Search Results (98)

The slow propagation speed of acoustic waves in water leads to significant variations and random fluctuations in communication delays among underwater acoustic sensor network (UASN) nodes. Conventional deep reinforcement learning (DRL)-based underwater acoustic network access methods can adaptively adjust their parameters and improve network communication efficiency by effectively utilizing inter-node delay differences for concurrent communication. However, they still suffer from shortcomings such as not accounting for random delay fluctuations in underwater acoustic links and low learning efficiency. This paper proposes a DRL-based delay-fluctuation-resistant underwater acoustic network access method. First, delay fluctuations are integrated into the state model of deep reinforcement learning, enabling the model to adapt to delay fluctuations during learning. Then, a double deep Q-network (DDQN) is introduced, and its structure is optimized to enhance learning and decision-making in complex environments. Simulations demonstrate that the proposed method achieves an average improvement of 29.3% and 15.5% in convergence speed compared to the other two DRL-based methods under varying delay fluctuations. Furthermore, the proposed method significantly enhances the normalized throughput compared to conventional Time Division Multiple Access (TDMA) and DOTS protocols. Full article

Offshore wind turbines (OWTs) are being developed with larger capacities for deeper waters, facing complex environmental loads that challenge structural safety. In contrast to onshore turbines, OWT foundations must withstand combined hydrodynamic forces (waves and currents), leading to substantially higher construction costs. For floating offshore wind turbines (FOWTs), additional considerations include radiation hydrodynamic loads and additional hydrodynamic damping effects caused by platform motion. Dynamic analysis of these foundations remains a critical bottleneck, presenting new challenges for offshore wind power advancement. This article first introduces the main structural types of OWT foundations, with case studies predominantly from China. The remaining part of the article proceeds as follows: dynamics of fixed OWT foundations, dynamics of FOWT foundations, and conclusions. Next, it covers several important topics related to fixed offshore wind turbines, including pile–soil interaction, wave loads, and seismic analysis. It then discusses support platform motion analysis, hydroelastic analysis, and mooring system characteristics of floating offshore wind turbines. Finally, it presents some insights to improve design and optimization methods for enhancing the safety and reliability of offshore wind turbines. This research clarifies OWT foundation dynamics, helping researchers address challenges and optimize designs. Full article

This paper presents a novel submersible seaweed cultivation infrastructure designed to enhance seaweed growth through deep cycling. The system consists of a square grid of ropes for growing seaweed, supported by buoys, mooring lines, and innovative SubTractors—movable buoys that enable controlled submersion. The grid ropes are stabilized by four SubTractors, an array of small buoys, intermediate sinker weights and mooring lines anchored to the seabed. The SubTractors facilitate dynamic positioning, allowing the seaweed rope grid to be submerged below the thermocline—at depths of 100 m or more—where nutrient-rich deep water accelerates seaweed growth in offshore sites with low surface nutrient levels. Small buoys attached to the grid provide buoyancy, keeping the seaweed rope grid planar and near the surface to optimize photosynthesis when not submerged. This paper first describes the seaweed cultivation infrastructure, then develops a hydroelastic model of the proposed cultivation system, followed by a hydroelastic analysis under varying wave and current conditions. The results provide insights into the system’s dynamic behaviour, informing engineering design and structural optimization. Full article

This study focuses on the sandstone of the Kizil Grottoes as the research object. Sandstone samples reinforced with barium hydroxide nanoparticle (Ba(OH)₂) solutions at different concentrations were subjected to mass and deformation monitoring, wave velocity tests, triaxial shear tests, and conventional mercury intrusion porosimetry (MIP) to investigate the reinforcement mechanism and effectiveness of nano-Ba(OH)₂ on Kizil sandstone. The results indicate that after treatment with nano-Ba(OH)₂, the strength and wave velocity of the sandstone samples significantly increased, with the 15% concentration showing the optimal reinforcement effect. Nano-Ba(OH)₂ enhances the cementation between sandstone particles, alters pore morphology and size distribution, reduces capillary water rise height, and inhibits sulfate ion crystallization and recrystallization, thereby achieving the dual effects of strength reinforcement and deterioration prevention. Full article

Jack-up offshore platforms, supported by truss legs, are integral to the development of marine energy resources, including oil, gas, and offshore wind. Due to the structural complexity of truss legs, accurately quantifying wave loads is crucial for ensuring the safety and efficiency of energy extraction operations. In this work, a numerical wave tank approach combined with theoretical analysis is employed comprehensively to investigate wave loads on truss legs, with a particular emphasis on the effects of component forces and inflow angle. The results demonstrate that wave loads are not solely dependent on member dimensions. The influencing factors affecting component forces include water depth and phase differences between structural units, which amplify the contribution of the component forces of members near the free surface and without phase difference to the total force. Furthermore, the total force varies periodically with the inflow angle in cycles of 60°. Notably, the influence of inflow angle on the total force becomes negligible when the wavelength substantially exceeds the pile spacing. This framework fundamentally provides a theoretical basis for the structural optimization of Jack-up offshore platform support systems, thereby enhancing the safety and reliability of energy infrastructure. Full article

To enhance the spectral efficiency of hybrid beamforming in millimeter-wave massive MIMO systems, the problem is formulated as a high-dimensional non-convex optimization under constant modulus constraints. A novel algorithm based on fourth-order tensor Tucker decomposition is proposed. Specifically, the frequency-domain channel matrices are structured into a fourth-order tensor to explicitly capture the couplings across the spatial, frequency, and user domains. To tackle the non-convexity induced by constant modulus constraints, the analog precoder and combiner are derived by solving a truncated-rank Tucker decomposition problem through the Alternating Direction Method of Multipliers and Alternating Least Squares schemes. Subsequently, in the digital domain, the Regularized Block Diagonalization algorithm is integrated with the subcarrier and user factor matrices—obtained from the tensor decomposition—along with the water-filling strategy to design the digital precoder and combiner, thereby achieving a balance between multi-user interference suppression and noise enhancement. The proposed tensor-based algorithm is demonstrated through simulations to outperform existing state-of-the-art schemes. This work provides an efficient and mathematically sound solution for hybrid beamforming in dense multi-user scenarios envisioned for sixth-generation mobile communications. Full article

Due to their unique structural configuration, Very Large Floating Structures (VLFS) exhibit significant hydroelastic responses during their motion in the water. These responses, which are a result of the interaction between the structure and the waves, can lead to undesirable vibrations and deformations, potentially compromising the stability and performance of the VLFS. Reducing the hydroelastic response in VLFS has become a critical research focus for scholars worldwide. In the field of marine engineering, various methods are employed to address this issue, with the use of porous structures being one of the most effective solutions. These porous structures help to dissipate the energy of propagating waves, thereby reducing the magnitude of hydroelastic responses. This paper introduces a multi-layer porous plate structure designed to mitigate the hydroelastic response of horizontally moored VLFS. The proposed structure consists of multiple layers of porous plates strategically arranged to optimize the dissipation of wave energy. To evaluate the performance of this structure, a series of physical model tests were conducted, focusing on the hydrodynamic behavior of the VLFS with the multi-layer porous plate structure. The experimental results indicate that within a specific wavelength range, the properly configured multi-layer porous plate structure can significantly reduce the hydroelastic response of the VLFS. This reduction is especially noticeable in the attenuation of wave-induced forces, leading to a decrease in the structural vibrations and enhancing the stability of the floating system. The findings demonstrate that this innovative design can provide a reliable method for improving the performance of VLFS in challenging marine environments. Full article

A perforated aquaculture vessel represents an environmentally sustainable approach to fish farming, leveraging seawater circulation to optimize water quality and enhance fish health and growth. The perforations on the side of the fish tank significantly influence its hydrodynamic characteristics. This study investigated the influence of pore parameters on the perforated fishing tank with various pore designs, such as the asymmetric distribution of the opening in depth, windward, and leeward directions. A numerical study was conducted using STAR-CCM+ to analyze the perforated tank under beam wave conditions. This study aimed to analyze the effects of pore location, opening ratio, and asymmetric distribution on the hydrodynamic performance and flow characteristics within aquaculture tanks. The results demonstrated that an asymmetric pore distribution on the windward and leeward sides of the vessel had a notable impact on the roll motion and the flow velocity in the vicinity of the pores. The findings also indicated that the effects of pore distribution were more significant than those of opening ratio, especially regarding asymmetry. The results revealed that higher flow velocities occurred under a smaller opening ratio. Modifying pore structure parameters on the windward and leeward sides can alter the local flow field. Full article

Electrochemical sensors have been developed based on polyethylene terephthalate track-etched membranes (PET TeMs) modified by photograft copolymerization of N-vinylformamide (N-VFA) and trimethylolpropane trimethacrylate (TMPTMA). The modification, structure and properties of the modified PET TeMs were thoroughly characterized using scanning electron microscopy (SEM) and atomic force microscopy (AFM), thermogravimetric analysis (TGA), Fourier-transform infrared (FTIR) spectroscopy, gas permeability measurements and contact angle analysis. Optimal membrane modification was achieved using C = 10% (N-VFA), 60 min of UV irradiation and a UV lamp distance of 10 cm. Furthermore, the modified membranes were implemented in a two-electrode configuration for the determination of Eu³⁺, Gd³⁺, La³⁺ and Ce³⁺ ions via square-wave anodic stripping voltammetry (SW-ASV). The sensors exhibited a linear detection range from 10⁻⁷ M to 10⁻³ M, with limits of detection of 1.0 × 10⁻⁶ M (Eu³⁺), 6.0 × 10⁻⁶ M (Gd³⁺), 2.0 × 10⁻⁴ M (La³⁺) and 2.5 × 10⁻⁵ M (Ce³⁺). The results demonstrated a significant enhancement in electrochemical response due to the grafted PET TeMs-g-N-PVFA-TMPTMA structure, and the sensor showed practical applicability and consistent performance in detecting rare earth ions in tap water. Full article

Extreme surface winds and wave heights of tropical cyclones (TCs)—pose serious threats to coastal community, infrastructure and environments. In recent decades, progress in numerical wave modeling has significantly enhanced the ability to reconstruct and predict wave behavior. This review offers an in-depth overview of TC-related wave modeling utilizing different computational schemes, with a special attention to WAVEWATCH III (WW3) and Simulating Waves Nearshore (SWAN). Due to the complex air–sea interactions during TCs, it is challenging to obtain accurate wind input data and optimize the parameterizations. Substantial spatial and temporal variations in water levels and current patterns occurs when coastal circulation is modulated by varying underwater topography. To explore their influence on waves, this study employs a coupled SWAN and Finite-Volume Community Ocean Model (FVCOM) modeling approach. Additionally, the interplay between wave and sea surface temperature (SST) is investigated by incorporating four key wave-induced forcing through breaking and non-breaking waves, radiation stress, and Stokes drift from WW3 into the Stony Brook Parallel Ocean Model (sbPOM). 20 TC events were analyzed to evaluate the performance of the selected parameterizations of external forcings in WW3 and SWAN. Among different nonlinear wave interaction schemes, Generalized Multiple Discrete Interaction Approximation (GMD) Discrete Interaction Approximation (DIA) and the computationally expensive Wave-Ray Tracing (WRT) A refined drag coefficient (C_d) equation, applied within an upgraded ST6 configuration, reduce significant wave height (SWH) prediction errors and the root mean square error (RMSE) for both SWAN and WW3 wave models. Surface currents and sea level variations notably altered the wave energy and wave height distributions, especially in the area with strong TC-induced oceanic current. Finally, coupling four wave-induced forcings into sbPOM enhanced SST simulation by refining heat flux estimates and promoting vertical mixing. Validation against Argo data showed that the updated sbPOM model achieved an RMSE as low as 1.39 m, with correlation coefficients nearing 0.9881. Full article

Ship seismic wave signals represent one of the most critical physical field characteristics of vessels. To achieve the high-precision detection of ship seismic wave field signals in marine environments, an underwater seismic wave signal detection system was designed. The system adopts a three-stage architecture consisting of watertight instrument housing, a communication circuit, and a buoy to realize high-capacity real-time data transmissions. The host computer performs the collaborative optimization of multi-modal hardware architecture and adaptive signal processing algorithms, enabling the detection of ship targets in oceanic environments. Through verification in a water tank and sea trials, the system successfully measured seismic wave signals. An improved ALE-LOFAR (Adaptive Line Enhancer–Low-Frequency Analysis) joint framework, combined with DEMON (Demodulation of Envelope Modulation) demodulation technology, was proposed to conduct the spectral feature analysis of ship seismic wave signals, yielding the low-frequency signal characteristics of vessels. This scheme provides an important method for the covert monitoring of shallow-sea targets, providing early warnings of illegal fishing and ensuring underwater security. Full article

This study presents a novel semi-submersible platform design for 10 MW vertical-axis wind turbines (VAWTs), specifically engineered to address the compounded challenges of China’s intermediate-depth (40 m), typhoon-prone maritime environment. Unlike conventional horizontal-axis configurations, VAWTs impose unique demands due to omnidirectional wind reception, high aerodynamic load fluctuations, and substantial self-weight—factors exacerbated by short installation windows and complex hydrodynamic interactions. Through systematic scheme demonstration, we establish the optimal four-column configuration, resolving critical limitations of existing concepts in terms of water depth adaptability, stability, and fabrication economics. The integrated design features central turbine mounting, hexagonal pontoons for enhanced damping, and optimized ballast distribution, achieving a 3400-tonne steel mass (29% reduction vs. benchmarks). Comprehensive performance validation confirms exceptional survivability under 50-year typhoon conditions (Hs = 4.42 m, Uw = 54 m/s), limiting platform tilt to 8.02° (53% of allowable) and nacelle accelerations to 0.10 g (17% of structural limit). Hydrodynamic analysis reveals heave/pitch natural periods > 20 s, avoiding wave resonance (Tp = 7.64 s), while comparative assessment demonstrates 33% lower pitch RAOs than leading horizontal-axis platforms. The design achieves unprecedented synergy of typhoon resilience, motion performance, and cost-efficiency—validated by 29% steel savings—providing a technically and economically viable solution for megawatt-scale VAWT deployment in challenging seas. Full article

Accurate crop yield prediction is vital towards optimizing agricultural productivity. Machine Learning (ML) has shown promise in this field; however, its application to legume crops, especially to lupin, remains limited, while many models lack interpretability, hindering real-world adoption. To bridge this literature gap, an interpretable ML framework was developed for predicting lupin yield using Sentinel-2 remote sensing data integrated with georeferenced yield measurements. Data preprocessing involved computing vegetation indices, removing outliers, addressing multicollinearity, normalizing feature scales, and applying data augmentation techniques to correct target imbalance. Subsequently, six ML models were evaluated representing different algorithmic strategies. Among them, XGBoost showed the best performance ($R^2$ = 0.8756) and low error values across $MAE$, $MSE$, and $RMSE$ metrics. To enhance model transparency, SHapley Additive exPlanations (SHAP) values were applied to interpret the feature contributions of the XGBoost model. The Enhanced Vegetation Index ($EVI$) and Normalized Difference Vegetation Index ($NDVI$) were found to be key predictors of crop yield, both showing a positive correlation with higher values reflecting greater vegetation vigor and corresponding to increased yield. These were followed by $B03$ (green) and $B12$ (short-wave infrared), which captured key reflectance properties associated with chlorophyll activity and water content, respectively. Both of them substantially influence photosynthetic efficiency and plant health, ultimately affecting yield potential. Full article

As a renewable and clean energy source with abundant reserves, the development of wave energy relies on accurate predictions of significant wave height (Hs). The fluctuation of Hs is a non-stationary process influenced by seasonal variations in marine climate conditions, which poses significant challenges for accurate predictions. This study proposes a deep learning method based on buoy datasets collected from four research locations in China’s offshore waters over three years (2021–2023, 3-hourly). The hybrid modal decomposition CEEMDAN-VMD is employed for reducing non-stationarity of the Hs sequence, with peak information incorporated as a data augmentation strategy to enhance the performance of deep learning. A probabilistic deep learning model, QRCNN-BiLSTM, was developed using quantile regression, achieving 12-, 24-, and 36-h interval predictions of Hs based on 12 days of historical data with three input features (Hs and wave velocities only). Furthermore, an optimization algorithm that integrates the proposed innovative enhancement strategies is used to automatically adjust the network parameters, making the model more lightweight. Results demonstrate that under a 0.95 prediction interval nominal confidence (PINC), the prediction interval coverage probability (PICP) reaches 100% for at least 6 days across all datasets, indicating that the developed system exhibits superior performance in short-term wave forecasting. Full article

Climate changes have increased heavy rainfall, intensifying flood damage, especially along small streams with steep slopes, fast flows, and narrow widths. In Korea, nearly half of flood-related casualties occur in these regions, underscoring the need for effective flood early warning systems. However, predicting flood depths is challenging due to the complex channels and rapid flood wave propagation in small streams. This study developed a flood early warning framework (FEWF) tailored for small streams in Korea, optimizing rainfall–discharge nomographs using hydro-informatic data from four streams. The FEWF integrates a four-parameter logistic model with real-time updates with a nomograph using a robust constrained nonlinear optimization algorithm. A simplified two-level early warning system (attention and severe) is based on field-verified thresholds. Discharge predictions estimate the water depth in unmeasured cross-sections using the Manning formula, with real-time data updates allowing for the dynamic identification of the flood depth. The framework was validated during the 2022 flood event, where no inundation or bank failures were observed. By improving flood prediction and adaptive management, this framework can significantly enhance disaster response and reduce casualties in vulnerable small stream areas. Full article