Search Results (326)

The global transition to a diversified renewable energy portfolio requires reliable assessment of predictable marine energy resources. This study develops a high-resolution, three-dimensional Regional Ocean Modeling System (ROMS) to quantitatively evaluate theoretical tidal current energy resources in the Bohai and Yellow Seas. The model, configured with fine-scale bathymetry and forced by harmonic tidal constituents, is validated against tide gauge and Acoustic Doppler Current Profiler (ADCP) observations. Multi-year simulations reveal pronounced spatial heterogeneity in tidal current energy distribution. Rather than treating resource assessment as a single power density mapping exercise, this study combines annual mean theoretical power density, peak theoretical power density, threshold-dependent effective flow duration, effective water depth, current directionality, and vertical velocity structure to characterize resource intensity, temporal persistence, and vertical deployability. The results identify distinct hydrodynamic resource regimes. High theoretical resource intensity is concentrated west of Laotieshan Cape and east of Chengshantou, where cumulative annual effective flow duration exceeds 5000 h and short-term instantaneous theoretical power density can reach approximately 10 kW/m² and 8 kW/m², respectively. These peak values indicate strong local tidal acceleration but should be interpreted together with annual mean power density and effective flow duration. In contrast, the northern Jiangsu coastal area exhibits lower peak intensity but relatively persistent moderate flow conditions. The results provide a hydrodynamic resource basis for preliminary site screening and for guiding subsequent turbine-performance, wake/array, environmental, grid accessibility, and techno-economic assessments. Full article

Tidal energy assessment in tropical estuaries is constrained by low current velocities and high spatial variability, which limit conventional evaluation approaches. This study proposes a methodological framework adapted to velocity-constrained environments. The framework integrates ADCP-calibrated hydrodynamic modeling, velocity-exceedance-based site selection, low cut-in tidal turbine compatibility analysis, and a localized Levelized Cost of Energy evaluation within a unified decision-support structure. The methodology is applied to Buenaventura Bay, Colombia, where numerical simulations reproduce the mixed tidal regime with errors of approximately 0.30 m in water levels and 0.022 m/s in current velocities, enabling consistent characterization under low-flow conditions. Results at three locations indicate average available power densities of 64 W/m² at La Bocana, 19 W/m² at Buoy 29, and negligible values at Aguadulce, supporting the identification of marginal and non-viable sites based on velocity distributions. Under a low-velocity turbine configuration (10 m rotor diameter, 0.4 m/s cut-in speed), annual energy production is about 18 MWh per unit, while a 300-turbine array would generate approximately 5.4 GWh per year. The results indicate that annual energy production and capital expenditure are the main drivers of techno-economic feasibility in low-energy estuarine systems. Full article

Tidal turbine wake predictions in regional-scale hydrodynamic models typically account for rotor thrust but neglect the drag of support structures. This study introduces a method for representing turbine support structures as permeable drag volumes within TELEMAC-3D and evaluates their influence on wake characteristics. The method is demonstrated for the 1 MW DeepGen-IV turbine deployed at the Fall of Warness test site at the European Marine Energy Centre, Scotland. The tripod foundation, tower, and nacelle are each implemented as momentum source terms alongside an actuator disc rotor in a regional-scale model with mesh resolution down to 1.5 m with 24 sigma layers and output at 60 s intervals (1 s at instrument locations), validated against seabed-mounted ADCP measurements. Including the support structures improves the agreement with measured wake profiles by 6–18% in root-mean-square error at 3.7 rotor diameters downstream and extends the hub-height 5% velocity deficit distance by an average of three rotor diameters (~54 m), with substantial variability across tidal conditions. The tripod and tower drag also extend the velocity deficit into the lower water column, a feature absent from the rotor-only formulation, with potential relevance to near-bed processes such as bed shear stress and sediment transport which are not examined in the present study. The implementation is in principle extendable to other support concepts and multi-device studies, and the results indicate that support structure drag should be considered in regional wake models where wake persistence and downstream interactions are important. Full article

Accurate power estimation is fundamental to effective tidal turbine design. While turbines are typically designed for specific Tip Speed Ratio (TSR) ranges, the Reynolds number ($Re$) can vary significantly even at a constant TSR depending on flow velocity and turbine scale. Such variations in $Re$ can fundamentally alter the flow characteristics around the blades, directly impacting performance. Conventionally, $Re$-dependent lift and drag coefficients are incorporated into Blade Element Momentum Theory (BEMT) to address these variations, often supplemented by hub and tip loss corrections. However, since BEMT relies on two-dimensional airfoil characteristics, it may not fully capture the complex three-dimensional viscous effects that occur during actual operation. Therefore, this study employs three-dimensional CFD simulations to quantitatively evaluate $Re$ effects on turbine performance. By quantifying power generation deviations across a broad $Re$ spectrum, the results show that discrepancies at identical TSRs range from 0.312% to 7.32%. Notably, these differences stabilise near 1% when $Re$ exceeds $1.0\times {10}^7$. Furthermore, the underlying causes of these scale effects were identified by decomposing the torque into shear and pressure components. These quantified indicators provide a practical basis for incorporating Reynolds number effects into the turbine design process, thereby contributing to more accurate full-scale performance prediction. Full article

The increasing deployment of dense offshore wind turbine monopile foundations pose significant challenges for accurately simulating tidal-flow modification and energy transport at the array scale. Balancing physical realism with computational efficiency remains a key challenge in hydrodynamic modelling of offshore wind farms. In this study, an established drag-based source-term approach is implemented through a dedicated module developed within the TELEMAC-3D framework to represent the momentum-blocking effects of offshore wind-farm arrays. A representative dense 8 × 10 wind turbine monopile array configuration is constructed in a typical tidal channel to systematically examine array-induced tidal-flow responses. The results indicate that the drag-based source-term approach preserves the regional-scale tidal flow structure while effectively capturing array-induced local velocity adjustments and pronounced downstream wake attenuation and recovery. Detailed analyses further reveal distinct spatial and temporal characteristics of the velocity response, including the decay and recovery of velocity deviations downstream of the array. In addition, the monopile array induces a clear modulation of flow kinetic energy, characterized by enhanced energy dissipation and a finite array-scale redistribution of kinetic energy. These findings demonstrate that this approach efficiently simulates the array-scale hydrodynamic and energetic impacts of large offshore wind farms and contribute to a better understanding of array-induced tidal flow modification and energy redistribution. Full article

Blade impact faults degrade power generation quality, if not detected in time, may lead to turbine malfunction or even complete failure. Moreover, the accuracy of blade impact fault detection in tidal current turbine (TCT) is significantly affected by variations in flow velocity and tidal flow period. To solve this problem, a self-adaptive detection method based on stator current signals and k-nearest neighbor-multiplicative score (KNN-MS) is proposed. The method first employs the KNN algorithm to characterize local feature distributions. Then, robustness under unstable flow conditions is improved through variance-based weighting. Finally, a cumulative multiplicative scoring mechanism is proposed to amplify and quantify fault-related anomaly indicators. The experimental results show that the proposed method achieves high diagnostic accuracy and stability across steady, periodic, and variable-period flow scenarios. Full article

To improve the safety and reliability of tidal stream turbines (TSTs) under harsh marine environments, a novel probabilistic approach is proposed for blades fault detection in TSTs subject to stochastic disturbances of unknown probability distribution. On the basis of analytically analyzing the influence of blade imbalance fault on stator current signals, stationary wavelet transform (SWT) is first performed to extract multiscale time–frequency characteristics of blade faults from stator current data corrupted by non-stationary stochastic disturbances. Then an enhanced feature space is established by further computing the energy, standard deviation and kurtosis of SWT decomposition coefficients. By introducing the mean-covariance-based ambiguity set to characterize the probability distribution of feature vector in both fault-free and faulty cases, an optimal separating hyperplane for fault detection is learned using a distributionally robust optimization technique. It can achieve an optimal trade-off between the false alarm rate and the missed detection rate in a probabilistic setting, without requiring any specific distribution assumption. In this way, the proposed fault detection system is robust not only against disturbances but also against distributional uncertainties of disturbances. Finally, an experimental study based on a 0.23 kW tidal stream turbine platform is carried out to validate the effectiveness of the proposed method. Full article

Amid the global transition to carbon neutrality, tidal current energy has become a strategic sustainable energy resource due to its high predictability, power density, and environmental compatibility. Horizontal-axis turbines show great potential for marine energy harvesting, yet the large-scale commercialization of tidal turbines is severely hindered by complex wake dynamics and the lack of reliable, efficient prediction tools for out-of-distribution (OOD) operating conditions. Traditional high-fidelity CFD methods are computationally prohibitive for engineering optimization, while conventional data-driven surrogate models suffer from poor extrapolation performance, extrapolation collapse near training parameter boundaries, and the absence of uncertainty quantification. To address these bottlenecks, this study focuses on the OOD extrapolation of wake flow prediction across tip speed ratio (TSR) distributions for a single horizontal-axis tidal turbine. A CFD-generated spatiotemporal benchmark dataset is constructed for comparative OOD evaluation across various TSR conditions with 9504 total samples. A novel physics-constrained Fourier neural operator framework named TSR-FNO is proposed to improve OOD generalization. The model integrates TSR–Lipschitz regularization to suppress extrapolation collapse and Monte Carlo Dropout to provide reliable uncertainty estimation. Extensive experiments demonstrate that the proposed method effectively reduces prediction error in unseen TSR regimes, mitigates performance degradation in far-field extrapolation, and produces well-calibrated uncertainty estimates consistent with actual prediction confidence. This work provides a data-driven surrogate modeling strategy for fast and reliable wake prediction on a common CFD-generated benchmark, supporting the efficient design, array layout optimization, and engineering deployment of tidal current energy systems. Full article

Fluid mechanics in disordered structures gives rise to rich multiscale dynamics through the interplay of topology, symmetry breaking, and fluid–structure interactions. Heterogeneous networks encode mechanical responses, regulate flow organization, and shape energy dissipation, enabling memory effects and emergent collective behaviors across both natural and engineered systems. These principles operate across vast scales: from seamounts with characteristic scales of $L\approx {10}^3\mathrm{m}$ and Froude numbers $Fr\approx {10}^{-2}–{10}^{-1}$ generating deep-ocean turbulent mixing, to marine tidal turbines operating at Reynolds numbers $Re\approx {10}^7–{10}^8$ and Euler numbers $Eu\approx {10}^{-1}–{10}^0$, where inertial forces dominate flow dynamics. Although the dominant physical forces may vary across scales—for example, planetary rotation and stratification in large-scale oceanic flows versus viscous or interfacial effects in microscale systems—the comparison of dimensionless parameters provides a useful framework for discussing similarities in flow organization and scaling behavior. Empirical observations, network-based descriptions, and multiscale simulations collectively demonstrate how topological features constrain symmetry, organize transport pathways, and support predictive reconstruction and inverse design. These principles underpin applications ranging from engineered systems that exploit broken symmetries to rectify chaotic transport, to biological architectures where flows mediate information transfer, locomotion, and structural self-organization. In this Review, we synthesize recent advances to propose a unifying physical paradigm: fluid flows actively interact with disorder, reorganize dissipation, and convert structural asymmetry into functional mechanical performance across scales. Full article

Tidal energy is increasingly harnessed due to its high energy density, substantial reserves, and reliable predictability. However, marine fouling on turbine blades adds weight and induces asymmetric system loads; prolonged operation exacerbates generator magnetic saturation, causing inductance parameter deviations from controller presets, which further leads to current loop delays, amplified tracking errors and unstable power output. To mitigate these issues, a self-tuning inductance-oriented model-free predictive current control method is proposed. The proposed method utilizes a simplified hyperlocal model alongside an extended state observer to effectively counteract the effects of non-inductive parameters. Simultaneously, the incremental model coupled with a dynamic adjustment method is proposed for real-time adaptive inductance tuning. Simulation results demonstrate that the proposed method significantly enhances system robustness against inductance mismatches and reduces parameter sensitivity, thereby ensuring stable operation. Compared with traditional PI control and model predictive control strategies, the proposed approach exhibits superior performance in disturbance rejection, parameter adaptability, and operational stability. Full article

Marine renewable energy (MRE) systems operate in harsh marine environments where long-term exposure to seawater leads to biofouling, resulting in increased surface roughness, hydrodynamic drag, added mass, structural loading, sensor degradation, and reduced energy production. Despite its significant operational and economic impact, biofouling management in MRE devices has traditionally relied on manual inspections and empirical growth models, which offer limited predictive capability. This review provides a structured, data-centric synthesis of recent advances in machine learning (ML) and bioinformatics approaches for biofouling modeling, performance prediction, and maintenance planning in offshore wind turbines, tidal turbines, and wave energy converters. The study systematically examines key fouling locations and associated engineering impacts, and analyzes the major data streams used for predictive modeling, including SCADA and condition-monitoring time series, metocean variables, inspection imagery, laboratory and field experiments, and environmental DNA (eDNA) sequencing outputs. We compare modeling strategies ranging from physics-based simulations to classical ML, deep learning, computer vision, and hybrid physics-informed frameworks, and discuss how biological indicators such as microbial community profiles and eDNA-derived taxa abundances can be integrated as predictive features. The review further outlines emerging digital twin architectures for fouling-aware performance forecasting and maintenance decision support. Finally, we identify key challenges including data scarcity, cross-site generalization, validation practices, and uncertainty quantification, and propose future research directions toward integrated, proactive biofouling management systems in marine renewable energy infrastructure. Full article

During fatigue tests of tidal turbine blades, digital image correlation (DIC) is used to collect vital information about the specimen. DIC provides high-resolution displacement and strain maps of selected blade sections; however, continuous operation is hindered by the need to acquire, transfer, and process large volumes of high-resolution images, precluding real-time use during long tests. We address this problem by optimising sparse sensing locations on the blade surface so that full-field maps can be accurately reconstructed from a small subset of pixel measurements. In contrast to most DIC improvements found in the literature, which focus on accelerating the processing stage, this approach circumvents the need to collect high-resolution data. We evaluate this approach in a case study at FastBlade, a dedicated testing facility for tidal turbine blades. With less than 1% of the original pixels measured, the mean relative error evaluated on the dataset is 0.4% and 16% for displacement and strain maps, respectively, with the larger strain error reflecting the higher spatial complexity of strain fields. The optimised layouts outperform random and grid-like arrangements. The framework enables real-time monitoring and, subject to relevant validation, might be applied to reconstruct high-resolution strain maps directly from strain-gauge readings, potentially extending to in-ocean blade monitoring. Given the high accuracy of deflection reconstructions, using them to derive strain fields is suggested as a direction for further study. Full article

Tidal turbines have emerged as a promising alternative to fossil-fuel-based energy generation, with estuarine environments identified as potential sites for their deployment. However, estuaries are sensitive ecosystems, and understanding the impacts of turbine installation on local hydrodynamics and sediment transport is critical. While previous studies have shown the influence of turbines on seabed morphology under steady current conditions, the effects of combined wave–current loading remain insufficiently explored. In this study, we present a novel numerical modeling framework to predict seabed evolution in the vicinity of tidal turbines subjected to wave–current interactions. The approach integrates Blade Element Theory (BET) to represent turbine-induced forces, an Euler–Euler multiphase model for sediment transport, and the first-order wave theory to capture wave dynamics, all implemented within the OpenFOAM-based solver. Wave effects are incorporated as source terms in the momentum equations, and wave velocities are added to the current field at the velocity inlet boundary condition. Results demonstrate that wave–current loading induces oscillatory sediment transport, but net scouring remains significant in the vicinity of the turbine. The proposed framework is validated component-wise (wave forcing and rotor loading) and then demonstrated on mobile-bed simulations to quantify how oscillatory wave–current forcing modifies near-bed transport and early-stage scour development around a tidal turbine. While the present simulations focus on short morphodynamic times, the approach provides a physics-based basis for exploring wave effects on turbine-induced sediment dynamics. Full article

Although interest in tidal energy has increased in recent years, its development remains significantly behind that of other renewable sources such as solar and wind energy. This delay is primarily caused by the complex and harsh ocean environment, which imposes significant constraints on operational systems. This paper proposes a new approach to the design and control of a marine current turbine (MCT) emulator without a pitch mechanism, operating in real time below the rated marine current speed.The emulator control strategy integrates two approaches: predictive control for regulating the speed of the DC machine, and a Linear Quadratic Regulator (LQR) control scheme for maximizing power extraction from the marine current. Our experimental results demonstrate the effectiveness of the proposed hybrid control strategy, which allows precise tracking of reference signals and stable regulation of the direct current machine (DCM) speed, thereby ensuring synchronization with the turbine’s rotational speed. This approach ensures optimal and robust performance over the entire range of marine current variations. Full article

The horizontal-axis tidal turbine is a representative device for harnessing ocean tidal energy, and the structural optimization of its blades is crucial for enhancing the power capture efficiency. In this work, the twist and chord distributions of the blade are determined using an improved Blade Element Momentum (BEM) approach, in which tip and hub loss factors are employed to enhance the modeling accuracy, and these results are employed to construct a parametric model of the original rotor. Due to its simplified assumptions and inability to capture three-dimensional flow effects, computational fluid dynamics (CFD) simulations were carried out to evaluate the hydrodynamic performance and flow analysis of the designed rotor. Further, the orthogonal test method was used to optimize the hydraulic performance of the rotor. Three optimization parameters, namely hub diameter, airfoil type, and maximum airfoil thickness, were set with three levels. Based on the orthogonal design scheme, nine rotor configurations were generated, and their energy capture characteristics and flow fields were subsequently evaluated through numerical simulations. The analysis indicates that the choice of airfoil exerts the strongest impact on the rotor’s energy capture efficiency, while the influences of maximum airfoil thickness and hub diameter follow in descending order. Consequently, the optimized rotor adopts a NACA63-415 airfoil with a reduced maximum thickness of 0.9 T₀ and an intermediate hub diameter of 15%R, achieving a power coefficient of 0.445 at the design tip-speed ratio of 4, corresponding to a 3.08% improvement compared with the original design. Flow field analysis demonstrates that the optimized geometry promotes a more uniform spanwise pressure distribution and effectively suppresses flow separation, thereby enhancing the overall hydrodynamic efficiency. Full article