bluuebunny/crossref_metadata_2025_split huggingface dataset:
A two-step topology and layout wind farm optimization approach
Authors: Pollini Nicolò 
Published: June 2024

Abstract
A novel two-step approach is presented to optimize the placement of wind turbines within a wind farm. The goal is to maximize the annual energy production, while the topology and layout of the wind turbines are optimized in sequence with a two-step procedure using gradient-based algorithms. In particular, in the first step the topology of the wind farm is optimized. A grid of potential wind turbine locations is defined, and continuous density variables ranging from zero to one are assigned to each location. The variables indicate the presence or absence of a wind turbine. Throughout the topology optimization process, we penalize the intermediate values of these variables, implicitly pushing the algorithm towards crisp 0-1 final layouts. Constraints are imposed on the minimum and maximum number of wind turbines and the spacing between them. Subsequently, in the second step, we refine the wind farm layout by optimizing the coordinates (x, y) of the wind turbines, which are initialized based on the optimal topology obtained in the first step. Numerical findings demonstrate the capability of the proposed approach to identify non-intuitive optimized wind farm layouts, with reasonable computational resources and time.
Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US
Authors: Phillips Caleb , Duplyakin Dmitry , Sheridan Lindsay , Ruzekowicz Jenna , Nelson Matthew , Fytanidis Dimitrios , Linn Rod , Kotamarthi Rao , Tinnesand Heidi 
Published: June 2024

Abstract
Current wind resources within the United States (US) indicate a potential to profitably install nearly 1,400 gigawatts of distributed wind (DW) capacity. This amount is equivalent to over half of the United States’ current energy demand from electricity, making it enough to power millions of homes and businesses and replace countless fossil fuel-based generating plants. Despite the potential growth of DW in the US, deployments are presently hindered by a lack of confidence in resource estimation methods. One potential challenge is that smaller-scale turbines, with hub heights of 40 meters or less, are disproportionately impacted by obstacles such as buildings and vegetation. These obstacles may produce complex wake effects, best modeled with high-fidelity complex fluid dynamics (CFD) models that are too computationally expensive to use for routine siting and resource assessment. Thus, installers today make use of heuristics and simple equations to approximate the impact of obstacles while also leveraging long-term resource data from commercial or publicly available atmospheric models. This study evaluates these historical and commonly used methods alongside new lower-order obstacle models produced from CFD simulations and measurement-based bias correction. The preliminary results from this study show the importance of taking care in the choice and application of mesoscale atmospheric models and the significant value of bias correction using measurements from nearby meteorological towers. Detailed obstacle modeling provides only modest additional gains in performance and, in some cases, can add error, especially at sites where turbines have already been located to avoid obvious impact from upwind obstacles. These findings reinforce the importance of collecting in situ measurements and suggest that obstacle models may be better applied in practice to automated or computer-aided siting, rather than in economic wind resource assessments.
Uncertainty Quantification for an Automatised RANS-Based Wind Resource Assessment Approach
Authors: Remmler Sebastian , Langner Judith , Letzel Marcus O. 
Published: June 2024

Abstract
In order to assess the model uncertainty of our RANS-based tool chain for wind-resource assessment “E-Wind”, we have simulated a large number of real wind farm projects with multiple measurement locations. Using one measurement location as reference and all others as target position, we systematically evaluated and analysed the horizontal extrapolation error for the annual mean wind speed introduced by the RANS method. Similar to previous studies, we found the model error to be strongly correlated with the simulated speed-up factor between two locations. We derived a method for site-specific prediction of the model uncertainty based on terrain complexity, simulated speed-up and distance between the measurement location and the turbine.
Assessing canopy models across forest edges on flat terrain
Authors: Monteiro J R , Batista V T P , Palma J M L M 
Published: June 2024

Abstract
Computational modelling of atmospheric flows in forested areas is crucial for wind resource assessment, wind farm design and turbine siting, enabling the identification of low wind speed and high turbulence regions. However, when setting up a RANS simulation in a forested area, the question arises: what is the most appropriate canopy model? In this study, employing VENTOS-2D (RANS with k-ε turbulence model), we identified the most suitable of seven canopy models to simulate the mean flow characteristics (wind speed) and second-order flow statistics (turbulent kinetic energy and shear stress) of the flow over a forest edge at the Island of Falster, by comparing against anemometer measurements and LES results. All models exhibited similar profiles for the mean wind speed, with a normalised RMSE of approximately 0.300. Regarding turbulent kinetic energy, the Lopes model outperformed the others, achieving an RMSE of 0.002. Lastly, for the Reynolds shear stress, Liu and Svensson’s models showed the lowest RMSE values (1.756 and 1.609), while others were over 4× larger (RMSE > 7). In summary, the performance of each model varies with the variable in focus, which is related to the model’s derivation method. However, considering all three analysed variables, Liu emerged as the most effective.
Power prediction using high-resolution SCADA data with a farm-wide deep neural network approach
Authors: Daenens Simon , Vervlimmeren Ivo , Verstraeten Timothy , Daems Pieter-Jan , Nowé Ann , Helsen Jan 
Published: June 2024

Abstract
Accurate loss estimation methods with a high level of temporal granularity are necessary to enable the implementation of efficient and adaptable control strategies for wind farms. Predictive models for the power of wind turbines within a wind farm are investigated using high-resolution SCADA data and deep learning methodologies. Traditional physical models offer detailed insights but are computationally expensive. Statistical models face limitations in handling wind energy variability. In this study, deep learning models are explored to capture spatial and temporal dynamics, with four models developed: Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory network (LSTM), and a hybrid CNN-LSTM model. SCADA data from an offshore wind farm is preprocessed, anomalies removed, and annotated based on operational regions. The models are trained, validated, and tested, demonstrating improved accuracy over baseline methods. The hybrid model, incorporating spatial and temporal information, yields the highest predictive performance, showcasing the significance of these dimensions in wind power prediction.
Analytical Wake Modeling in Atmospheric Boundary Layers: Accounting for Wind Veer and Thermal Stratification
Authors: Narasimhan Ghanesh , Gayme Dennice F. , Meneveau Charles 
Published: June 2024

Abstract
Reliable characterization of wind turbine wakes in the presence of Atmospheric Boundary Layer (ABL) flows is crucial to accurately predict wind farm performance. Wind veering in the ABL shears the wake in the lateral direction, and wind veer strength depends on the thermal stability of the ABL. Analytical wake modeling approaches must capture these ABL effects to ensure correct prediction of the wake structure under varied atmospheric conditions. To this end, a new physics-based analytical wake model is developed in this study that is capable of predicting the shape of wakes influenced by wind veer and thermal stratification effects. This model combines a novel ABL wind field model with the Gaussian wake model. The new ABL wind model is capable of predicting both the streamwise and spanwise velocity components in conventionally neutral (CNBL) and stable (SBL) ABL flows. The analytical expressions for both of these horizontal velocity components adhere to Monin-Obukhov Similarity Theory (MOST) in the surface layer, while capturing wind veering in the outer layer of the ABL. Incorporating this ABL model with the Gaussian wake model predicts laterally deflected wake shapes in a fully predictive and self-consistent fashion for a wide range of atmospheric conditions. The results also demonstrate that the enhanced wake model gives improved predictions relative to Large Eddy Simulations of power losses due to wake interactions under strongly stably stratified atmospheric conditions, where wind veer effects are dominant.
Optimizing Wind Farm Efficiency through Active Yaw Control: A Neural Network-Aided Game Theory Approach
Authors: Abedi H , Sarkar S , Wickström A 
Published: June 2024

Abstract
This research investigates the potential of a game-theoretic-based Active Yaw Control (AYC) strategy to enhance power generation in wind farms. The proposed AYC strategy in this study replaces traditional look-up tables with a trained Artificial Neural Network (ANN) that determines the optimal yaw misalignment for turbines under time-varying atmospheric conditions. The study examines a hypothetical 3x2 rectangular arrangement of NREL 5-MW wind turbines. The FAST.Farm simulation tool, utilizing the dynamic wake meandering (DWM) model, is employed to assess both the power performance and structural load on the wind turbines. When tested with two different inflow directions and ambient turbulence (10%), the AYC strategy demonstrated a maximum increase in total power output of 2.6%, although it affected individual turbines differently. It also exhibits an increase in some structural loads, such as tower-top torque, while some components experience a slight reduction in load. The results underscore the effectiveness of the ANN-guided game-theoretic algorithm in improving wind farm power generation by mitigating the negative impact of wake interference, offering a scalable and efficient method for optimizing large-scale wind farm. However, it is essential to evaluate the overall impact of AYC on wind farm efficiency in terms of both Annual Energy Production (AEP) and structural loading under various atmospheric conditions.
Extension and Validation of Minimalistic Prediction Model to Determine the Energy Production of Offshore Wind Farms
Authors: Sørensen Jens N. , Garcia Ariadna M. I. , Larsen Gunner C. , Pedersen Mads M. , Fournely David 
Published: June 2024

Abstract
Calculations of annual energy productions of wind farms are normally very computing demanding as they require simulations of the wind flow field inside the wind farms for a range of ambient wind conditions and directions. Although there exists many advanced computing tools for atmospheric flows, which, in principle, cope with all flow situations, most wind power developers rely their work on simplified engineering models based on analytical approaches and superposition of the flow behind a single row of wind turbines. An alternative to wake modeling is the fully developed wind farm array boundary layer model, which assumes that the wind farm is so large, that the wind field inside the wind farm is in equilibrium with the flow field of the ambient atmospheric boundary layer. Such a model was recently further developed by the authors using a simple correction for coping with finite-sized wind farms. The purpose of the present work is to extend further the finiteness correction formula and validate the model by comparing results to actual production data and to results from other simulation models, such as the Jensen, Gaussian and TurbOPark engineering models. In spite of the simplicity of the proposed model, it outperforms the other models, achieving results within 5% accuracy as compared with full-scale data from existing wind farms.
BladedFarmWake: A framework for evaluating the influence of upstream wakes on turbine loads using Bladed
Authors: Bangga G. , Bossanyi E. 
Published: June 2024

Abstract
A new framework “BladedFarmWake” to include the upstream wake effects into a wind turbine design tool Bladed was developed in the present work. The effects of neighboring turbines in a wind farm are extracted from a wind farm flow solver LongSim, which has been developed for designing wind farm controllers and evaluating wind farm performance, taking account of atmospheric conditions and wake effects including the importance of turbine layouts and individual turbine or wind farm control strategies. These wind farm effects are incorporated into Bladed simulations to obtain time accurate load analyses. BladedFarmWake is designed to work with less human interaction as much as possible, allowing the tool to be adopted in large scale load analyses within the wind turbine design load cases (DLCs). It is demonstrated that the timeseries of the wind flow field and the wake meandering effects are successfully modelled in the framework. The effects of velocity deficit and the wake added turbulence are well captured in the generated turbulent data. As a consequence of the velocity deficit from the upstream turbine, the hub load changes considerably due to the wake meandering effects. The newly developed integrated framework will be of value for wind turbine engineers to incorporate wind farm effects in the design process.
Wake structure analysis of a wind turbine under various atmospheric flow conditions and its application for mid-fidelity calibration
Authors: Euzenat F. , Baiza Martinez L. A. , Yu W. , Maheshwari P. , Pallud M. 
Published: June 2024

Abstract
A coupled Actuator Line Method (ALM) Large Eddy Simulation (LES) is used to simulate the wake structure of an isolated wind turbine. The wake centre position, boundary, and dynamics downstream of the wind turbine is determined under different atmospheric flow conditions, including neutral, stable, and unstable stratification. The wake centre lateral displacement and autocorrelation are seen to be affected by the atmospheric flow conditions. Finally, the instantaneous wake structure is then used as reference to improve the accuracy of mid-fidelity approach.
Dynamic response of a wind turbine wake subjected to surge and heave step motions under different inflow conditions
Authors: Hubert Antonin , Conan Boris , Aubrun Sandrine 
Published: June 2024

Abstract
The development of floating offshore wind turbines poses new challenges since the floating platform introduces complex dynamics in the wind turbine wake. These wake dynamics are intricately tied to the advection velocity, referring to the velocity of the downstream propagation of the air flow, and used in the context of wind farm modelling. The present article investigates the far-wake dynamic response of a wind turbine model subjected to heave (up-down translation) and surge (fore-aft translation) step motions under two distinct inflow conditions. Wind tunnel experiments were conducted with hot-wires in a realistic turbulent inflow and a low shear and no ground effect inflow, achieved by varying the hub height of the wind turbine model in the atmospheric boundary layer developed in the test section. The results show that the dynamic response of the wake under the low shear and no ground effect inflow conditions aligns with a second-order system with the presence of undershoots and overshoots. In contrast, under realistic conditions, it appears like a first-order system with undershoots and overshoots less evident in most cases. Despite these variations the determined advection velocity remains roughly the same and consistent with the literature for both heave and surge step motions, regardless of the inflow conditions.
Wind tunnel inflow data assimilation in an LES digital twin for improving validation against an experimental wind turbine wake
Authors: Gillyns E. , Buckingham S. , Van Beeck J. , Winckelmans G. 
Published: June 2024

Abstract
Wind energy is playing an essential role in the energy transition towards a carbon-neutral system, for which wind turbine wakes are one of the most important aspects. By extracting energy from the flow, the turbine generates power but also decreases the production and increases the loading of downstream wind turbines; and potentially affects nearby structures.
To better understand the physical phenomenon, Large Eddy Simulation (LES) offers scale-resolving capabilities and a physical insight into complex turbulence flows. The state of the art in terms of high-fidelity wake simulations consists in combining LES with an Actuator Line Model in order to capture the tip vortices and detailed wake dynamics.
This paper focuses on the comparison between such simulation, with experiments in the controlled environment of a wind tunnel, in order to assess the performance and limitations of the model. A large part is dedicated to reproducing a developing flow, with a controlled level of turbulence. The Recycle and Rescale Method is used to provide the mean inflow profile, while a novel approach using controlled volume forces allows to improve the turbulence level of the flow. With the proposed methodology, the mean profile is properly imposed, while the associated turbulence characteristics is improved by roughly 50 %.
Influence of platform motion on the energy production of a floating wind farm
Authors: De Pascali M. , Fontanella A. , Muggiasca S. , Belloli M. 
Published: June 2024

Abstract
This study investigates the dynamics of energy production in floating wind farms. Unlike their bottom-fixed counterparts, floating wind turbines experience large-amplitude, low-frequency movements affecting the rotor response and the wake development. We employed the multi-physics simulator FAST.Farm to study a seven-turbine wind farm, comparing conventional monopile foundations with semi-submersibles. Results show that, under undisturbed wind conditions, floating turbines exhibit a lower power-conversion efficiency due to platform tilt and the use of a thrust clipping controller. Conversely, waked turbines in a floating configuration have a higher power output than with a bottom-fixed foundation. This is attributed to the higher wind speed in their wake which is due to the lower thrust set point of the floating controller, the vertical deflection of the wake and the dynamic conditions at rotor created by motion.
Influence of wake meandering path on floater motions
Authors: Eliassen Lene Vien , Arreba Irene Rivera , De Vaal Jacobus , Panjwani Balram 
Published: June 2024

Abstract
This study shows how different versions of the dynamic wake meandering (DWM) model will result in different dynamic behaviour of a floating wind turbine. In this study, different softwares as well as different input wind boxes are applied. The effect of these variations were studied using statistics from both the meandering behaviour of the wake and the response of a floating wind turbine. The yaw response is the most sensitive, and this study shows that the standard deviation of the yaw response is between 10% to 60%, depending on the DWM options used when generating the wake wind field.
The effect of nacelle-to-rotor size on the wake of a miniature wind turbine
Authors: Dar Arslan Salim , Majzoub Rim , Porté-Agel Fernando 
Published: June 2024

Abstract
Wind tunnel experiments are performed to investigate the effect of nacelle-to-rotor size on the wake of a wind turbine under different Reynolds numbers. Four different turbine configurations are tested, which vary in the rotor diameter and nacelle length and diameter. The near wake region is observed to be significantly affected by the nacelle-to-rotor size of the turbine. The difference in the averaged streamwise velocity and streamwise turbulence intensity is affected more by the change in the rotor diameter than by the change in the nacelle diameter. This is likely due to the change in the nacelle length to rotor diameter ratio, and the change in the distance between the ground and rotor lower tip. The differences in the near wake characteristics are reduced by the increase in the Reynolds number. The onset of the far wake is unaffected by the nacelle-to-rotor size, and consequently, the far wake modeling can be performed with good accuracy using existing analytical models. The power coefficient values show an improvement with the increase in the Reynolds number due to the higher efficiency of the rotor and motor.
Experimental and numerical investigation on the potential of wake mixing by dynamic yaw for wind farm power optimization
Authors: Mühle F V , Tamaro S , Klinger F , Campagnolo F , Bottasso C L 
Published: June 2024

Abstract
This study investigates open-loop dynamic yaw control as a strategy for enhancing wind farm performance through wake mixing. The focus is on understanding the potential for enhanced wake mixing under different turbulence intensities, and the mechanisms triggering mixing. Experimental tests are conducted using scaled wind tunnel experiments with two model wind turbines. The wake flow is analysed by large eddy simulations (LES). Dynamic yawing is prescribed by sinusoidally varying the yaw angle at different excitation frequencies. The study reveals that dynamic yaw control, particularly at low inflow turbulence, leads to increased wake mixing. The resulting enhanced wind farm power capture has a rather flat maximum around an optimal Strouhal number. This is in contrast to high inflow turbulence, where the effectiveness of the control strategy is significantly reduced. The meandering motion of the wake induced by dynamic yaw excitation is identified as the key mechanism for improved wake recovery.
Dynamic individual pitch control for wake mitigation: Why does the helix handedness in the wake matter?
Authors: Coquelet M , Gutknecht J , Van Wingerden JW , Duponcheel M , Chatelain P 
Published: June 2024

AbstractWind farm flow control aims at mitigating wake effects in order to maximize power production in wind farms. This work mostly focuses on the Helix strategy, which relies on individual pitch control to radially offset the application point of the thrust force from the rotor center and to dynamically change its azimuthal position. Previous studies have shown that power gains for a downstream turbine are higher for a counter-clockwise (CCW) rotation of the application point than for a clockwise (CW) one. In the CCW case, the wake develops as a right-handed helix, while in the CW case, a left-handed helix is observed. Using Large Eddy Simulations, this paper shows that the helix handedness in the wake matters due to its interaction with the wake swirl. Results of the CCW and CW helix first highlight the formation of streamwise vorticity in the near wake, which is transformed into strong coherent vortices in the far wake. Those vortex structures, to some extent similar to the counter-rotating vortex pair in the wake of yawed wind turbines, are responsible for (i) displacing the wake thanks to their induced velocities and (ii) deforming the shape of the wake.