanism effectively captures complex spatiotemporal features. The resulting ROM accurately reproduces the wake processes around a cylinder (group), demonstrating high consistency with computational fluid dynamics (CFD) solutions (coefficient of determination > 0.98), and showcases satisfactory resilience to a (Gaussian) noise level of up to 25 %. This study contributes a robust ROM capable of handling spatiotemporal dynamics, facilitating swift prediction of future outcomes using historical data, which is particularly critical for efficient real-time analysis and informed decision-making in dynamic settings, e.g., digital twins and predictive maintenance.

Microbial-induced calcite precipitation (MICP) has recently emerged as a sustainable, eco-friendly, potentially sound ground improvement technique. The durability of MICP-treated samples remains a major concern in this innovative method. This study examines the impact of three types of fiber reinforcements, namely carbon, basalt, and polypropylene, on the durability of biotreated samples of coastal sand. The fiber content used was 0.20%, 0.40%, and 0.60% of soil weight. A comprehensive biotreatment investigation was conducted using Sporosarcina pasteurii in a 0.5 molar cementation solution. The amount of calcite precipitation, scanning electron microscope (SEM) and energy dispersive X-ray spectroscopy (EDS) were used to interpret biocementation. Biotreated samples were subjected to 5, 10, and 15 wetting-drying (WD) cycles under seawater conditions to evaluate the durability of the fiber-reinforced MICP-treated Indian coastal soil. Following WD testing, mass loss, unconfined compressive strength (UCS), split tensile strength (STS), and ultrasonic pulse velocity (UPV) were measured for different fiber-reinforced MICP-treated samples. The study revealed that the WD cyclic process affects the mechanical and physical characteristics of fiber-reinforced MICP-treated samples. The optimal fiber content for carbon, basalt, and polypropylene fibers was 0.40%, 0.40%, and 0.20%, respectively. Notably, the basalt fiber-reinforced sample with a fiber content of 0.40% exhibited minimal effects from the WD cycles, with only a 3.53% mass loss after 15 cycles. Overall, the results strongly support the durability of fiber reinforcement under WD conditions.