Wake Losses Research Articles

Experimental Study on the Effect of the Blade Tip Distance on the Power and the Wake Recovery with Small Multi-Rotor Wind Turbines

In order to investigate the output power and wake velocity of small multi-rotor wind turbines compared to single-rotor wind turbines, which operate in the same swept area at various blade tip distances, this paper used the wind tunnel test method to examine single-rotor wind turbines with diameter D of 0.4 m and 0.34 m corresponding to the triple-rotor wind turbines and double-rotor wind turbines with a single rotor diameter D of 0.24 m, respectively. The experimental results indicated that, without rotation speed control, the triple-rotor wind turbine produced more power than the single-rotor wind turbine with an equivalent swept area and that the output power tended to rise initially and then fall as the distance between each rotor increased. Moreover, the power increase reached a maximum of 8.4% at the 0.4D blade tip distance. In terms of wake measurement, triple-rotor wind turbines had smaller wake losses and faster recovery rates than single-rotor wind turbines. The smaller the blade tip distance, the earlier the wake merged and fused and the faster the recovery rate. In designing small multi-rotor wind turbines, the above discussion can serve as a guide.

Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model

Abstract. Wind farm design and analysis heavily rely on computationally efficient engineering models that are evaluated many times to find an optimal solution. A recent article compared the state-of-the-art Gauss-curl hybrid (GCH) model to historical data of three offshore wind farms. Two points of model discrepancy were identified therein: poor wake predictions for turbines experiencing a lot of wakes and wake interactions between two turbines over long distances. The present article addresses those two concerns and presents the cumulative-curl (CC) model. Comparison of the CC model to high-fidelity simulation data and historical data of three offshore wind farms confirms the improved accuracy of the CC model over the GCH model in situations with large wake losses and wake recovery over large inter-turbine distances. Additionally, the CC model performs comparably to the GCH model for single- and fewer-turbine wake interactions, which were already accurately modeled. Lastly, the CC model has been implemented in a vectorized form, greatly reducing the computation time for many wind conditions. The CC model now enables reliable simulation studies for both small and large offshore wind farms at a low computational cost, thereby making it an ideal candidate for wake-steering optimization and layout optimization.

Regional climate modelling of North Sea winds with COSMO-CLM: model evaluation and the impact of future wind farm characteristics on cluster-scale wake losses - data and scripts for figures

Regional climate modelling of North Sea winds with COSMO-CLM: model evaluation and the impact of future wind farm characteristics on cluster-scale wake losses - data and scripts for figures

Direct Numerical Simulations of Turbulent Flow over Low-Pressure Turbine Blades with Aeroelastic Vibrations and Inflow Wakes

In the present work, direct numerical simulation is employed to investigate the unsteady flow characteristics and energy performance of low-pressure turbines (LPT) by considering the blades aeroelastic vibrations and inflow wakes. The effects of inflow disturbance (0 < φ < 0.91) and reduced blade vibration (0 < f < 250 Hz) on the turbulent flow behavior of LPTs are investigated for the first time. The transient governing equations on the vibrating blades are modelled by the high-order spectral/hp element method. The results revealed that by increasing the inflow disturbances, the separated bubbles tend to shrink, which has a noticeable influence on the pressure in the downstream region. The maximum wake loss value is reduced by 16.4% by increasing the φ from 0.31 to 0.91. The flow separation is majorly affected by inflow wakes and blade vibrations. The results revealed that the maximum pressure coefficient in the separated flow region of the vibrating blade has been increased by 108% by raising φ from 0 to 0.91. The blade vibration further intensifies the vortex generation process, adding more energy to the flow and the downstream vortex shedding. The vortex generation and shedding are intensified on the vibrating blade compared to the non-vibrating one that is subject to inflow wakes. The results and findings from this paper are also useful for the design and modeling of turbine blades that are prone to aeroelastic instabilities, such as large offshore wind turbine blades.

Vertical Axis Wind Turbine Layout Optimization

Vertical Axis Wind Turbines (VAWTs) are not mature enough yet for offshore wind farms, but they offer benefits compared to conventional Horizontal Axis Wind Turbines (HAWTs). Higher power densities, reduced wakes, lower center of mass, and different power and thrust curves make VAWTs an interesting option to complement existing wind farms. The optimization of wind farm layouts—finding the optimal positions of wind turbines in a park—has proven crucial to extract more energy from conventional wind farms. In this study, we build an optimizer for VAWTs that can consider arbitrarily shaped layouts as well as obstacles in the area. We adapt a recent model for the wakes of VAWTs considering a Troposkien design. We can then model and optimize a large VAWT park in a real wind scenario and assess for the first time its performance operating Troposkien VAWTs. In addition, we present a novel model for wind farm optimization that considers the clockwise and counterclockwise rotation of turbines. This optimization exploits the asymmetric wakes of VAWTs, thus increasing the total energy production. We benchmark our optimization on realistic instances and compare VAWTs and HAWTs wind farm layouts, showing that VAWTs can achieve higher density and power production than HAWTs in the same area. Finally, the wake loss reduction is compared to the literature.

Turbulence anisotropy analysis at the middle section of a highly loaded 3D linear turbine cascade using Large Eddy Simulation

This study analyzes the flow over a three-dimensional linear low-pressure turbine cascade blade using large eddy simulation at Re = 90,000. The computational model consists of one blade passage with periodic boundaries and synthetic turbulence is generated at the inlet of the domain. Various flow metrics, including isentropic Mach number distribution at mid-span and wake total pressure losses are compared with available experimental data and found to be in good agreement. A more detailed analysis of the turbulence with particular attention to the separation bubble region is subsequently presented. The analysis revealed that the turbulence is in a nearly two-component state very close to the wall region and gradually follows a certain anisotropy trajectory, as the distance from the wall increases. Even in the free-stream region no fully isotropic state is reached, due to large acceleration and flow turning. The results give a new insight into the state of turbulence within the separation region on the blade suction side and emphasize the deficiencies of the Reynolds-averaged Navier Stokes (RANS) turbulence models in reproducing the turbulence anisotropy. This insight is of relevance for the aerodynamic design of turbines, since large parts of the total pressure loss are generated in the separation region.

Iceland wind farm assessment case study and development: An empirical data from wind and wind turbine

This study aimed to apply empirical data to assess wind energy production at the Búrfell site in Iceland based on the E44 turbine model. The empirical data are 5 years of recordings at the site location by the Iceland Metrological office. The wind speed data are measured at a 10 m height from 2017 to 2021. There are two E44 wind turbines test installed on the site. In the previous studies, the wind farm capacity and Levelized cost of energy (LCOE) were reported without investigating the wake loss model and its impacts on LCOE and have an estimation applied. The previous research was based on the two installed wind turbines at the site, which are located in a straight line and perpendicular to the prevailing wind speed. This study applies the Jensen-Katic model to investigate wake loss. Downwind and crosswind ten-rotor diameters and five-rotor diameters are calculated respectively as the best options. Afterward, an appropriate number of wind turbines is suggested for 80MW production. In addition, this study's optimum capacity factor (CF) is 26.08%, which was reported at 37.9% - 38.38% before. On average, the turbines produce less than 30% of their rated power, which has been reported at 38.15% in prior studies. This study presents the LCOE as equal to 0.0659 USD/kWh, which is less than 0.0703 USD/kWh in the previous studies and the LCOE reported by the 2020 LCOE European report. The obtained LCOE in this study is based on the weighted average cost of capital in the energy project by Landsvirkjun, the national power company of Iceland. The obtained result from the model used, which matched the empirical measurements, displays Iceland's best rank for wind energy LCOE metric among European countries. The proposed method provides a vision to use the wake loss model output in deep learning training to predict power production, leading to a sustainable and reliable power grid.

A passively self‐adjusting floating wind farm layout to increase the annual energy production

AbstractWake losses inside a wind farm occur due to the aerodynamic interactions when a downwind turbine is in the wake of upwind turbines. The ability of floating offshore wind turbines (FOWTs) to relocate their positions in the horizontal plane introduces an opportunity to decrease the wake losses in a floating wind farm (FWF). Our goal is to use this ability to passively move the downwind FOWT out of the wake of upwind ones. Since the mooring system (MS) attached to a FOWT is responsible for its station keeping, the horizontal motions of the FOWT depend on the MS design. Hence, if we can design the MS to passively move the FOWT out of the wake, we can increase the FWF annual energy production (AEP). In this paper, we investigate if we can benefit from relocating FOWTs in a FWF and increase its AEP. In addition, we present a novel approach that considers the ability of a FOWT to relocate its position as a new degree of freedom (DoF) in the FWF layout design. This means we will have a self‐adjusting wind farm layout where the FOWTs passively re‐arrange themselves depending on the wind direction and the wind speed. Consequently, we will have a slightly different wind farm layout for every wind direction and every wind speed. To achieve this layout, we include the MS design as part of the FWF's layout design. In a self‐adjusting FWF layout, each FOWT is attached to a customized MS design allowing it to relocate its position in the best way possible according to the wind direction, to increase the overall AEP of the wind farm. The results of one case study show that the novel approach can increase the FWF's AEP by 1.6% when compared with a current state of the art optimized floating wind farm layout. Finally, we implemented our method as an open‐source python tool to be used and enhanced further within the wind energy community.

Hydraulic loss analysis in a pump-turbine with special emphasis on local rigid vortex and shear

Since the pump as turbine is increasingly employed in energy storage, improving efficiency in both pump and turbine modes is required for economic benefits. This study aims to analyze vortex flow characteristics and vortex control methods in both modes to reduce hydraulic loss. In this paper, a delayed detached eddy simulation was applied in a low specific speed pump-turbine. Based on the entropy production analysis and vorticity binary decomposition in the local vortices, the results show that the local shear is the leading cause of hydraulic loss instead of the existence of vortices. The average wake loss can be 1.6 times higher than the loss in jet regions in pump mode, but there is little difference in the distribution of shear and vortices in the wake flow in turbine mode. The local loss caused by the rotor–stator interaction with a tongue effect at blade passing frequency is up to threefold over the loss without a tongue effect in both modes. Reducing shear and ratio of shear to rigid vorticity of the local vortices via modification in the volute tongue angle to suppress the tongue effect can be an effective way to decrease hydraulic loss in both modes.

Large-eddy simulation study of rotating detonation supersonic turbine nozzle generated by the method of characteristics under oscillating incoming flow

Rotating detonation turbine engine is receiving considerable attention due to its' high cycle efficiency, outstanding thrust characteristics, self-pressurization, and energy-saving attributes. Conventional turbines are inefficient (30%) under rotating detonation inflow conditions. In order to obtain the turbine operating efficiently under the condition of rotating detonation inflow, this paper uses the method of characteristics and Bessel parameterization to design the blade profile of the rotating detonation supersonic turbine. The Large Eddy Simulation is used to numerically study the flow field characteristics of the supersonic turbine blade designed by the method of characteristics. The study found that the rotating detonation supersonic turbine guide vane can effectively reduce the pressure oscillation amplitude of the incoming flow to 25% of the original amplitude, and the main frequency (10 kHz) of the incoming flow occupies the main part of the flow field frequency. Second, the morphological evolution of the shock waves attenuates the adverse pressure gradient on the suction surface. The separation area of the suction surface slowly oscillates and attenuates, and is eventually confined to a small region. The wake accelerates and dissipates under the squeezing jet of the dovetail wave and the intense shearing action, forming a small wake area. The attenuation of large-scale separation gradually reduces the separation loss and wake loss, and the convergence and interaction of shock waves and the wake vortex significantly enhance the proportion of entropy production in the shock region. From the pressure coefficient and is entropic Mach number distributions, it is found that the blade load is mainly concentrated in the tail, and is minimized when the flow field becomes stable. These features provide a reference for the design of rotating detonation supersonic turbines and a deeper understanding of the flow field characteristics of rotating detonation turbine engines.

FarmConners wind farm flow control benchmark – Part 1: Blind test results

Abstract. Wind farm flow control (WFFC) is a topic of interest at several research institutes and industry and certification agencies worldwide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, the FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits, such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments, and high-fidelity simulations. Within that database, four blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wakes under WFFC. Here, we present Part I of the FarmConners benchmark results, focusing on the blind tests with large-scale rotors. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. The majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration are particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, the sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.

Effect of loading level and axial distribution on uncertainty performance of turbine blade with geometric variations

Uncertainty aerodynamic design of turbine blades considering the uncertainty impact of geometric variations is significant for the robustness of actual performance. The effect of loading level and its axial distribution on the uncertainty performance of a turbine blade was discussed, and the guidance for the uncertainty loading design was derived. It is demonstrated that the uncertainty impact on the performance is always notable and nonlinear, which is mainly caused by the significant geometric variations in the sensitive areas where the flow state is sensitive to local geometric variations, and the high loading in these areas will aggravate this impact. The nonlinearity means that the damage to performance caused by those harmful geometric variations cannot be offset by the opposite ones, and the loss increases overall. This impact increases parabolically with the increase of loading level, and will be enlarged clearly with an obvious front-loading or aft-loading. Fortunately, modest aft-loading/mid-loading has a great capacity to resist this uncertainty impact, for the relieving of loading in the sensitive areas, and the negative correlation between the profile loss caused by separation and the wake mixing loss. Considering the overall uncertainty performance, a modest mid-loading/aft-loading design is optimal for high/moderate loading levels, respectively. It is proved that reasonable loading distribution can improve the actual uncertainty performance of turbine blades, which provide a powerful support for the consequent uncertainty design.

Collective wind farm operation based on a predictive model increases utility-scale energy production

In wind farms, turbines are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. Here we operate a wind turbine array collectively to maximize array production through wake steering. We develop a physics-based, data-assisted flow control model to predict the power-maximizing control strategy. We first validate the model with a multi-month field experiment at a utility-scale wind farm. The model is able to predict the yaw-misalignment angles which maximize array power production within ± 5° for most wind directions (5–32% gains). Using the validated model, we design a control protocol which increases the energy production of the farm in a second multi-month experiment by 3.0% ± 0.7% and 1.2% ± 0.4% for wind speeds between 6 m s−1 and 8 m s−1  and all wind speeds, respectively. The predictive model can enable a wider adoption of collective wind farm operation. Individual operation of turbines in wind farms results in energy losses from wake interactions. Here Howland et al. report on an experimentally validated model to implement collective operation of turbines, which increases the farm’s energy production.

Loads assessment of a fixed‐bottom offshore wind farm with wake steering

AbstractWake steering via deliberate yaw offset is an emerging wind farm control technique that has the potential to mitigate wake losses and further increase wind farm energy yield. The loads impact of this technique has been studied, but there is limited insight into wind‐farm‐wide impacts of wake steering. Understanding such impacts is crucial to determining the feasibility of using wake steering in commercial wind farms. To that end, this work investigates the impacts of wake steering on the loads of all turbine components across all turbines in a wind farm operating under a broad set of inflow conditions, including inflow velocity, shear exponent, turbulence class, and inflow angle. This was done by performing FAST.Farm simulations of a 12‐turbine wind farm array, excerpted from a larger hypothetical wind farm. The International Energy Agency Wind 15‐MW reference wind turbine was modeled atop a monopile substructure, an open‐source model that closely approximates the properties of similar commercial options. Wake steering was included via yaw offsets that were computed using an offline optimization with the National Renewable Energy Laboratory tool FLORIS. For each inflow case, the 12‐turbine array was simulated with and without wake steering. Results were compared in terms of time‐averaged means, standard deviations, ultimate loads, and damage‐equivalent loads. The findings show that because wake steering is generally applied at rated wind speeds and below, it is unlikely to drive ultimate loads. For fatigue loads, wake steering does increase the overall fatigue accumulation for some load channels, such as blade‐root and shaft bending. This is to be expected when overall power yield increases but may cause the damage accumulation to be more uniform throughout the array. The significance of the added fatigue loading is dependent on how frequent wake steering is utilized in the overall set of inflow conditions across the wind rose.

Wind farm layout optimization to minimize the wake induced turbulence effect on wind turbines

High turbulence intensity induced by the wake effect in wind farms is of great significance to the fatigue life of wind turbines. However, previous wind farm layout optimization (WFLO) only considers the shading effect between wind turbines. To fill this critical gap, we propose a novel multi-objective WFLO (MO-WFLO) framework considering both the power generation and the distribution of streamwise turbulence intensity in the wind farm. In this framework, a newly developed turbulence intensity model of turbine wake is used to calculate the turbulence intensity distribution, and a two-dimensional Gaussian wake model is adopted to calculate the wake losses of the wind farm, in which the wake expansion rate of turbine wake is determined accounting for the turbulence intensity of the turbine’s inflow. The nondomination sorting genetic algorithm with elite strategy (NSGA-II) is resorted to maximize the total power generation and to minimize the comprehensive streamwise turbulence intensity of the turbines’ inflow. The results show that under the same annual energy production, our proposed framework can effectively reduce the maximum turbulence intensity level of the turbines with high fatigue loads, and shorten the cumulative operation time of the wind turbines in high turbulence conditions in the wind farm. By applying the proposed framework to a realistic wind farm, a new layout can be obtained with increased total power by 0.8% and a reduced maximum comprehensive streamwise turbulence intensity of the turbines by 8.1% compared with the original layout.

Comparison of individual versus ensemble wind farm parameterizations inclusive of sub‐grid wakes for the WRF model

AbstractWind turbine wakes can be predicted somewhat accurately with mesoscale numerical models, such as the Weather Research and Forecast (WRF) model, via a wind farm parameterization (WFP) that treats the effects of the wakes, which are sub‐grid features, on power production and the environment. A few WFPs have been proposed in the literature, but none has been able to properly account for the individual wakes within a grid cell or the effects of overlapping wakes from multiple turbines. A solution to these two issues is a WFP that includes both a wake model, which is a simplified analytical model of the wind speed (or wind power) deficit caused by a wake, and a wake superposition model, which accounts for overlapping wakes. Several such WFPs are developed here for the WRF model—based on the Jensen, the Geometric, and the Gaussian wake models coupled with two wake superposition methods (based on a squared deficit and a squared velocity superposition)—and tested individually, as well as combined together in an ensemble (EWFP), at two modern offshore wind farms. Most WFPs perform satisfactorily alone, but the EWFP generally outperforms them at both farms. The issue of resolved versus sub‐grid wakes is explored for single‐ and multi‐cell cases and for directions of alignment and non‐alignment between the wind direction and the turbine columns. Although different combinations of wake loss and wake superposition models might be preferred at other wind farms, the general findings and detailed performance statistics given here might provide useful guidance in their selection.

Study on influencing factors of trailing edge flow characteristics of transonic micro turbine rotor

In order to meet the needs of the development of high-performance micro-engine, the micro-turbine is faced with the technical challenge of high-load and transonic flow. At present, the flow characteristics and loss mechanism of high-load micro-impeller are not clear. In this paper, the flow characteristics of high-load micro-turbine rotor are studied. The results show that the loss in rotor trailing edge is the main factor that affect the operation of the micro transonic turbine, and the key to reduce the loss is to reduce the wake diffusion range and the Mach number in front of the extended shock wave. By adjusting the local curvature of the front in front of the incident point of the back-extension wave in the throat of the suction surface, the convergent-divergent ratio is in the range of 1.04 ~ 1.08. Meanwhile, the Mach number of wave front can be reduced by 3.4% and the reflection of trailing edge inward extension can be effectively suppressed, and the total rotor pressure loss coefficient can be reduced by 14.1% at most. The thickness has an important effect on the wave strength and wake loss in the trailing edge. When the relative thickness of the trailing edge decreases by 50%, the maximum Mach number on the suction surface decreases by 7.8%, the turbine stage efficiency increases by 1.92%, and the relative thickness of trailing edge is 0.124~0.185, the studied micro-transonic turbine performs better.

Wind power generation and appropriate feed-in-tariff under limited wind resource in central Thailand

The objective of this paper is to assess the wind energy resource in the central region of Thailand for wind power generation, along with analyzing the economic feasibility and appropriate feed-in-tariff (FIT) of a proposed 15 MW wind power plant. A microscale wind resource map was created using measured wind data, a computational fluid dynamics wind flow modeling and high-resolution topography databases. Five utility-scale wind turbine generators (WTG), with hub heights ranging from 80 to 120 m above ground level (agl), were used to estimate the annual energy production (AEP). Considering the available wind resource, the most appropriate WTG was identified, and a wind power plant layout was achieved to maximize the AEP as well as minimizing the wake losses. The maximum net AEP, capacity factor (CF), %AEP improvement, %wake loss reduction, and CO2eq emission avoidance were also analyzed. Several financial indices and the levelized cost of energy (LCOE) were analyzed on the basis of a cost–benefit analysis. The economic sensitivity of the costs was used to determine the most appropriate FIT for the project. Results reveal that the mean annual wind speed at 120 m agl in the central region of Thailand reaches 5.8 m/s. The net AEP, CF, %AEP improvement, %wake loss, and CO2eq emission avoidance for a 15 MW wind power plant are estimated at 41 GWh/year, 30%, 6%, 10% and 231 ktonnes CO2eq/year, respectively. The LCOE for a base case scenario is estimated at 0.093 US$/kWh, with a FIT of 0.195 US$/kWh. Finally, the results of this work can be used as guidelines for wind power project development in the central region of Thailand and in other regions of the world where the wind resource is low to moderate under current existing WTG technology.

Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses

With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and have found wide success in predicting wake losses and expected power production. This paper proposes a modular framework for attention-based graph neural networks (GNN), where attention can be applied to any desired component of a graph block. The results show that the model significantly outperforms a multilayer perceptron (MLP) and a bidirectional LSTM (BLSTM) model, while delivering performance on-par with a vanilla GNN model. Moreover, we argue that the proposed graph attention architecture can easily adapt to different applications by offering flexibility into the desired attention operations to be used, which might depend on the specific application. Through analysis of the attention weights, it was showed that employing attention-based GNNs can provide insights into what the models learn. In particular, the attention networks seemed to realise turbine dependencies that aligned with some physical intuition about wake losses.

A parametric study of the mooring system design parameters to reduce wake losses in a floating wind farm

Wake effects inside a conventional fixed bottom wind farm decrease the power produced by the downwind turbines, hence decreasing the farm’s annual energy production (AEP). However, floating offshore wind turbines (FOWTs) have the ability to relocate their positions laterally through surge and sway motions. This flexibility provides a new degree of freedom (DOF) in the floating wind farm layout, which can be used to decrease the aerodynamic interactions inside the floating wind farm and hence decrease the wake losses. The lateral movement of FOWTs can be passively controlled by the mooring system design. The mooring system’s restoring characteristics allows the FOWT to only move within a specific area in the x-y plane known as the watch circle. Current state of the art mooring system designs are following oil and gas design basis where the floating platforms are not allowed to have large lateral displacements. In this work, we use full factorial design to analyse the effect of different mooring system design parameters on the ability of the floater to relocate its position. The analysis shows that each design parameter has a different way of affecting the FOWT’s response. The mooring lines’ headings control which wind directions cause the biggest displacements in the crosswind direction. The smaller the lines’ diameters the higher the displacements of the FOWT. Finally, the longer the line length the smaller the mooring system’s stiffness and hence the larger the FOWT’s displacement. The results of this study can be used as the basis for floating wind farm optimization, in which the wind turbines are allowed to passively relocate their positions according to the wind speed and wind direction.