Row Of Turbines Research Articles

A deep-learning approach for 3D realization of mean wake flow of marine hydrokinetic turbine arrays

We present a novel convolutional neural network (CNN) algorithm to reconstruct turbulence statistics in the wake of marine hydrokinetic (MHK) turbine arrays installed in large meandering rivers. To train the CNN, we utilize large eddy simulation (LES) data depicting the wake flow from a single row of turbines. Once trained, the CNN is deployed to forecast the wake flow of MHK turbine arrays under different hydrodynamic conditions and for varying waterway plan-form geometry. Validation of the CNN predictions are conducted using independently performed LES. Our findings demonstrate the capacity of CNN to accurately predict the wake flow of MHK turbine arrays at significantly reduced computational cost compared to LES. Additionally, the comparison between CNN and unsteady Reynolds-averaged Navier-Stokes (URANS) simulation exhibits a notable advantage of CNN in prediction efficiency and accuracy. This research highlights the potential of CNN to establish reduced-order models for facilitating control co-design and optimization of MHK turbine arrays within natural environments.

Effects of wind turbine rotor tilt on large-scale wind farms

Recent studies have explored the use of rotor tilt adjustments to reduce wake losses in wind farms. While downward wake deflection in aligned wind farms has shown promise for significant power gains, the impact of wind farm layout on the effectiveness of tilt strategies is not yet fully understood. Additionally, the effect on downstream farms remains unclear. Our large eddy simulations reveal that a rotor tilt of 20 degrees significantly reduces wake losses in aligned wind farms. For wind farms with 8 turbine rows, we observe an overall increase in wind farm productivity of up to 11%. However, tilting the rotors may decrease power production in staggered wind farms, where wake losses are inherently lower due to the increased spacing between turbines. Our findings also suggest that a downstream wind farm might benefit from an upstream farm implementing rotor tilt, although this advantage is primarily observed in the first row of the downstream farm.

The global blockage effect of a wind farm cluster - an LES study

The interaction of wind farm clusters with the atmospheric flow is complex. It comes along with phenomena that have still not been fully understood in detail. However, having an understanding of the flow is a prerequisite for the derivation of models that can accurately and with limited computational resources replicate the most prominent features of the flow. This study exploits large-eddy simulations (LES) to create a better understanding of the wind farm cluster blockage under a set of different atmospheric conditions. The specific wind farm cluster consists of three wind farms, with a relatively narrow gap between the two northernmost wind farms. Results reveal that under conventionally neutral boundary layers, the induction zone relatively large is when there is a low atmospheric boundary layer height with a strong temperature inversion. In our LES study, wind speed is reduced between 2% and 4% 2D upstream of the front row of the wind farm, while inside the gap of the wind farm cluster, there is an acceleration of the wind speed. Comparatively, blockage for a solitary row or single wind turbine is similar and smaller than for a whole cluster. On average, turbines in the front row of a cluster produce 5.1% less power than a single turbine.

Turbulence coherence in wind farms: The role of turbines

Models for wind farm power fluctuations primarily focus on the impact of atmospheric turbulence. We employ large eddy simulations (LES) to demonstrate that dynamic changes in thrust (CT ) and power (CP ) coefficient affect the coherence of power fluctuations in turbine pairs. We consider various inflow wind speeds to examine the velocity and power coherence between consecutive turbine rows under three scenarios: (I) a fully developed region where all turbines operate below rated power, (II) the front row operates above rated power while the downstream row operates below rated power, and (III) both rows operate above rated power. In scenario I, the random sweeping hypothesis turbulence model by Tobin and Chamorro, JFM 855, 1116-1129 (2018) can effectively predict the coherence between the turbines. However, in scenarios II and III, the model fails to capture the simulation results. This discrepancy arises due to the operation of above-rated turbines, with dynamically varying CT and CP , which have a distinctly different effect on the flow than turbines operating with fixed CT and CP .

Influence of the tip speed ratio on the wake dynamics and recovery of axial-flow turbines

Detached eddy simulation is employed to investigate the wake development downstream of the rotor of an axial-flow turbine and its dependence on the tip speed ratio. In this study, we found that the trend of the momentum deficit as a function of the rotational speed shows opposite directions in the near wake and further downstream. While the momentum deficit in the near wake increases with the rotational speed, it decreases further downstream. For instance, we found that at six diameters downstream of the rotor the streamwise velocity in its wake recovered to about 30% of its free-stream value at the lowest simulated tip speed ratio of 4, while its recovery was equal to about 65% at the largest tip speed ratio of 10. This is due to the earlier breakdown of the tip vortices. The results of the computations demonstrate indeed that mutual inductance phenomena between tip vortices, promoting pairing events and the eventual instability of the helical structures, occur at shorter downstream distances for higher values of tip speed ratio. Wake instability enhances the process of wake recovery, especially due to radial advection. Therefore, higher rotational speeds do not promote wake recovery through more intense tip vortices, but through their greater instability. Implications are important, affecting the optimal distance between rows of axial-flow turbines in array configurations: the operation at higher rotational speeds allows for smaller distances between turbines, decreasing the cost and environmental impact of farms consisting of several devices.

Predicting turbulent wake flow of marine hydrokinetic turbine arrays in large-scale waterways via physics-enhanced convolutional neural networks

We present a physics-enhanced convolutional neural network (PECNN) algorithm for reconstructing the mean flow and turbulence statistics in the wake of marine hydrokinetic (MHK) turbine arrays installed in large-scale meandering rivers. The algorithm embeds the mass and momentum conservation equations into the loss function of the PECNN algorithm to improve the physical realism of the reconstructed flow fields. The PECNN is trained using large eddy simulation (LES) results of the wake flow of a single row of turbines in a virtual meandering river. Subsequently, the trained PECNN is applied to predict the wake flow of MHK turbines with arrangements and positionings different than those considered during the training process. The PECNN predictions are validated using the results of separately performed LES. The results show that the PECNN algorithm can accurately predict the wake flow of MHK turbine farms at a small fraction of the cost of LES. The PECNN can improve the accuracy by around 1% and reduce the physical constraint indices by around 50% compared to the CNN without physical constraints. This work underscores the potential of PECNN to develop reduced-order models for control co-design and optimization of MHK turbine arrays in natural riverine environments.

Impact of yaw misalignment on turbine loads in the presence of wind farm blockage

SummaryWake steering is very effective in optimizing the power production of an array of turbines aligned with the wind direction. However, the wind farm behaves as a porous obstacle for the incoming flow, inducing a secondary flow in the lateral direction and a reduction of the upstream wind speed. This is normally referred to as blockage effect. Little is known on how the blockage and the secondary flow influence the loads on the turbines when an intentional yaw misalignment is applied to steer the wake. In this work, we assess the variation of the loads on a virtual 4 by 4 array of turbines with intentional yaw misalignment under different levels of turbulence intensity. We estimate the upstream distance at which the incoming wind is influenced by the wind farm, and we determine the wind farm blockage effect on the loads. In presence of low turbulence intensity in the incoming flow, the application of yaw misalignment was found to induce a significant increase of damage equivalent load (DEL) mainly in the most downstream row of turbines. We also found that the sign (positive or negative) of the yaw misalignment affects differently the dynamic loads and the DEL on the turbines. Thus, it is important to consider both the power production and the blade fatigue loads to evaluate the benefits of intentional yaw misalignment control especially in conditions with low turbulence intensity upstream of the wind farm.

High-resolution CFD modelling of Lillgrund Wind farm

We report on a fully dynamic simulation of Vattenfall’s Lillgrund offshore Wind Farm, with a focus on the wake effects of turbines on the performance of individual turbines, and of the farm as a whole. The model uses a dynamic representation of a wind turbine to simulate interaction between the wind and the turbine rotors, calculating the instantaneous power output and forces on the air; this was embedded in a finite element, large eddy simulation (LES) computational fluid dynamics code. This model was applied to the wind farm for a selection of key wind speeds and directions, to investigate cases where a row of turbines would be fully aligned with the wind or at specific angles to the wind. The simulation results were then compared to actual performance measurements from the wind farm spanning several years’ of operation. These results demonstrate that time-resolving LES simulations are able to reproduce realistic wake structures, including wake meandering and wake recovery, as well as the effect of wakes on turbine performance.

Tidal turbine array modelling using goal-oriented mesh adaptation

To examine the accuracy and sensitivity of tidal array performance assessment by numerical techniques applying goal-oriented mesh adaptation. The goal-oriented framework is designed to give rise to adaptive meshes upon which a given diagnostic quantity of interest (QoI) can be accurately captured, whilst maintaining a low overall computational cost. We seek to improve the accuracy of the discontinuous Galerkin method applied to a depth-averaged shallow water model of a tidal energy farm, where turbines are represented using a drag parametrisation and the energy output is specified as the QoI. Two goal-oriented adaptation strategies are considered, which give rise to meshes with isotropic and anisotropic elements. We present both fixed mesh and goal-oriented adaptive mesh simulations for an established test case involving an idealised tidal turbine array positioned in a channel. With both the fixed meshes and the goal-oriented methodologies, we reproduce results from the literature which demonstrate how a staggered array configuration extracts more energy than an aligned array. We also make detailed qualitative and quantitative comparisons between the fixed mesh and adaptive outputs. The proposed goal-oriented mesh adaptation strategies are validated for the purposes of tidal energy resource assessment. Using only a tenth of the number of degrees of freedom as a high-resolution fixed mesh benchmark and lower overall runtime, they are shown to enable energy output differences smaller than 2% for a tidal array test case with aligned rows of turbines and less than 10% for a staggered array configuration.

Study on the Impact of Offshore Wind Farms on Surrounding Water Environment in the Yangtze Estuary Based on Remote Sensing

Offshore wind farms (OWFs), built extensively in recent years, induce changes in the surrounding water environment. The changes in the suspended sediment concentration (SSC) and chlorophyll-a concentration (Chl-aC) induced by an OWF in the Yangtze River Estuary were analyzed based on Chinese Gaofen (GF) satellite data. The results show the following: (1) The flow near the wind turbines makes the bottom water surge, driving the sediment to “re-suspend” and be lost, deepening the scour pit around the bottom of the wind turbines, which is known as “self-digging”. The interaction between the pillar of a wind turbine and tidal currents makes hydrodynamic factors more complicated. Blocking by wind turbines promoting the scour of the bottom seabed of the OWF results in speeding up the circulation rate of sediment loss and “re-suspension”, which contributes to the change in the SSC and Chl-aC. This kind of change in sediment transport in estuarine areas due to human construction affects the balance of the ecological environment. Long-term sediment loss around wind turbines also influences the safety of wind turbines. (2) The SSC and Chl-aC are mainly in the range of 200–600 mg/L and 3–7 μg/L, respectively, in the OWF area, higher than the values obtained in surrounding waters. The SSC and Chl-aC downstream of the OWF are higher than those upstream, with differences of 100–300 mg/L and 0.5–2 μg/L. High SSC and Chl-aC “tails” appear downstream of wind turbines, consistent with the direction of local tidal currents, with lengths in the range of 2–4 km. In addition, the water environment in the vicinity of a wind turbine array, with a roughly 2–5 km scope (within 4 km during flooding and around 2.5 km during ebbing approximately) downstream of the wind turbine array, is impacted by the OWF. (3) In order to solve the problem of “self-digging” induced by OWFs, it is suggested that the distance between two wind turbines should be controlled within 2–3.5 km in the main flow direction, promising that the second row of wind turbines will be placed on the suspended sediment deposition belt induced by the first row. In this way, the problems of ecosystem imbalance and tidal current structure change caused by sediment loss because of local scouring can be reduced. Furthermore, mutual compensation between wind turbines can solve the “self-digging” problem to a certain extent and ensure the safety of OWFs.

Evaluation of small hydropower turbines installed downstream of Nile River branches (Egypt)

The current study proposes a new strategy for using small hydroelectric turbines in downstream river branches with the least amount of construction and the lowest cost by comparing two different methods of installing the turbines, the first by installing the turbines at the river's bottom and the second by installing the turbines on floating boats. The methodology of this article is based on predicting the distribution of velocities through the watercourse using experimental data collected at various points in the river's depth, and then predicting the resulting electrical power for different sizes of turbines, as well as estimating the number of turbines for each row and the number of rows along the river. Therefore, Investigate the proposed systems. The proposed small hydropower system's economic viability and environmental impact are investigated in this article. According to the nature of the waterway, the best diameter of a turbine that can be used is 1.5 m based on water velocities and river depths. The proposed power plant generated 25.8 kW per single turbine row, with an estimated cost of produced power (0.035 USD/kWh) of approximately 20 turbines installed per row. Compared to other renewable energy sources, the proposed hydropower system is cost-effective and environmentally friendly, as generating electricity with the proposed small hydropower plant could reduce annual carbon dioxide emissions by 368 tones of CO2 per single turbine row.

High-fidelity modelling of a six-turbine tidal array in the Shetlands

Micro-siting tidal stream turbines in a confined seabed area requires a extensive understanding of the flow dynamics over the water column at turbine deployment locations so that operating conditions are assessed, wake effects can be estimated to infer the energy yield [1], or bathymetry effects can be quantified. Tidal currents have the advantage of being highly predictable and mostly bidirectional but the uneven bathymetry found at most of sites introduces a high variability to the flow conditions within relatively short distances. Considering future tidal turbine arrays will comprise dozens of devices, deploying ADCPs at each turbine position would be very expensive or very time consuming, outlining the need for accurate modelling tools to be used as digital twins in micro-siting. Shallow-water models are widely adopted in preliminary design of tidal arrays but fail to capture the three dimensional nature of the flow and predict deflected wakes whose streamwise length is also over-predicted [2]. Thus, eddy-resolving method are required to fully capture the turbulence from the free-stream flow, induced by turbines and from bathymetry. \\
 This study provides a real-project application of the state-of-the-art large-eddy simulation (LES) code DOFAS (Digital Offshore Farms Simulator) [3] to the six 100kW-turbine array deployed by NOVA Innovation Ltd. in the Shetlands, UK [4]. The bathymetry data has been obtained from EMODnet database with the velocity profiles set at the inlet condition of both ebb and flood tides imported from Macleod et al. (2019) [4]. The deployment site is characterised by steep slopes with a maximum depth of 45 m at the cross-section where turbines are located. The bathymetry has a downwards slope when the flow goes in the ebb tide direction whilst upstream during flood tide. The tidal rose indicated a slight deviation of about 20$^{\circ}$ between ebb and flood directions. The turbines have a diameter ($D$) of 9 m attached to a 10 m long hub whose diameter is 1 m. Three array configurations have been studied: (i) single row of three turbines, (ii) two rows of turbines spaced 8$D$, and (iii) two rows of turbines spaced 12$D$. The computational domain extends over 600 m by 300 m in the horizontal plane yielding 540 million cells, requiring 32,500 CPU hours to compute 30 min of physical time.\\
 Results show that bathymetry effects at this site play a larger role when designing the location of the secondary row of turbines compared to wake effects from upstream turbines. During the ebb tide, the increase in water depth reduces the wake recovery far downstream so that the array with 12$D$ row spacing has a lower performance than the 8$D$ one with approx. 30\% decrease in energy yield. Conversely, the uphill shape of the bathymetry during the flood enables a fast wake recovery so that the downstream row experiences almost no energy yield loss due to wakes.

Instabilities in tidal turbine wakes

We report on the observation of vortex instabilities in the wake of a tidal turbine undergoing harmonic unsteady axial inflow (e.g. due to surface waves). The work was carried out using unsteady RANS (URANS) modelling using the open-source CFD software OpenFOAM, using the `pimpleDyMFoam' solver. The PIMPLE algorithm used in the URANS solver is a combination of the PISO (Pressure Implicit with Splitting of Operator) and SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) algorithms. URANS simulations usually have a limitation on the time step set by the Courant number (C = u (delta t)/(delta x), where delta x is the minimum cell length and u the local velocity. In the PISO algorithm, C<1 is usually required. However, the PIMPLE algorithm allows stable transient simulations at C>>1 by applying under-relaxation to each time-step until a convergence criterion is met, before allowing the time-step to complete with no applied relaxation factors. The turbulence model used was komega-SST, which has been used extensively in tidal turbine modelling.
 As with a wind turbine, the response of a tidal turbine to unsteady flow is heavily influenced by the behaviour of the helical wake that returns in close proximity to the turbine blades times per revolution ( – number of blades). Unsteady inflow causes the blades to shed vorticity into the wake, which in turn leads to a spatial variation in wake vortex strength, such that the vorticity of adjacent returning wake segments can differ, and thus there is a spatially varying velocity deficit in the wake. This spatial variation in velocity deficit triggers instability in the blade tip vortices, the emergence of which we demonstrate is governed by the non-dimensional group relating the blade passing frequency to the gust frequency. If the ratio between these two frequencies is an integer, the wake is stable. If not, the tip vortices exhibit various forms of instability. If the wake is unstable, adjacent wake segments will eventually coalesce as they move downstream, such that the wake consists of larger regions of vorticity with lower spatial frequency, compared to the wake immediately behind the turbine. There is also some indication that these unstable, coalesced wakes persist further downstream, compared with stable wakes. This has implications for wind and tidal farm design, where interaction of a row of turbines with the wakes of upstream turbines is an important consideration.

The characteristics of helically deflected wind turbine wakes

The helix approach is a new individual pitch control method to mitigate wake effects of wind turbines. Its name is derived from the helical shape of the wake caused by a rotating radial force exerted by the turbine. While its potential to increase power production has been shown in previous studies, the physics of the helical wake are not well understood to date. Open questions include whether the increased momentum in the wake stems from an enhanced wake mixing or from the wake deflection. Furthermore, its application to a row of more than two turbines has not been examined before. We study this approach in depth from both an analytical and numerical perspective. We examine large-eddy simulations (LES) of the wake of a single turbine and find that the helix approach exhibits both higher entrainment and notable deflection. As for the application to a row of turbines, we show that the phase difference between two helical wakes is independent of ambient turbulence. Examination of LES of a row of three turbines shows that power gains greatly depend on the phase difference between the helices. We find a maximum increase in the total power of approximately 10 % at a phase difference of $270^\circ$ . However, we do not optimise the phase difference any further. In summary, we provide a set of analytical tools for the examination of helical wakes, show why the helix approach is able to increase power production, and provide a method to extend it to a wind farm.

Optimal control of tidal flow

The tidal flow through a channel connecting two basins with different tidal regimes can be optimally controlled by means of a turbine fence or array to maximise the extracted mechanical power. The paper gives the optimal control strategy as a function of the blockage ratio $\sigma$ , i.e. the ratio of the turbine cross-section to the cross-section of the local passage of a turbine. The results presented are a physically consistent generalisation of the results of Garrett & Cummins (Proc. R. Soc. Lond. A, vol. 461, 2060, pp, 2563–2572), valid only for $\sigma =1$ and turbine efficiency of one, now for arbitrary blockage ratio $0 < \sigma \leqslant 1$ . Published research over the past decade on the same topic has taken the momentum equation and the turbine drag force as a starting point. The new approach presented here, in contrast, takes the energy equation as the starting point and uses the relative volume flow as the control variable. As the work shows, this new approach has three advantages. First, starting with the energy equation allows us to derive an optimal flow control problem resulting in an Euler–Lagrange equation using the physically consistent and experimentally validated actuator disk model for the free surface flow of Pelz et al. (J. Fluid Mech., vol. 889, 2020) in a direct and formal way. The optimal control problem is solved (a) numerically and (b) analytically. In the latter case, the turbine characteristics are approximated by a rational function in the relevant design and operating range. The analytical solution (b) validated against the numerical solution (a) is surprisingly concise and easy to apply in practice, as shown by use cases. Second, instead of the induction factor, we use the volume flow that is the same for all turbines in a cascade, i.e. a row of turbines in the direction of flow, which significantly reduces the complexity of the optimal control task of turbine arrays. Third, we obtain a well-founded energy estimate, whereas previous methods overestimate the energy yield due to inconsistent turbine disc models (for the consistency and valid parameter ranges of different models, also in comparison with experiments, see Pelz et al., J. Fluid Mech., vol. 889, 2020). The results can be used for the conceptual design of turbine arrays, but also for a sound physically realistic and consistent resource assessment of tidal power for a system consisting of two basins, a channel and a turbine fence with $0<\sigma \leqslant 1$ and operated in a complete tidal cycle.

Comparison of three DWM-based wake models at above-rated wind speeds

In this study we investigate three mid-fidelity wind turbine wake models based on the dynamic wake meandering (DWM) model principle, and compare their performance with a reference dataset, produced with large-eddy simulations using the actuator line model. The models are compared with respect to flow field, power, and loads on a row of four 5MW reference turbines experiencing above-rated wind conditions. In general, the DWM models show fairly good agreement with large-eddy simulation for the time-averaged flow fields, blade forces and power, with increasing differences along the turbine row. Also when comparing fatigue loads of blade root moments, the differences between the models increase further into the row, with deviations up to 25 % of the reference case. However, while the development in blade root moment fatigue along the turbine row is predominantly driven by the energy content at the frequency corresponding to the turbine’s rotational period (1P) for the DWM models, the large-eddy simulation results suggest that the key drivers for the blade root and tower loads are the increase in meandering and energy at higher frequencies (> 1P) deeper into the turbine row. For the tower loads, the DWM models highly underestimate the fatigue for the waked turbines. From these results, we suggest priorities for future model developments so that robust model implementations can be used in wind farm design and operation.

Full-scale validation of optimal axial induction control of a row of turbines at Lillgrund wind farm

We present a full-scale validation study of an optimal power open-loop wind farm control schedule (WFCS) using a full row of turbines (Row-A) at the Lillgrund offshore wind farm as the demonstration case. The derived WFCS is conditioned on the ambient wind speed and wind direction. The WFCS’s are developed using the DNV and DTU numerical models LongSim and Fuga. Prior to implementation, the developed optimal controllers are tested by comparing LongSim results, based on Fuga optimized input, with Fuga optimized results and vice versa. These models are very different in their nature — Fuga being a linearized RANS CFD solver, and LongSim being based on a range of engineering wake models and allowing for unsteady wind farm flow modelling — but nevertheless the overall computed Row-A power gains are reasonably similar. This gives confidence to the developed WFCS’s, even though the two models predict significantly different distributions of the power gains over wind speeds and directions. Based on the numerical analysis, simplified WFCS’s for implementation in the Row-A turbines are developed and tested against the more detailed optimized controller. The test predicts only a small reduction of performance enhancement of the simplified controller compared to the detailed controller, and on this basis it is decided to use the simplified controller for the full-scale turbine implementation. The test, toggling between base control and optimal control in 50-minute successive time slots, has been running since 09-05-2022 and is still ongoing. A huge amount of data is needed to obtain robust statistics for the comparison of measured and numerical data.

Turbulent entrainment in finite-length wind farms

In this article, we present an entrainment-based model for predicting the flow and power output of finite-length wind farms. The model is an extension of the three-layer approach of Luzzatto-Fegiz & Caulfield (Phys. Rev. Fluids, vol. 3, 2018, 093802) for wind farms of infinite length, and assumes dependence of key flow quantities, such as the wind farm bulk velocity, on the streamwise distance from the farm entrance. To assist our analysis and validate the proposed model, we undertake a series of large-eddy simulations with different turbine spacing arrangements and layouts. Comparisons are also made with the top-down model with entrance effects of Meneveau (J. Turbul., vol. 13, 2012, N7) and data from the literature. The finite-length entrainment model is shown to be capable of capturing the power drop between contiguous rows of turbines as well as describing the advection and turbulent transport of kinetic energy in both the entrance and fully developed regions. The fully developed regime is approximated only deep in the wind farm, after approximately 15 rows of turbines. Our data suggest that for the cases considered in this study, the empirical coefficients that can be used to describe turbulent entrainment and transfers above the wind farm exhibit little dependence on the farm layout and may be considered constant for modelling purposes. However, the flow field within the wind farm layer can be strongly modulated by the turbine density (spacing) as well as the array layout, and to that extent it can be argued that they are both primary factors determining the wind farm power output.

Coupled loads analysis of a novel shared-mooring floating wind farm

Shared mooring lines are a method to reduce the stationkeeping cost of floating offshore wind farms; however, their impacts on floating array dynamics are not yet well understood. This article presents advancements to floating wind farm simulation capabilities that allow the dynamic modeling of shared-mooring floating wind arrays—specifically, platform coupling through shared lines and proper phasing of wave loads across the array. These capabilities are demonstrated on a 10-turbine shared-mooring array featuring two staggered rows of wind turbines. A baseline array design with individually moored turbines is used as a basis for comparison. Coupled analysis using FAST.Farm shows that the shared-mooring design has smaller fluctuations in surge when compared to the baseline design. The mean and maximum platform offsets are highest in normal operating conditions, but the maximum mooring line tensions are highest in 50-year storm conditions. Power spectral density analysis of platform surge and sway motions shows that the shared mooring configuration does not introduce any significant inter-platform resonances. The tower-base bending moment statistics and the line tension damage equivalent loads indicate no consequential differences in turbine loading or mooring fatigue life. Anchor load analysis shows that the total required anchor capacity for the shared-mooring array is decreased by approximately 25% when shared anchors are used. Dynamic simulations of the shared mooring system under line failures show that remaining line tensions stay within bounds and that offsets are significantly smaller when compared to the baseline design. Overall, the results show that shared moorings do not introduce any dynamic response concerns in this floating wind array design.

Can lidars assess wind plant blockage in simple terrain? A WRF-LES study

Wind plant blockage reduces wind velocity upstream of wind plants, reducing the power generated by turbines adjacent to the inflow, and potentially throughout the plant as well. The nature of the mechanism that amplifies blockage as well as the velocity reductions in both the induction zone and potentially deeper into the array are not well understood. Field observations can provide valuable insight into the characteristics of the induction zone and the mechanisms that amplify it. However, the relatively small velocity reductions that have been measured experimentally pose a challenge in quantifying blockage, especially in onshore environments with flow heterogeneities that may be of the same scale as the blockage effect itself. We simulate the flow around the King Plains wind plant in the relatively simple terrain of Oklahoma, the location of the American WAKE experimeNt, to evaluate wind plant blockage in this environment. Using numerical simulations, we find the largest velocity deceleration (0.64 m s−1; 8%) immediately upstream of the wind plant, and 1% velocity deficits 24 rotor diameters upstream of the first turbine row. We also use virtual measurements upstream of the wind plant to analyze the uncertainties and difficulties in measuring blockage using a scanning lidar on shore. Based on our virtual lidar study, the induction zone of land-based wind plants can be incorrectly estimated using observations if the effects of nonuniform terrain on the flow are not carefully considered. Changes in terrain elevation produce local variations in wind speed (as measured by a scanning lidar) that exceed in magnitude the deceleration within the induction zone. We refer to these local changes in wind speed as terrain effects. A methodology to differentiate between terrain effects and blockage in experimental settings is proposed and evaluated herein, highlighting the difficulties and uncertainties associated with measurement and simulation of blockage in even relatively simple onshore environments.