bluuebunny/crossref_metadata_2025_split huggingface dataset:
The impacts of climate change on offshore wind park projects: from changes in resource to project profitability
Authors: Olivares-Cabello Claudia , López Ana , Gebraad Pieter , Lizcano Gil , Edvaldsson Edvald 
Published: June 2024

Abstract
Offshore wind power generation is projected to increase 15-fold over the next two decades. This generation is, however, weather dependent. The compounding of the anthropogenic climate change signal with high spatial and temporal wind variability can lead to large uncertainties in the projected impacts of climate change on wind resources, and further down the line in the economics of a project. In this study, we showcase a methodology to analyze the impact of climate change on the economic indicators of an offshore wind farm project. Projections of changes in wind resources are obtained using an ensemble of statistically downscaled Coupled Model Intercomparison Project 6 (CMIP6) for different emission scenarios and time periods. A series of assumptions about the features of a representative wind farm and its key economic parameters are made to compute two economic indicators: the Internal Rate of Return (IRR) and the Levelized Cost of Energy (LCOE). The IRR is an estimate of the profitability of potential investments and the LCOE is the cost over the lifetime of an investment compared to the expected energy production. We find that the effect of changes of resource on IRR and LCOE depends on the region, emissions scenario, and projection period. In general, and conditioned on the assumptions underlying this study, impacts on IRR are more significant -i.e., with differences between -0.5% and -1% in Brazil- than on LCOE. We conclude that it is especially important to take into account climate change when making investment decisions based on a project’s expected profitability.
The Influence of Topographical Variations on Wind Turbine Wake Characteristics Using LES
Authors: Patel Jay A. , Maity Ankita , Ghaisas Niranjan S. 
Published: June 2024

Abstract
A detailed understanding of the characteristics of wind-turbine wakes over complex terrain is crucial for designing efficient wind farms. Complexity of the terrain can be due to changes in elevation (due to, e.g., hills/valley/cliffs), and changes in the surface roughness (due to land types, e.g. fallow/cultivated/forested). We employ large-eddy simulations to study the evolution of a wind-turbine wake sited at different locations on a gradual two-dimensional hill with and without an abrupt transition in its surface aerodynamic roughness. A turbine placed at the foot of the hill, midway between the foot and the peak, and at the hill peak behave very differently when compared against each other. For a homogeneously rough surface, the profile of the velocity deficit normalized by the inflow velocity at the hill peak height shows that the wake recovers fastest for a turbine placed at the midway location. For a heterogeneously rough surface, however, the wake recovery for a turbine at the midway location is not significantly faster than that for a turbine at the hill-foot. The surface heterogeneity accelerates the flow downstream, which affects the wake recovery, due to which the deficits over heterogeneously rough surfaces are typically larger than for homogeneous surfaces. The turbulence intensity as well as the turbulence intensity added due to the presence of the turbine are affected by the position of the turbine as well as the surface roughness heterogeneity. The observed differences between the rates of wake recovery are only partly explained by the pressure gradient of the background flow over the hill in the absence of the wind turbine. The pressure drop over a region from one diameter upstream to five diameters downstream of the turbine explains the wake recovery for the turbines sited on homogeneously rough surface but not on a heterogeneously rough surface.
Wake steering experiments in onshore and offshore wind farms
Authors: Pena Martinez Juan Jose , Coussy Juliette 
Published: June 2024

Abstract
Over the past years, wake steering has been the subject of much research in the wind industry. Multiple academic publications have shown results from high fidelity simulations (such as SOWFA), engineering simulations (such as FLORIS) or from field tests. Most of this research is focused on the power increase as it is the main goal of wake steering. However, other key topics need to be covered upfront. Wake Adapt is the product developed by Siemens Gamesa Renewable Energy that applies wake steering at commercial level. As presented in this paper, some prerequisites are needed before Wake Adapt installation to ensure the wind turbines are calibrated to apply the optimum yaw offsets. The increase in energy is not without costs: yaw activity is increasing which impacts the maintenance costs. Wake Adapt loads must be assessed, and it has been shown that Wake Adapt loads cases are covered by design loads of the turbines. On the modelling side, the Wake Adapt yaw offsets are generated using some engineering models well known by the scientific community. Some robustness is added to the Yaw Offset Lookup Table to ensure that the optimum yaw offsets calculated in steady-state conditions are still optimum with unsteady conditions. Finally, in order to measure the Wake Adapt benefit, a Swap Test can be conducted where wind farm power output with and without Wake Adapt are compared following a certain procedure, especially if the predicted and measured power benefit needs to be compared. The last section of this paper shows some first tests in an onshore wind farm, as well as the results of a full Swap Test in an offshore wind farm, where the measured gain and the predicted gain shows very similar values.
Wind turbine power extraction under partial wake operations, a CFD study using ALM.
Authors: Campaña-Alonso Guillén , Ferrer Esteban , Méndez-López Beatriz 
Published: June 2024

Abstract
This study focuses on the intricate interplay between large wind turbines and their wake effects on neighboring turbines. We specifically investigate the influence in power production and aerodynamic loads when a turbine operates under the influence of upstream turbine wakes. The analysis has been performed by means of Computational Fluid Dynamic simulations using OpenFOAM, while wind turbines are modelled with an Actuator Line Model approach. Two large IEA 15 MW reference wind turbines, with one turbine partially affected by the other’s wake, are analyzed. The research assesses the influence of turbine spacing on aerodynamic torque and blade loads. The study shows that the CP
may suffer oscillations 2 orders of magnitude greater than the ones observed at the reference cases. For the most affected case, the torque experiences oscillations of a 5.46 % w.r.t. to the averaged torque over the last ten revolutions. The influence of operating in partial wake conditions is specially relevant on the blade root loads that are found to suffer an increase of ∼ 20 % and a decrease of ∼ 30 % over one revolution as the blade is affected by the highly sheared flow resulting from the upstream wake.
Exploring the impact of different inflow conditions on wind turbine wakes using Large-Eddy Simulations
Authors: Parinam Anand , Benard Pierre , Von Terzi Dominic , Viré Axelle 
Published: June 2024

Abstract
The ever-growing demand for renewable energy, driven by cost-effectiveness and minimal ecological impacts, has resulted in the deployment of larger wind turbines with rotor diameters surpassing 200 m. This underscores the importance of a thorough understanding of flow dynamics to optimize operational efficiency in diverse atmospheric inflow scenarios. Understanding the intricate impact of atmospheric conditions, including wind shear and turbulence, on wind turbine wakes is crucial for optimizing wind farm layouts and performance, influencing wake evolution, turbine loads, and power output. This research focuses on bridging the gap between idealized inflow scenarios and real-world atmospheric inflow conditions by systematically integrating linear shear, turbulence and the logarithmic wind shear profile into the uniform inflow conditions and analyzing the wake behind the IEA-15 MW wind turbine. To specifically examine inflow effects, a constant hub height wind speed was maintained through a velocity controller. The study focuses on analyzing the wake’s flow field and providing insights into its recovery process. It was found that turbulence plays a critical role in a faster wake recovery as well as increasing the power production of the turbine for sheared inflows and the wind speed selected.
CFD wind farm evaluation in complex terrain under free and wake induced flow conditions
Authors: Bretos David , Campaña-Alonso Guillén , Méndez-López Beatriz , Cantero-Nouqueret Elena 
Published: June 2024

Abstract
Massive deployment of wind energy is critical to achieving the renewable energy production targets. This requires the development and improvement of models and tools for the optimal exploitation of high altitude and complex terrain sites for wind energy installations. Predicting the wind resource assessment of these sites is very challenging, as is predicting the interaction of wind farms in complex terrain with neighbouring installations, which is necessary to maximise the efficiency of wind energy. To address these challenges, the use of high-fidelity Computational Fluid Dynamics (CFD) models is recommended. In this study, the wind resource at the complex terrain site of the CENER experimental wind farm (Alaiz) is evaluated using steady-state RANS CFD simulations performed with OpenFOAM v2212, taking into account the effects of terrain topography and vegetation. Furthermore, a virtual wind farm located at Alaiz is modelled with the Actuator Disk (AD) method to analyse the effect of topography on the the wake evolution.
Comparison of horizontal wind speed and direction measurements from dual-Doppler radar and profiling lidars
Authors: Vöhringer A L , Gottschall J , Hirth B D , Hung L-Y , Lundquist J K , Schneemann J , Schroeder J L , Theuer F , Kühn M 
Published: June 2024

AbstractDual-Doppler radar is a relatively new technology in the wind energy community and thus not yet studied vastly. This paper aims to compare horizontal wind speed and direction data retrieved from dual-Doppler radar and profiling lidars within the American WAKE experimeNt (AWAKEN) to investigate the influence of measurement height, wind direction and speed on the comparison. The 10-min averaged data show a better agreement of the measurements for higher altitudes, especially at faster wind speeds. For the wind direction, two sectors of larger differences in the measurements were detected: around 270° transient winds occur with a higher frequency than in other sectors. To explain the different measurement values in the wind direction sector around 90°, further studies, e.g. on the influence of atmospheric stability, are necessary.
Exploring the Power & Loads Paradigm: Tocha Farm Case Study
Authors: Lucas Frutuoso T R , Marante Pereira M , Santos Pereira R B , Madeira J F A , Cintra João 
Published: June 2024

Abstract
Wind energy plays a vital role in the European Union’s decarbonization and electrification efforts. The present study addresses wind farm control strategies exploring the design space of optimal solutions and understanding how these solutions vary when different objectives and meteorological scenarios are considered. The goal is to bridge the gap in existing literature by avoiding the simplification of the multi-objective optimization problem into single-objective using the weights method. This method was not chosen due to its various limitations, particularly its inability to deal with non-convex Pareto fronts. Instead, in this work, a multiobjective optimization method is adopted that overcomes these constraints and offers a set of solutions (the Pareto front) where the decision maker selects the most suitable solution. The methodology involves four main steps: flow calculation using FLORIS, aerodynamic load calculation with CCBlade, conversion of loads into damage equivalent moments (DEM), and multi-objective optimization using a direct multi-search (DMS) algorithm. By exploring various objectives, such as power and fatigue loads at different components, and different wind turbines, this study aims to explore the design space and quantify the potential improvement in terms of the defined cost-functions. The study utilizes data from Tocha Wind Farm in Portugal, that comprises five VESTAS wind turbines. Strain gauges, accelerometers, and meteorological measurements are used for data collection. Simulations under ‘greedy control’, where a turbine is affected by a wake, are compared with campaign data, considering a fixed wind speed and varying turbulence intensity. The optimization reveals Pareto-optimal strategies and the respective optimal yaw angles. Considering a wind velocity of 8 m.s
−1 and a wind direction of 308°, power may improve up to 4.6%, and the blade root DEM and tower base DEM up to 5.7% and 84%, respectively. The study highlights the impact of considering multiple objectives and supports the selection of wind farm control strategies to effectively manage critical structural components, thereby extending the lifespan of the wind farm.
Investigation of far-wake models coupled with yaw-induction control for power optimization
Authors: Heck Kirby S. , Liew Jaime , Howland Michael F. 
Published: June 2024

Abstract
Combined wake steering and induction control is a promising strategy for increasing collective wind farm power production over standard turbine control. However, computationally efficient models for predicting optimal control set points still need to be tested against high-fidelity simulations, particularly in regimes of high rotor thrust. In this study, large eddy simulations (LES) are used to investigate a two-turbine array using actuator disk modeling in conventionally neutral atmospheric conditions. The thrust coefficient and yaw-misalignment angle are independently prescribed to the upwind turbine in each simulation while downwind turbine operation is fixed. Analyzing the LES velocity fields shows that near-wake length decreases and wake recovery rate increases with increasing thrust. We model the wake behavior with a physics-based near-wake and induction model coupled with a Gaussian far-wake model. The near-wake model predicts the turbine thrust and power depending on the wake steering and induction control set point. The initial wake velocities predicted by the near-wake model are validated against LES data, and a calibrated far-wake model is used to estimate the power maximizing control set point and power gain. Both model-predicted and LES optimal set points exhibit increases in yaw angle and thrust coefficient for the leading turbine relative to standard control. The model-optimal set point predicts a power gain of 18.1% while the LES optimal set point results in a power gain of 20.7%. In contrast, using a tuned cosine model for the power-yaw relationship results in a set point with a lower magnitude of yaw, a thrust coefficient lower than in standard control, and predicts a power gain of 13.7%. Using the physics-based, model-predicted set points in LES results in a power within 1.5% of optimal, showing potential for joint yaw-induction control as a method for predictably increasing wind farm power output.
A simple RANS closure for wind-farms under neutral atmospheric conditions: Preliminary findings
Authors: Jigjid Kherlen , Dwight Richard , Allaerts Dries , Steiner Julia 
Published: June 2024

Abstract
Accurately predicting wind turbine wake effects is essential for optimizing wind-farm performance and minimizing maintenance costs. This study explores the applicability of the Sparse Regression of Turbulent Stress Anisotropy (SpaRTA) framework to develop a simple yet robust Reynolds-averaged Navier-Stokes (RANS) model for wake prediction in wind energy contexts. The framework introduces two correction terms into two-equation models, with k − ε model being utilized in the current study. One correction term resembles the residual of the Turbulent Kinetic Energy (TKE) equation, and the other corrects the deviatoric part of the Reynolds Stress Tensor (RST). The terms are calculated from high-fidelity measurement or simulation data, and symbolic regression is used to determine the model for these terms.
In this study, Large Eddy Simulation (LES) data from a single turbine is used as the training dataset, and a sample pre-selection process is employed to discover a correction model efficiently. The derived model incorporates two terms based on Pope’s basis tensors and their invariants. The expression of the obtained model shows that it functions as a modification to the constant Cµ
in the k − ε model. The model is evaluated by comparing its predicted velocity and TKE fields with the LES data used for the training. The model showed satisfactory performance in predicting both fields. Additionally, its generalizability is evaluated by testing it against a more complex six-turbine unseen case. The results indicate that the model effectively captures the velocity field and power output, but it tends to overpredict TKE, especially in the wake region.
Sensitivity of the Prediction of Wind Turbine Wakes to the Sub-Grid Scale Model
Authors: Ghobrial Mina , Stallard Tim , Schultz David M. , Ouro Pablo 
Published: June 2024

Abstract
In a large-eddy simulation (LES) approach, the sub-grid scale (SGS) model accounts for the contribution of eddies and their fluxes whose length scales are smaller than the filter width. In wind turbine and farm simulations, different SGS models have been adopted, but their impact on turbine performance and wake prediction remains unknown for non-neutrally stable atmospheric boundary layers. Here, large-eddy simulations of an NREL–5MW wind turbine in stable atmospheric conditions are performed with six SGS models: standard Smagorinsky, Lagrangian-Averaged Scale-Dependent Dynamic (LASDD), Wall-Adapting Local Eddy-Viscosity, Turbulent Kinetic Energy, Stability Dependent Smagorinsky, and Anisotropic Minimum-Dissipation (AMD) models. The resolved flow field and turbine loading have shown limited sensitivity to the SGS model with some deviations from the LASDD in wind speed and turbulence intensity at the turbine elevation. This limited sensitivity is owed to the adopted high-resolution grid necessary to provide an acceptable resolution for the actuator-line method. Regarding the computational costs, the LASDD model has the highest compute overhead to the LES compared to the other five SGS models. The AMD model is simple to implement and provides three-dimensional variation of the SGS eddy-viscosity without any parameter tuning, thus it has the highest potential to be used in LES of wind turbines and farms operating in stable conditions.
Turbulence coherence in wind farms: The role of turbines
Authors: Liu Yang , Stevens Richard J. A. M. 
Published: June 2024

Abstract
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
.
Assessment of synthetic turbulence of stably stratified atmospheric boundary layers for LES
Authors: Olivares-Espinosa Hugo , Arnqvist Johan 
Published: June 2024

Abstract
This work studies the evolution of turbulence in a Stable Boundary Layer (SBL) in Large-Eddy Simulations (LES) when a synthetic turbulence field of velocity and temperature is used as initial condition and as well as precursor. Following common approaches found in the literature, initial fields of different characteristics are created and their evolution in the LES is evaluated in order to assess the ability of the synthetically generated turbulence to represent a snapshot of SBL turbulence. The study is carried out with two modelling cases, a forested site as well as the GABLS benchmark [4]. In both cases, the usage of a turbulent SBL as an initial condition proved advantageous to reach a faster convergence presenting an opportunity for computational savings. Yet, the very different turbulence conditions of the modelling setups produce varying results with regard to the type of velocity and temperature fluctuations. Likewise, the usage of a precursor was shown able to represent the flow in a successor LES although with varying reliability, dependent on the background turbulence and the stratification level, that suppresses velocity fluctuations and could lead to the misrepresentation of turbulence level.
Estimating Uncertainties from Dual-Doppler Radar Measurements of Onshore Wind Plants Using LES
Authors: Brown K. , Cheung L. , Develder N. , Herges T. , Hsieh A. , Blaylock M. , Yalla G. , Knaus R. , Maniaci D. , Hirth B. 
Published: June 2024

Abstract
Towards the ongoing work of improving the capability of flow modeling within and around wind plants, an onshore model validation benchmark campaign is underway based on a field experiment involving multiple wind plants in Oklahoma, U.S.A. Dual-Doppler radar is being leveraged to provide flowfield information for the benchmarking owing to the unparalleled capability of such radar to capture minute-by-minute horizontal wind fields over a scale of tens of kilometers. However, dual-Doppler radar exhibits sampling artifacts that must be considered during model validation, and these are due to probe-volume averaging, coarse azimuthal/elevational resolution, non-ideal stereo angles, and coarse temporal sampling. Such sources of error in radar-reconstructed flowfields can be quantified using virtual radar sampling in the high-fidelity simulation environment (i.e., large-eddy simulation (LES)) where the true velocity field is known with confidence, and this is the uncertainty quantification approach adopted in this article. We leverage a virtual radar tool designed to replicate the specific sampling strategy of the X-band dual-Doppler instrument installed in the field campaign. This tool is featured in LES of an expansive 100 km by 100 km region of Oklahoma including hundreds of wind turbines modeled as actuator disks. In agreement with the sampling principles of radar, the results show that large-scale flow structures are qualitatively well-resolved by the instrument, though more simulation time and analysis are needed to determine the accuracy of the radar’s integral lengthscale estimates. At the turbine scale, the radar struggles to capture all of the features of the turbine wakes. The process of probe-volume averaging, as well as the subsequent interpolation to a Cartesian grid, biases the reconstruction of the peak near-wake deficit by an average of 2 m/s, or around 20% of the freestream velocity. Errors in this quantity of interest, as well as one characterizing the magnitude of the free-flow wind speed around the wakes, are found to be sensitive to the radar beam-crossing angle and beam range.