Wake Interaction Research Articles

Wake interactions behind individual-tower multi-rotor wind turbine configurations

Multiple rotors on single structures have long been proposed to increase wind turbine energy capture with no increase in rotor size, but at the cost of additional mechanical complexity in the yaw and tower designs. Standard turbines on their own very-closely-spaced towers avoid these disadvantages but create a significant disadvantage; for some wind directions the wake turbulence of a rotor enters the swept area of a very close downwind rotor causing low output, fatigue stress, and changes in wake recovery. Knowing how the performance of pairs of closely spaced rotors varies with wind direction is essential to design a layout that maximizes the useful directions and minimizes the losses and stress at other directions. In the current work, the high-fidelity large-eddy simulation (LES) code Exa-Wind/Nalu-Wind is used to simulate the wake interactions from paired-rotor configurations in a neutrally stratified atmospheric boundary layer to investigate performance and feasibility. Each rotor pair consists of two Vestas V27 turbines with hub-to-hub separation distances of 1.5 rotor diameters. The on-design wind direction results are consistent with previous literature. For an off-design wind direction of 26.6°, results indicate little change in power and far-wake recovery relative to the on-design case. At a direction of 45.0°, significant rotor-wake interactions produce an increase in power but also in far-wake velocity deficit and turbulence intensity. A severely off-design case is also considered.

Preface

The eighth Wake Conference, held between 20th and 22nd of June 2023, marks nearly a decade and a half of successful international conferences focused on advancements in the field of wake dynamics. This bi-annual conference, held for the past decade on the island of Gotland, Sweden seeks to aid in this by providing a venue for scientists and PhD students working in the field of wake dynamics to come together and share their research and expertise.To understand the need for this conference one only needs to look to modern wind farms. These massive energy facilities rival the production of a nuclear power plant and consist of dozens or potentially hundreds of individual wind turbines. The numbers of turbines involved make it unavoidable that they will invariably be impacted by the wakes of upstream turbines. This wake interaction results in a decreased power production, caused by the lower kinetic energy in the wind, and an increase in the turbulence intensity. This decrease in power emphases the importance of understanding the physical nature of vortices and their dynamics in the wake of a turbine when designing the layout of these immense wind farms. The clear importance of farm design, and the related interest in the field of wake and wind farm aerodynamics, can be seen in the growing number of scientific articles on the subject.Further, in order to make a substantial impact on one of the most significant challenges of our time - global climate change - the wind industry’s growth must continue. A part of making this growth possible, and to solve some of the scientific problems of doing so, will require research into the different areas of the physics of wind turbine and wind farm wakes. To this end, the conference covers the following subject areas: Wake and vortex dynamics, instabilities in trailing vortices and wakes, simulation and measurements of wakes, analytical approaches for modeling wakes, wake interaction along with other wind farm investigations.We would like to thank all those that assisted in the review process for the 55 papers which constitute the proceedings of the Wake Conference 2023 that were selected to be published. We are also thankful for the help we have received from the editorial team at IOP Publishing during the process.Visby, Sweden June 2023 The Organizers, Professor Stefan Ivanell, Uppsala University Professor Jens Nørkær Sørensen, Technical University of Denmark List of Editorial Committee are available in the pdf.

LiDAR Measurements to Investigate Farm-to-Farm Interactions at the AWAKEN Experiment

The exponential growth of wind energy and the need to exploit wind resources over areas with higher energy potential have led to the construction of neighboring wind turbines and farms with relatively small separation distances. As a result, for specific wind and atmospheric conditions, the wakes generated by an upstream wind farm may affect wind resources available for a downstream wind farm resulting in detrimental impacts on energy harvesting and structural loads for the downwind wind turbines. Distances between neighboring wind farms are typically larger than those associated with intra-wind-farm wake interactions, generating cumulative wakes whose characteristics might differ from those predicted through classical engineering wake models. These phenomena are referred to as farm-to-farm interactions. A better understanding and characterization of farm-to-farm interactions is one of the science goals tackled by the ongoing American WAKE experimeNt (AWAKEN). The site under investigation for this field campaign comprises two large wind farms in northern Oklahoma, USA, which are spaced roughly 5km apart along the prevailing South-North wind direction. To investigate possible interactions between these two wind farms, the WindFluX mobile LiDAR station has been deployed mainly to perform volumetric scans over their gap region. In this paper, preliminary results from these LiDAR volumetric scans will be discussed, specifically for a case with multiple wind turbine wakes evolving during the occurrence of a low-level jet.

Graph machine learning for predicting wake interaction losses based on SCADA data

Six wind turbine generators (WTGs) within a larger wind farm were chosen to model wake interaction losses by means of graph machine learning. 10-minute averaged measurement data values at each turbine over a period of two years were available. Based on the measured power output and ”free-stream” (undisturbed) wind conditions, determined at an upstream WTG unaffected by wakes, a data-driven free-stream power curve model was built. The cluster of WTGs was then represented by a graph data structure in the form of an adjacency matrix, where the turbines are nodes, which in turn are connected by edges. The edges are represented by weightings, indicating the strength of wake interactions between connected turbines. To learn the weightings, the predicted power outputs of the free-stream power curve model were compared to the measured power outputs and then optimised to account for these differences, which are attributed to wake interaction losses. After that a new data-driven model was trained, with free-stream wind speed, direction and yaw misalignment as features and the optimised weightings as targets. It was shown that the model was able to predict power curves that show clear patterns due to wake effects and reduced the root mean squared error, based on the measured and predicted values, by 16.29% compared to the free-stream power curve model.

A review on insects flight aerodynamics, noise sources, and flow control mechanisms

Wildlife always acts as an inspiration source for humans to help them study and mimic flight methods. Insects are one of the most important sources of biological systems’ inspiration to control flow and reduce aerodynamic noise. Insects are classified into different kinds, and most can fly by fluttering their wings. In general, insects’ flight muscles are divided into direct and indirect types that act synchronously and asynchronously with nerve impulses, respectively. These muscles help insects use a mixture of rotating, flapping, and pitching movements to achieve specific wing kinematics. Insects use various mechanisms for generating aerodynamic forces, including the Weis-Fogh or clap and fling mechanism, delayed stall due to unsteady motion (Wagner effect), wing rotation (Kramer effect), wake capture or wing–wake interaction, added mass, and absence of stall. On the other hand, the insect noises are divided into aerodynamic and structural. Insects’ aerodynamic noise is created by fluctuating forces, flow–solid interaction, shed vortex, and turbulence inflow. Meanwhile, insects’ structural noise is made by frictional and tymbal mechanisms. Their flow control methods are classified into two categories: wing shape and sub-structures. Wing shape features such as planform, chord length and location, twist, sweep, wingtip, and aspect ratio influence the flow around the insects. The sub-structures such as leading edge, trailing edge, swallowtail, and surface textures affect the flow too. A thorough understanding of insects’ fly, aerodynamic noise, and their control flow techniques will significantly help engineers to produce competitive products with better aerodynamic performance and aeroacoustic signature.

CFD modeling of vertical-axis wind turbine wake interaction

Since wind turbines placed in wind farms need to minimize their footprint on the ground, the effects of the wake must be considered. Placement optimization, turbine spacing, and direction of rotation are known to affect the performance of vertical-axis wind turbines (VAWTs). However, rigorous numerical modeling methodologies that investigate the influence of these characteristics are lacking, especially in the case of large wind turbines. The goal of this study is to analyze turbine configurations that might enhance the power production of VAWT farms using two-dimensional CFD models based on the Star CCM+ package. The novelty of this work is to analyze wind farm configurations for very large turbines. This is important because large turbines are much more performant than small turbines and have a high power coefficient. Results show that CFD simulations adequately capture the performance of wind turbines in farms with multiple VAWTs. In general, if a second rotor is spaced more than 10 turbine diameters downstream of the first rotor, the effect of the wake is less significant. Furthermore, a specific farm configuration with five VAWTs is investigated and shows a 20% increase in power output compared with the same number of turbines operating in isolation.

POD analysis of the structure of vertical axis wind turbine wakes

Proper orthogonal decomposition (POD) is used to investigate the structure and evolution of the wake behind a model vertical axis wind turbine. The analysis reveals the high level of energy contained in the coherent structures that mark the vortex shedding due to the dynamic stall on the blades and the vortex shedding downstream of the tower, and the dynamics of the wake interactions. Vortex pairing is shown to be an important feature of the wake evolution. Changing the inflow conditions from a uniform flow to a simulated atmospheric boundary layer flow reduced the relative energy contained in the coherent motions, because of the reduced level of dynamic stall. Similarly, changing the blade sweep angle reduces the modal energy associated with dynamic stall, which is desirable in terms of mitigating fatigue due to torque and force variations on the blades.

Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning

Reconstruction of unsteady vortical flow fields from limited sensor measurements is challenging. We develop machine learning methods to reconstruct flow features from sparse sensor measurements during transient vortex–airfoil wake interaction using only a limited amount of training data. The present machine learning models accurately reconstruct the aerodynamic force coefficients, pressure distributions over airfoil surface, and two-dimensional vorticity field for a variety of untrained cases. Multi-layer perceptron is used for estimating aerodynamic forces and pressure profiles over the surface, establishing a nonlinear model between the pressure sensor measurements and the output variables. A combination of multi-layer perceptron with convolutional neural network is utilized to reconstruct the vortical wake. Furthermore, the use of transfer learning and long short-term memory algorithm combined in the training models greatly improves the reconstruction of transient wakes by embedding the dynamics. The present machine-learning methods are able to estimate the transient flow features while exhibiting robustness against noisy sensor measurements. Finally, appropriate sensor locations over different time periods are assessed for accurately estimating the wakes. The present study offers insights into the dynamics of vortex–airfoil interaction and the development of data-driven flow estimation.Graphic abstract

A sharp interface immersed edge-based smoothed finite element method with extended fictitious domain scheme

This paper proposes a versatile and robust immersed edge-based smoothed finite element method with the mass conservation algorithm (IESFEM/Mass) to solve partitioned fluid–structure interaction (FSI). A gradient smoothing technique was used to solve the system governing equations, which can improve the calculated capability of the linear triangular elements in two phases. Based on the quadratic sharp interface representation of immersed boundary, an extended fictitious domain constructed by a least squares method approximately corrected the residual flux error. The compatibility for boundary conditions on moving interfaces was satisfied, thus eliminating spurious oscillations. The results from all numerical examples were consistent with those from the existing experiments and published numerical solutions. Furthermore, the present divergence-free vector field had a faster-converged rate in the flow velocity, pressure, and FSI force. Even if in distorted meshes, the proposed algorithm maintained a stable accuracy improvement. The aerodynamics of one- and two-winged flapping motions in insect flight has been investigated through the IESFEM/Mass. It can be seen that the wing–wake interaction mechanism is a vital factor affecting the lift. The applicability of the present method in the biological FSI scenario was also well-demonstrated.

Numerical investigation of the aerodynamic and wake characteristics of a floating twin-rotor wind turbine under surge motion

To elevate the power capacity of floating wind turbines and cut costs, development towards large-scale systems is underway. However, this requires solving the problem of fatigue loading on large, flexible blades, as well as corresponding improvements to production and installation technology. As an alternative to larger turbines, twin-rotor floating wind turbines have been proposed. The aerodynamic and wake interference characteristics of this twin-rotor are not yet well understood. In this study, we have investigated the power and wake characteristics of a twin-rotor floating wind turbine based on unsteady Reynolds-averaged Navier-Stokes turbulence model for a comprehensive analysis. Specifically, the power and wake recovery of a twin-rotor floating horizontal-axis wind turbine (FHAWT) under surge motion are compared in detail with those of a single floating wind turbine and bottom-fixed case, while both the effects of the three wind directions (β = 0°, 45°, 90°) and spacing ratios (S/D = 1.2, 1.375, 1.5) are considered. The results suggest that the mean power of the twin-rotor is enhanced by a maximum of 13% under surge motion compared to the single rotor, the small spacing ratio promotes the power gain. Surge motion plays a significant role in wake interaction between the twin-rotor and facilitates wake recovery. As compared to the baseline case, both power and wake recovery performance of the twin-rotor are boosted at β = 45°, with an increase in power of 12.7% as opposed to the single rotor. The modified Jensen wake model is proposed for the far-wake region and a modified Gaussian wake model is also suggested for different cases. This study can provide affiliated guidance for the application of the twin-rotor floating wind turbine.

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.

Uncertainty quantification analysis for simulation of wakes in wind-farms using a stochastic RANS solver, compared with a deep learning approach

Quantification of uncertainties in Reynolds–Averaged Navier–Stokes (RANS) simulations has gained a considerable interest in turbulence modeling. We present two different approaches for the quantification and propagation of model-form and operational uncertainties in context of wind turbine RANS simulations. The first approach is based on a stochastic RANS solver in OpenFOAM using intrusive polynomial chaos method (Parekh and Verstappen, 2023). Here the uncertainties are propagated through a single (large) simulation for the coupled coefficients of the polynomial expansion. The second approach is a surrogate based uncertainty quantification (SBUQ) method. The surrogate model comprises of a 3D U-Net neural network (trained over a single wind turbine) combined with a wake superposition model in order to the prediction of flow field in an array of wind turbines. The above-mentioned approaches are applied for uncertainty quantification analysis in RANS simulations of two turbulent engineering flow problems — (i) a wake past a single wind turbine, and (ii) wake interactions and power losses in an array of wind turbines. The results show that the uncertain RANS solutions from the two approaches are able to reasonably capture the reference high-fidelity solution. We also discuss comparisons between the two approaches including computational cost, applicability, generality etc. The two methods can be further explored and applied to engineering applications where it is critical to compute the turbulent RANS solution in presence of various sources of uncertainties.

Actuator line model using simplified force calculation methods

Abstract. To simulate transient wind turbine wake interaction problems using limited wind turbine data, two new variants of the actuator line technique are proposed in which the rotor blade forces are computed locally using generic load data. The proposed models, which are extensions of the actuator disk force models proposed by Navarro Diaz et al. (2019a) and Sørensen et al. (2020), only demand thrust and power coefficients and the tip speed ratio as input parameters. In the paper the analogy between the actuator disk model (ADM) and the actuator line model (ALM) is shown, and from this a simple methodology to implement local forces in the ALM without the need for knowledge of blade geometry and local airfoil data is derived. Two simplified variants of ALMs are proposed, an analytical one based on Sørensen et al. (2020) and a numerical one based on Navarro Diaz et al. (2019a). The proposed models are compared to the ADM using analogous data, as well as to the classical ALM based on blade element theory, which provides more detailed force distributions by using airfoil data. To evaluate the local force calculation, the analysis of a partial-wake interaction case between two wind turbines is carried out for a uniform laminar inflow and for a turbulent neutral atmospheric boundary layer inflow. The computations are performed using the large eddy simulation facility in Open Source Field Operation and Manipulation (OpenFOAM), including Simulator for Wind Farm Applications (SOWFA) libraries and the reference National Renewable Energy Laboratory (NREL) 5 MW wind turbine as the test case. In the single-turbine case, computed normal and tangential force distributions along the blade showed a very good agreement between the employed models. The two new ALMs exhibited the same distribution as the ALM based on geometry and airfoil data, with minor differences due to the particular tip correction needed in the ALM. For the challenging partially impacted wake case, both the analytical and the numerical approaches manage to correctly capture the force distribution at the different regions of the rotor area, with, however, a consistent overestimation of the normal force outside the wake and an underestimation inside the wake. The analytical approach shows a slightly better performance in wake impact cases compared to the numerical one. As expected, the ALMs gave a much more detailed prediction of the higher-frequency power output fluctuations than the ADM. These promising findings open the possibility to simulate commercial wind farms in transient inflows using the ALM without having to get access to actual wind turbine and airfoil data, which in most cases are restricted due to confidentiality.

Research of the array spacing effect on wake interaction of tidal stream turbines

Understanding the wake interactions among turbines is important for developing efficient tidal farm configurations. The present study compares the power and wake generated by a 2-row by 1-column array separated at three lateral spacings with those of a single tidal stream turbine. According to the results, power fluctuations increase with the tip speed ratio, with mean values corresponding to the inverse-u shape prediction of the blade element methods. For a single turbine, the cloud diagram of the lateral velocity deficit follows a half-elliptical contour with its center at the hub and initial covertices at the tip. The twin turbines largely exhibit these symmetric transversal distributions; however, they are doubled, with wake fusion occurring at the mid-width and closer with turbines approximation. The superposition of Gaussian-type velocity distributions matches the experimental bimodal functions with horizontally decreasing heights only after x > 6D. Vertically, the TIx in all conditions displays two strong areas, just above and below the turbine, owing to the interaction of the tip with tower vortices and the free and bed surface. In the two-unit arrangement, the blockage effect and the velocity of the mid-passage flow increase with the lateral separation of the turbines. This has a direct effect on wake combination, width, and recovery.

Modelling the induction, thrust and power of a yaw-misaligned actuator disk

Collective wind farm flow control, where wind turbines are operated in an individually suboptimal strategy to benefit the aggregate farm, has demonstrated potential to reduce wake interactions and increase farm energy production. However, existing wake models used for flow control often estimate the thrust and power of yaw-misaligned turbines using simplified empirical expressions that require expensive calibration data and do not extrapolate accurately between turbine models. The thrust, wake velocity deficit, wake deflection and power of a yawed wind turbine depend on its induced velocity. Here, we extend classical one-dimensional momentum theory to model the induction of a yaw-misaligned actuator disk. Analytical expressions for the induction, thrust, initial wake velocities and power are developed as a function of the yaw angle ( $\gamma$ ) and thrust coefficient. The analytical model is validated against large eddy simulations of a yawed actuator disk. Because the induction depends on the yaw and thrust coefficient, the power generated by a yawed actuator disk will always be greater than a $\cos ^3(\gamma )$ model suggests. The power lost due to yaw misalignment depends on the thrust coefficient. An analytical expression for the thrust coefficient that maximizes power, depending on the yaw, is developed and validated. Finally, using the developed induction model as an initial condition for a turbulent far-wake model, we demonstrate how combining wake steering and thrust (induction) control can increase array power, compared to either independent steering or induction control, due to the joint dependence of the induction on the thrust coefficient and yaw angle.

Accounting for Circumferential Flow Nonuniformity in a Multistage Axial Compressor

AbstractThe flow field in a compressor is circumferentially nonuniform due to geometric imperfections, inlet flow nonuniformities, and blade row interactions. Therefore, the flow field, as represented by measurements from discrete stationary instrumentation, can be skewed and contributes to uncertainties in both calculated one-dimensional performance parameters and aerodynamic forcing functions needed for aeromechanics analyses. Considering this challenge, this article documents a continued effort to account for compressor circumferential flow nonuniformities based on discrete, undersampled measurements. First, the total pressure field downstream of the first two stators in a three-stage axial compressor was measured across half of the annulus. The circumferential nonuniformities in the stator exit flow, including vane wake variability, were characterized. In addition, the influence of wake variation on stage performance calculations and aerodynamic forcing functions were investigated. In the present study for the compressor with an approximate pressure ratio of 1.3 at the design point, the circumferential nonuniformity in total pressure yields an approximate 2.4-point variation in isentropic efficiency and 54% variation in spectral magnitudes of the fundamental forcing frequency for the embedded stage. Furthermore, the stator exit circumferential flow nonuniformity is accounted for by reconstructing the full-annulus flow using a novel multiwavelet approximation method. Strong agreement was achieved between experiment and the reconstructed total pressure field from a small segment of measurements representing 20% coverage of the annulus. The analysis shows the wake–wake interactions from the upstream vane rows dominate the circumferentially nonuniform distributions in the total pressure field downstream of stators. The features associated with wake–wake interactions accounting for passage-to-passage variations are resolved in the reconstructed total pressure profile, yielding representative mean flow properties and aerodynamic forcing functions.

Numerical investigation of h-Darrieus wind turbine aerodynamics at different tip speed ratios

PurposeThe purpose of the paper is to predict the aerodynamic performance of a complete scale model H-Darrieus vertical axis wind turbine (VAWT) with end plates at different operating conditions. This paper aims at understanding the flow physics around a model VAWT for three different tip speed ratios corresponding to three different flow regimes.Design/methodology/approachThis study achieves a first three-dimensional hybrid lattice Boltzmann method/very large eddy simulation (LBM-VLES) model for a complete scaled model VAWT with end plates and mast using the solver PowerFLOW. The power curve predicted from the numerical simulations is compared with the experimental data collected at Erlangen University. This study highlights the complexity of the turbulent flow features that are seen at three different operational regimes of the turbine using instantaneous flow structures, mean velocity, pressure iso-contours, blade loading and skin friction plots.FindingsThe power curve predicted using the LBM-VLES approach and setup provides a good overall match with the experimental power curve, with the peak and drop after the operational point being captured. Variable turbulent flow structures are seen over the azimuthal revolution that depends on the tip speed ratio (TSR). Significant dynamic stall structures are seen in the upwind phase and at the end of the downwind phase of rotation in the deep stall regime. Strong blade wake interactions and turbulent flow structures are seen inside the rotor at higher TSRs.Research limitations/implicationsThe computational cost and time for such high-fidelity simulations using the LBM-VLES remains expensive. Each simulation requires around a week using supercomputing facilities. Further studies need to be performed to improve analytical VAWT models using inputs/calibration from high fidelity simulation databases. As a future work, the impact of turbulent and nonuniform inflow conditions that are more representative of a typical urban environment also needs to be investigated.Practical implicationsThe LBM methodology is shown to be a reliable approach for VAWT power prediction. Dynamic stall and blade wake interactions reduce the aerodynamic performance of a VAWT. An ideal operation close to the peak of the power curve should be favored based on the local wind resource, as this point exhibits a smoother variation of forces improving operational performance. The 3D flow features also exhibit a significant wake asymmetry that could impact the optimal layout of VAWT clusters to increase their power density. The present work also highlights the importance of 3D simulations of the complete model including the support structures such as end plates and mast.Social implicationsAccurate predictions of power performance for Darrieus VAWTs could help in better siting of wind turbines thus improving return of investment and reducing levelized cost of energy. It could promote the development of onsite electricity generation, especially for industrial sites/urban areas and renew interest for VAWT wind farms.Originality/valueA first high-fidelity simulation of a complete VAWT with end plates and supporting structures has been performed using the LBM approach and compared with experimental data. The 3D flow physics has been analyzed at different operating regimes of the turbine. These physical insights and prediction capabilities of this approach could be useful for commercial VAWT manufacturers.

Bio-inspired wake tracking and phase matching of two diagonal flapping swimmers

Wake interaction provides hydrodynamic gain and flow-aided navigation in fish schools. The lateral spacing Ly and phase angle Φ relative to upstream wake are two important states for downstream swimmers. In this paper, the lateral wake tracking and phase matching of two diagonal flapping swimmers are studied through experiments. Bio-inspired differential pressure (DP) sensing on the downstream swimmer is adopted to capture the wake interaction features. Two DP sensing strategies, the symmetrical differential pressure (SDP) and leading edge differential pressure (LDP), are employed to capture the wake interaction features. SDP measures the pressure difference of two symmetrical ports on the two sides of the downstream swimmer, and LDP measures the pressure difference of leading edge port against the two side ports. One-dimensional convolutional neural networks (1D CNN) with a parallel structure are trained to decode wake interaction states (Ly and Φ) based on DP signals. The 1D CNN model is trained and tested offline and is used to estimate the wake interaction states online. Three demonstrations of online lateral wake tracking and phase matching control are carried out. Compared with SDP, LDP predicts Ly and Φ more accurately. It is found that the downstream wakes are more compact after control, which is consistent with high propulsive efficiency mode.

Inter-farm cluster interaction of the operational and planned offshore wind power base

The inter-farm cluster interaction is one of the most difficult aspects of building and planning ten million-kilowatt offshore wind power bases. This study is an attempt to assess the wake and production interactions in the substantial offshore wind farms in Jiangsu Province of China under operational (2020) and planned (2025) scenarios using the updated Weather Research and Forecasting model coupled with the 3D Price-Weller-Pinkel ocean model and the wind farm parameterization model. The results indicate a larger wind deficit of −0.73 ∼ −1.30 m/s in the planned levels of 2025 than that of −0.52 m/s in 2020, with the wake meandering enlarged from 30 km to 90–100 km. Moreover, the newly added power capacity path in the future wind farm development has an impact on power production. The capacity factor of the wind power base will decrease if the average number of turbines in a wind farm is increased or the turbine capacity is increased. It is interesting to note that the planned development mode of positive turbine capacity with conservative farm capacity has relatively low inter-farm cluster wake interference and can be recommended as a priority path for the offshore wind power industry to achieve carbon neutrality and cleaner production.

Quantification of wake interaction effects on multi-rotor configurations in forward flight

This study aims to illustrate the correlations of performance and wake dynamics in multi-rotor configurations and explain the induced circulation (IC), the quantification factor for wake interactions. A series of parametric studies are performed considering cross- and plus-type quadrotors to analyze the aerodynamic and aeroacoustic characteristics. The performance and tip vortex structure of each rotor are different according to each interaction relation. Total circulation is classified into the rotor-, wake-, and motion-induced components using the IC concept. Overall, the wake- and motion-IC primarily influence the rotor performance; however, the interaction effect is negligible as the advance ratio and incidence angle increase. In this study, the complex wake dynamics of multi-rotors are classified in terms of each interaction component. The physical relations of performance, wake dynamics, and IC can be fundamental steps toward wake interaction models for multi-rotor configurations.