Search Results (96)

This study presents a computational analysis of degradation caused by cavitation and hydro-abrasive erosion in a low-head axial microturbine ($H=4\mathrm{m}$), incorporating strategic air injection as a passive mitigation technique. Using Computational Fluid Dynamics (CFD) within ANSYS CFX 2025 R2, the study investigates hydrodynamic performance and the spatial distribution of surface wear across the runner blades. The turbine geometry was developed from aerofoil profiles mapped onto cylindrical coordinates, using a structured three-dimensional mesh with localized refinement to ensure grid independence. Physical modeling employed the Shear Stress Transport (SST) turbulence model, with cavitation dynamics governed by the Rayleigh–Plesset equation and sediment transport modeled using a Lagrangian framework incorporating the Finnie erosion model. The numerical framework showed good agreement with reference characteristic curves, confirming its predictive accuracy. The results indicate that vapor cavities form predominantly on the suction side, whereas solid particle erosion highly concentrated on the pressure side of the blades, where the outer $20\%$ of the span accounts for over $91\%$ of the total erosion intensity. Parametric assessments of controlled air injection revealed a highly non-linear mitigation response, identifying IAVF 2 as the optimal air-injection case. This configuration reduced integrated erosion by $0.95\%$ and maximum localized erosion by $6.17\%$. In contrast, excessive air volumes accelerated material removal due to localized flow distortion. The findings indicate that carefully controlled air injection is a viable strategy for extending the operational lifespan of small-scale hydropower assets. Full article

This study evaluates the resource, technical, economic, and CO₂ mitigation potentials of terrestrial hydropower, wind power, and photovoltaic (PV) power in China under historical and future SSP(Shared Socioeconomic Pathways) climate scenarios. By integrating hydro-meteorological observations, land-use information, digital elevation data, nature-reserve constraints, and CMIP6 climate outputs, we estimate renewable-energy potentials through a consistent national-scale screening framework and cost–supply curve analysis. The results show clear spatial heterogeneity among the three energy sources. Hydropower potential is concentrated mainly in the Yangtze River basin, Pearl River basin, and Southwestern International Rivers. Wind-power potential is relatively high in northwestern, northeastern, and plateau regions, while PV potential is particularly large in northwestern, northern, northeastern, and selected southeastern regions. Under the adopted assumptions, PV shows the largest resource and technical potential, followed by wind power and hydropower; however, this ranking reflects resource potential rather than comprehensive deployment superiority. Practical development is also constrained by ecological flow requirements, land-use competition, grid integration, storage demand, transmission capacity, curtailment risk, and regional demand matching. The findings provide a national-scale comparative reference for renewable-energy planning and CO₂ mitigation, while highlighting the need for future work that incorporates dynamic land use, system-level integration costs, detailed turbine or power-curve modeling, and dynamic grid-emission factors. Full article

With the large-scale development of renewable energy such as wind, solar and ocean energy, the demand for energy storage is more urgent. Pumped hydro energy storage (PHES) is one of the fundamental solutions to the problem of intermittent supply of renewable energy. The large-capacity/low-head pumped hydro energy storage (LL-PHES) system with the use of tubular pump turbine is a beneficial extension of traditional PHES systems owing to large flow rate and cheaper civil structures. However, the continuous competition between the “static water pressure difference caused by gravity” and the “pressure increase caused by accelerated impeller rotation” leads to prominent instability in the start-up process of the LL-PHES system under pump conditions. An explicit coupling algorithm is proposed for analyzing the transient characteristics in the start-up process of the LL-PHES system under pump conditions. This algorithm is based on the idea of dimensional transformation, and performs 3D flow calculations and 2D rigid body dynamics equation solution in the pump domain and the flap gate domain, respectively. This algorithm avoids the problems of high computational cost and poor convergence that exist in existing fully three-dimensional coupling algorithms and ensures the efficiency of transient hydraulic characteristic calculation. A comprehensive analysis of the transient characteristics of the LL-PHES system during pump start-up process is conducted using the proposed new algorithm. The entire process of the increase in rotational speed, valve opening, flow rate, and the continuous evolution of blade surface pressure during the start-up process is quantitatively described. The amplitude and spectral characteristics of the alternating pressure on multiple blades are clarified. The evolution law of blade load during the stage of severe pressure fluctuations during the start-up process is explained. The load distribution characteristics of “high in the leading and trailing edge areas and low in the middle” in the blade stream direction is presented. The research results have a direct guiding role in improving the hydraulic design and enhancing the operational stability of LL-PHES systems. Full article

Reversible pump turbines (RPTs) play a key role in pumped hydro energy storage systems, where increasing grid flexibility requires frequent operation under off-design conditions. In turbine mode, deep partial load and no-load operation are often associated with severe flow instabilities, rotating stall, and strong rotor–stator interactions, which can limit operational flexibility and increase mechanical stress. Previous studies have shown that blade lean can influence hydrodynamic stability; however, its effect under no-load conditions remains insufficiently understood. In this work, the influence of runner blade lean on flow instabilities and rotor–stator interaction in a reversible pump turbine is numerically investigated. Two runner configurations, featuring a 0° and a $-15°$ blade lean angle, are analyzed through unsteady CFD simulations during the transition from deep partial load to no-load operation. The analysis focuses on flow field characteristics, blade loading, and the spectral content of pressure, torque, and radial forces. The results show that the negatively leaned runner significantly mitigates flow recirculation near the hub, reduces pressure and torque fluctuations, and strongly suppresses higher-order harmonic components associated with rotor–stator interaction. In particular, radial force amplitudes at blade-passing harmonics are substantially reduced under no-load conditions. These findings demonstrate that a negative blade lean improves hydrodynamic stability and reduces vibratory loads, contributing to the enhanced operational reliability of reversible pump turbines. Full article

Vertical axis wind turbines (VAWTs) have regained attention for distributed, urban, and floating offshore applications, yet the literature remains fragmented across competing rotor concepts and modelling traditions. This review consolidates the principal archetypes—Savonius, H-Darrieus, troposkein Darrieus, helical Darrieus, and Savonius–Darrieus hybrids—through five governing parameters: drag-versus-lift-driven operating principle, tip speed ratio $\mathsf{\lambda }=\mathsf{\omega }\mathrm{R}/{\mathrm{V}}_{\infty }$ (0.6–1.2 for Savonius; 2.5–5.0 for Darrieus), solidity $\mathsf{\sigma }=\mathrm{N}\mathrm{c}/\mathrm{R}$ (0.1–0.4), chord-based Reynolds number ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ (${10}^5–{10}^6$), and peak power coefficient ${\mathrm{C}}_{\mathrm{p}\_\mathrm{m}\mathrm{a}\mathrm{x}}$ (0.15–0.25 for Savonius; 0.35–0.45 for optimized H-Darrieus). Off-design performance is dominated by unsteady mechanisms that quasi-steady streamtube models cannot resolve—leading edge vortex shedding, dynamic stall hysteresis, blade–wake interaction, and flow-curvature-induced virtual camber—each examined for its contribution to the instantaneous torque ${\mathrm{C}}_{\mathrm{T}}\left(\mathsf{\theta }\right)$ and the cycle-averaged ${\mathrm{C}}_{\mathrm{p}}$. Turbulence closures are benchmarked against phase-locked PIV and torque measurements: $\mathrm{k}–\mathsf{\omega }\mathrm{S}\mathrm{S}\mathrm{T}$ URANS captures peak-region ${\mathrm{C}}_{\mathrm{p}}$ to within $\pm 5–10\%$ but over-predicts torque below ${\mathsf{\lambda }}_{\mathrm{o}\mathrm{p}\mathrm{t}}$; the $\mathsf{\gamma }–{\mathrm{R}}_{\mathrm{e}\_\mathsf{\theta }}$ transition SST model reduces this error to $\pm 3–5\%$; DES, DDES, and LES reach $\pm 2–3\%$ at one to two orders of magnitude higher cost. Best practice computational fluid dynamics (CFD) guidelines are consolidated: domain extents of 15D upstream, 10D downstream, and 20D lateral; rotating sub-domain ${\mathrm{D}}_{\mathrm{r}\mathrm{o}\mathrm{t}}$ $\approx 1.5\mathrm{D}$; ${\mathrm{y}}^{+}\le 1$; $\Delta \mathsf{\theta }\le 0.1°$; and 20–30 revolutions before sampling. Performance enhancement strategies (variable pitch, guide vanes, helical twist, and hybridization) are reviewed quantitatively, with reported ${\mathrm{C}}_{\mathrm{p}}$ gains of $5–30\%$. Four research priorities are identified: (i) transition-sensitive turbulence closures validated below ${\mathrm{R}}_{\mathrm{e}\_\mathrm{c}}$ = $5\times {10}^5$; (ii) coupled aero-hydro-servo-elastic models for floating offshore VAWTs; (iii) machine-learning-augmented turbulence modelling—including physics-informed neural networks (PINNs) and neural-network-corrected RANS closures—to improve unsteady flow prediction at sub-LES cost; and (iv) integrated aeroacoustic–aeroelastic frameworks for urban and building-integrated deployment. Full article

The growing energy demand in rural areas such as the ejido Boca del Cerro, located in Tenosique, Tabasco (Mexico), near the Usumacinta River, calls for sustainable energy solutions such as microgrids. This study proposes an energy management system combining renewable energy forecasting and fuzzy control for a simulated small autonomous rural microgrid scenario designed to supply a fixed priority load of 5 kW and a variable flexible load ranging from 1 to 10 kW. Three LSTM architectures (vanilla, stacked, and bidirectional) are compared for predicting solar irradiance, wind speed, and river flow. The vanilla model is optimized using Hyperband to improve prediction accuracy, particularly for flow rate, which is rarely addressed in similar studies. Forecasts feed into models of photovoltaic, wind, and hydro systems within the microgrid. Energy dispatch is managed through fuzzy logic control. The fuzzy controller supports load prioritization, battery charge/discharge management, and surplus energy redirection to an absorbing load. The final vanilla LSTM achieved RMSE values of 25.741, 0.302, and 12.644 for solar irradiance, wind speed, and river flow, respectively, with NSE values above 0.949 in all cases. These results indicate high forecasting accuracy for solar irradiance and river flow, with limited improvement for wind speed. Overall, the proposed EMS enables effective energy flow management, while the integration of hydrokinetic turbines with AI-based forecasting represents a novel contribution. Full article

Hybrid hydro–solar microgrids offer a practical electrification option for remote and weak-grid communities by combining run-of-river hydropower with photovoltaic generation. However, their performance depends strongly on coordinated decisions across three layers: (i) system sizing and architecture, (ii) turbine selection and rating under variable river flow, and (iii) operational energy dispatch under time-varying solar resource and demand. This paper develops an optimization-driven planning framework for a run-of-river hydro–PV microgrid that co-optimizes component capacities and turbine-related design choices while enforcing time-series operational feasibility. Physics-based component models translate river discharge into hydroelectric output via turbine efficiency characteristics and operating limits, and compute PV generation and storage trajectories under dispatch and state-of-charge constraints. The planning problem is formulated as a multi-objective optimization that quantifies trade-offs among life-cycle cost, supply reliability (e.g., unmet-load metrics), and sustainability indicators (e.g., diesel-free operation or emissions when backup generation is present). A Pareto-optimal set of designs is obtained using a population-based multi-objective algorithm, and representative knee-point (balanced) solutions are selected to illustrate how turbine choice and dispatch strategy interact with seasonal hydrology and solar variability. The proposed approach supports transparent and robust design decisions for hybrid hydro–solar microgrids. Full article

Multi-purpose platforms, which combine renewable energy generation devices and diverse functionalities, are a smart way to expand the applications of offshore platforms. An environmentally friendly multi-purpose offshore platform is proposed by the ‘Blue Growth Farm’ project, which includes a wind turbine, a set of wave energy converters, and an aquaculture system. To assess its feasibility and performance, a field experiment is conducted at an offshore site in Italy using a 1:15 scaled outdoor platform prototype. To provide comprehensive insights into the platform’s behavior, in the present work, aero–hydro–servo–elastic coupled numerical models based on the blade element method and potential flow theory are developed for various experimentally tested configurations of this multi-purpose platform. Time domain analyses are conducted to investigate the performance of the outdoor prototype platform under the recorded realistic environmental loads from the field experiment. The numerical results, including platform motion, mooring line tension forces, and wind turbine responses, agree with the corresponding experimental records. For example, the absolute mean value errors for platform roll and pitch motions are approximately 1 degree, validating the developed numerical model. Meanwhile, the present comparative study demonstrates the feasibility of the proposed multi-purpose concept and can provide a reference for similar projects in the future. Full article

Floating offshore wind turbines (FOWTs) are essential for meeting global renewable energy goals, yet their viability depends strongly on platform motion in harsh marine environments and the resulting influence on structural loading and the levelized cost of energy. This study examines the dynamic response of a 15 MW semi-submersible FOWT based on the IEA-15-240-RWT developed by NREL. The baseline UMaine VolturnUS-S platform is evaluated alongside two newly proposed variants, KSNU-1 15 MW and KSNU-2 15 MW, each equipped with distinct heave-plate configurations designed to enhance hydrodynamic damping while maintaining equal surface area for fair comparison. Hydrodynamic coefficients are obtained through potential-flow analysis using Ansys Aqwa, and fully coupled aero-hydro-servo-elastic simulations are conducted with OpenFAST. The performance of all platforms is assessed under two design load cases (DLCs): the fatigue limit state (FLS) and the ultimate limit state (ULS). The results show that both KSNU platforms achieve slight reductions in surge, sway, and heave motions, with KSNU-2 providing the most consistent improvement in vertical and horizontal stability. Rotational responses increase modestly but remain within acceptable limits. Overall, the KSNU-2 design demonstrates improved motion control without compromising energy output, offering a promising configuration for large-scale floating wind applications. Full article

When operating in the marine environment, floating offshore wind turbines (FOWTs) are subjected to various inflow conditions such as wind, waves, and currents. To investigate the effects of complex inflow conditions on offshore wind farms, an integrated fluid-structure interaction computational and coupled dynamic analysis method for FOWTs is employed. An aero-hydro-servo-elastic coupled analysis model of the NREL 5 MW semi-submersible wind turbine array based on the OC4-DeepCwind platform is established. The study examines the variations in power generation, platform motion, structural loads, and flow field distribution of the FOWT array under different wave incident angles and yaw angles of the first column turbines. The results indicate that the changes in power generation, platform motion, and flow field distribution of the wind farm are significantly influenced by the yaw angle. The maximum tower top yaw bearing torque and the tower base Y-direction bending moment of the wind turbines undergo significant changes with the increase in the angle between wind and wave directions. The study reveals the mechanism of power generation and load variation during yaw control of the FOWT array under misaligned wind and wave conditions, providing a theoretical basis for the future development of offshore floating wind farms. Full article

Predicting lift and drag in hydro turbine design is important to optimize its performance. However, it poses significant challenges due to the complexity of fluid dynamics, which is traditionally addressed by Reynolds-Averaged Navier–Stokes equations, which is time-consuming. Moreover, these methods are computationally demanding, making them a costly approach and less efficient for complex turbine designs. Recent advancements in machine learning (ML) offer a promising alternative with reduced computational costs while maintaining accuracy. This paper explores the use of a data-driven ML model for predicting aerodynamic performance, specifically lift and drag, in hydro turbine design. The models were developed from experimental hydro turbine data gathered from various blade designs and flow conditions. CatBoost yielded the highest predictive accuracy among all the models tested. The findings indicate that CatBoost achieved the best predicted accuracy, followed by LGBM, demonstrating the efficacy of machine learning methodologies in modeling hydrodynamic forces in turbine design. Full article

This study aims to map the evolution of pico-hydropower and Michell–Banki (cross-flow) turbine research from 2000 to 2025 through a combined bibliometric analysis and qualitative mini-review. In total, 1036 Scopus-indexed records were initially identified and refined to 922 relevant publications for analysis. Bibliometric mapping with CiteSpace, VOSviewer, and Bibliometrix identified publication trends and seven major thematic clusters (dominated by topics such as cross-flow turbine design, renewable energy integration, and asynchronous generators), while a qualitative mini-review of key studies provided contextual depth. The analysis detected 25 keywords with strong citation bursts, indicating a shift in focus over the last decade from traditional electrical regulation toward digitalization and additive manufacturing. The mini-review distilled three dominant lines of inquiry geometric design optimization, hydraulic performance characterization, and socio-economic evaluation and highlighted critical knowledge gaps, including the absence of standardized flow–head–efficiency (Q–H–η) performance data, sparse reporting of economic metrics like levelized cost of energy (LCOE), and limited high-altitude (above 3000 m) validation of pico-hydro systems. This study’s integrative approach is unique compared to prior bibliometric or technical reviews, providing a comprehensive overview of the pico-hydropower landscape and outlining a future research agenda to standardize experimental protocols, integrate economic analysis, and extend cross-flow turbine deployments to high-Andean regions. Full article

Sediment erosion is a persistent problem that leads to the deterioration of hydro-turbines over time, ultimately causing blade failure. This paper analyzes the dynamics of sediment in water and its effects on a small Kaplan turbine. Flow data is obtained independently and transferred to a separate Lagrangian-based finite element code, which tracks particles throughout the computational domain to determine local impacts and erosion rates. This solver uses a random walk approach, along with statistical descriptions of particle sizes, numbers, and release positions. The turbine runner features significantly twisted blades with rounded corners, and complex three-dimensional (3-d) flow related to leakage and secondary flows. The results indicate that flow quality, particle size, concentration, and the relative position of the blades against the vanes significantly influence the distribution of impacts and erosion intensity, subsequently the local eroded mass is cumulated for each element face and averaged across one pitch of blades. At the highest concentration of 2500 mg/m³, the results show a substantial erosion rate from the rotor blades, quantified at 4.6784 × 10⁻³ mg/h and 9.4269 × 10⁻³ mg/h for the nominal and maximum power operating points, respectively. Extreme erosion is observed at the leading edge (LE) of the blades and along the front part of the pressure side (PS), as well as at the trailing edge (TE) near the hub corner. The distributor vanes also experience erosion, particularly at the LE on both sides, although the erosion rates in these areas are less pronounced. These findings provide essential insights into the specific regions where protective coatings should be applied, thereby extending the operational lifespan and enhancing overall resilience against sediment-induced wear. Full article

As a key type of hydro-turbine unit in China, the Kaplan turbine plays a significant role in the national energy portfolio. Nevertheless, being a rotating machine, it unavoidably leaves a tip clearance between the runner blade tip and the chamber wall, which strongly disturbs the internal flow field. Based on experimental data, this study conducts numerical simulations to investigate how the blade-tip clearance affects the runner flow under various guide-vane openings and operating conditions. The results reveal that the leakage flow markedly modifies both the pressure and velocity distributions on the blades; however, its influence is weakest at the on-cam (combined) operating point. Frequency analysis shows that the pressure pulsation in the clearance region is dominated by 1 f_n and 2 f_n components, indicating that leakage vortices and runner rotation are the main sources of blade-tip pressure fluctuations. The findings provide valuable guidance for improving the economic efficiency, safety, and stable operation of Kaplan turbine units. Full article

In recent years, a growing global effort has been underway to reduce the Earth’s carbon footprint. One of the main strategies to achieve this goal is the utilization of available renewable energy resources. Among the largest and most inexhaustible is hydro-power. This paper presents an experimental study of three hydrokinetic turbines tested under real river conditions, aiming to evaluate their effectiveness in harnessing the kinetic energy of flowing water. The experiment is described in detail, including velocity field measurements conducted within the river section used for testing. Based on the experimental data, the main performance characteristics of the three turbines are presented, specifically their power output and efficiency. The importance of selecting an optimal riverbed site and customizing turbine runners to local flow conditions is highlighted, as even slight velocity fluctuations can significantly impact performance. Among the tested designs, the K1–6 turbine runner showed the highest power and efficiency, while the K2–4 runner provided superior rotational stability, making it promising for consistent energy output in variable flow environments Full article