Abstract. Lidar systems installed on the nacelle of wind turbines can provide a preview of incoming turbulent wind. Lidar-assisted control (LAC) allows the turbine controller to react to changes in the wind before they affect the wind turbine. Currently, the most proven LAC technique is the collective pitch feedforward control, which has been found to be beneficial for load reduction. In literature, the benefits were mainly investigated using standard turbulence parameters suggested by the IEC 61400-1 standard and assuming Taylor's frozen hypothesis (the turbulence measured by the lidar propagates unchanged to the rotor). In reality, the turbulence spectrum and the spatial coherence change by the atmospheric stability conditions. Also, Taylor's frozen hypothesis does not take into account the coherence decay of turbulence in the longitudinal direction. In this work, we consider three atmospheric stability classes, unstable, neutral, and stable, and generate four-dimensional stochastic turbulence fields based on two models: the Mann model and the Kaimal model. The generated four-dimensional stochastic turbulence fields include realistic longitudinal coherence, thus avoiding assuming Taylor's frozen hypothesis. The Reference Open-Source Controller (ROSCO) by NREL is used as the baseline feedback-only controller. A reference lidar-assisted controller is developed and used to evaluate the benefit of LAC. Considering the NREL 5.0 MW reference wind turbine and a typical four-beam pulsed lidar system, it is found that the filter design of the LAC is not sensitive to the turbulence characteristics representative of the investigated atmospheric stability classes. The benefits of LAC are analyzed using the aeroelastic tool OpenFAST. According to the simulations, LAC's benefits are mainly the reductions in rotor speed variation (up to 40 %), tower fore–aft bending moment (up to 16.7 %), and power variation (up to 20 %). This work reveals that the benefits of LAC can depend on the turbulence models, the turbulence parameters, and the mean wind speed.
{"title":"Evaluation of lidar-assisted wind turbine control under various turbulence characteristics","authors":"F. Guo, D. Schlipf, P. Cheng","doi":"10.5194/wes-8-149-2023","DOIUrl":"https://doi.org/10.5194/wes-8-149-2023","url":null,"abstract":"Abstract. Lidar systems installed on the nacelle of wind turbines can provide a preview of incoming turbulent wind. Lidar-assisted control (LAC) allows the turbine controller to react to changes in the wind before they affect the wind turbine. Currently, the most proven LAC technique is the collective pitch feedforward control, which has been found to be beneficial for load reduction. In literature, the benefits were mainly investigated using standard turbulence parameters suggested by the IEC 61400-1 standard and assuming Taylor's frozen hypothesis (the turbulence measured by the lidar propagates unchanged to the rotor). In reality, the turbulence spectrum and the spatial coherence change by the atmospheric stability conditions. Also, Taylor's frozen hypothesis does not take into account the coherence decay of turbulence in the longitudinal direction. In this work, we consider three atmospheric stability classes, unstable, neutral, and stable, and generate four-dimensional stochastic turbulence fields based on two models: the Mann model and the Kaimal model. The generated four-dimensional stochastic turbulence fields include realistic longitudinal coherence, thus avoiding assuming Taylor's frozen hypothesis. The Reference Open-Source Controller (ROSCO) by NREL is used as the baseline feedback-only controller. A reference lidar-assisted controller is developed and used to evaluate the benefit of LAC. Considering the NREL 5.0 MW reference wind turbine and a typical four-beam pulsed lidar system, it is found that the filter design of the LAC is not sensitive to the turbulence characteristics representative of the investigated atmospheric stability classes. The benefits of LAC are analyzed using the aeroelastic tool OpenFAST. According to the simulations, LAC's benefits are mainly the reductions in rotor speed variation (up to 40 %), tower fore–aft bending moment (up to 16.7 %), and power variation (up to 20 %). This work reveals that the benefits of LAC can depend on the turbulence models, the turbulence parameters, and the mean wind speed.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43698625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. In this regard, wake superposition methods play a key role and remain a substantial source of uncertainty. Recently, new models based on mass and momentum conservation have been proposed in the literature. In the present work, such methods are extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.
{"title":"Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model","authors":"F. Blondel","doi":"10.5194/wes-8-141-2023","DOIUrl":"https://doi.org/10.5194/wes-8-141-2023","url":null,"abstract":"Abstract. Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. In this regard, wake superposition methods play a key role and remain a substantial source of uncertainty. Recently, new models based on mass and momentum conservation have been proposed in the literature. In the present work, such methods are extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49458449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. The flow inside and around large offshore wind farms can range from smaller structures associated with the mechanical turbulence generated by wind turbines to larger structures indicative of the mesoscale flow. In this study, we explore the variation in turbulence structures and dominant scales of vertical entrainment above large offshore wind farms located in the North Sea, using data obtained from a research aircraft. The aircraft was flown upstream, downstream, and above wind farm clusters. Under neutrally stratified conditions, there is high ambient turbulence in the atmosphere and an elevated energy dissipation rate compared to stable conditions. The intensity of small-scale turbulence structures is increased above and downstream of the wind farm, and it prevails over mesoscale fluctuations. But in stable stratification, mesoscale flow structures are not only dominant upstream of the wind farm but also downstream. We observed that the vertical flux of horizontal momentum is the main source of energy recovery in large offshore wind farms, and it strongly depends on the magnitude of the length scales of the vertical wind velocity component. The dominant length scales of entrainment range from 20 to ∼60 m above the wind farm in all stratification strengths, and in the wake flow these scales range from 10 to ∼100 m only under near-neutral stratification. For strongly stable conditions, negligible vertical entrainment of momentum was observed even just 2 km downstream of large wind farms. We also observed that there is a significant lateral momentum flux above the offshore wind farms, especially under strongly stable conditions, which suggests that these wind farms do not satisfy the conditions of an “infinite wind farm”.
{"title":"Turbulence structures and entrainment length scales in large offshore wind farms","authors":"Abdul Haseeb Syed, J. Mann, A. Platis, J. Bange","doi":"10.5194/wes-8-125-2023","DOIUrl":"https://doi.org/10.5194/wes-8-125-2023","url":null,"abstract":"Abstract. The flow inside and around large offshore wind farms can range from smaller structures associated with the mechanical turbulence generated by wind turbines to larger structures indicative of the mesoscale flow. In this study, we explore the variation in turbulence structures and dominant scales of vertical entrainment above large offshore wind farms located in the North Sea, using data obtained from a research aircraft. The aircraft was flown upstream, downstream, and above wind farm clusters. Under neutrally stratified conditions, there is high ambient turbulence in the atmosphere and an elevated energy dissipation rate compared to stable conditions. The intensity of small-scale turbulence structures is increased above and downstream of the wind farm, and it prevails over mesoscale fluctuations. But in stable stratification, mesoscale flow structures are not only dominant upstream of the wind farm but also downstream. We observed that the vertical flux of horizontal momentum is the main source of energy recovery in large offshore wind farms, and it strongly depends on the magnitude of the length scales of the vertical wind velocity component. The dominant length scales of entrainment range from 20 to ∼60 m above the wind farm in all stratification strengths, and in the wake flow these scales range from 10 to ∼100 m only under near-neutral stratification. For strongly stable conditions, negligible vertical entrainment of momentum was observed even just 2 km downstream of large wind farms. We also observed that there is a significant lateral momentum flux above the offshore wind farms, especially under strongly stable conditions, which suggests that these wind farms do not satisfy the conditions of an “infinite wind farm”.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47891018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Modal-based reduced-order models are preferred for modeling structures due to their computational efficiency in engineering problems. One of the important limitations of the classic modal approaches is that they are geometrically linear. This study proposes a fast correction method to account for geometric nonlinearities which stem from large deflections in cantilever beams. The method relies on pre-computed correction terms and thus adds negligibly small extra computational efforts during the time domain response analyses. The accuracy of the method is examined on a straight-beam model and International Energy Agency (IEA) 15 MW wind turbine blade model. The results show that the proposed method increases the accuracy of modal approaches significantly in secondary deflections due to nonlinearities such as axial and torsional motions for the two studied cases.
{"title":"A correction method for large deflections of cantilever beams with a modal approach","authors":"Ozan Gözcü, E. Barlas, Suguang Dou","doi":"10.5194/wes-8-109-2023","DOIUrl":"https://doi.org/10.5194/wes-8-109-2023","url":null,"abstract":"Abstract. Modal-based reduced-order models are preferred for modeling structures due to their computational efficiency in engineering problems. One of the important limitations of the classic modal approaches is that they are geometrically linear. This study proposes a fast correction method to account for geometric nonlinearities which stem from large deflections in cantilever beams. The method relies on pre-computed correction terms and thus adds negligibly small extra computational efforts during the time domain response analyses. The accuracy of the method is examined on a straight-beam model and International Energy Agency (IEA) 15 MW wind turbine blade model. The results show that the proposed method increases the accuracy of modal approaches significantly in secondary deflections due to nonlinearities such as axial and torsional motions for the two studied cases.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46003507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Venkatraman, Trond-Ola Hågbo, S. Buckingham, Knut Erik Teigen Giljarhus
Abstract. Assessing wind conditions in complex terrain requires computational fluid dynamics (CFD) simulations incorporating an accurate parameterization of forest canopy effects and Coriolis effects. This study investigates how incorporating source terms such as the presence of trees and the Coriolis force can improve flow predictions. Furthermore, the study examines the impact of using different sets of atmospheric boundary layer inflow profiles, including idealized profiles with a logarithmic velocity profile, and a set of fully developed profiles from a pressure-driven precursor simulation. A three-dimensional steady Reynolds-averaged Navier–Stokes (RANS) equations model is set up using OpenFOAM to simulate the flow over a complex terrain site comprising two parallel ridges near Perdigão, Portugal. A 7.5 km×7.5 km terrain of the Perdigão site is constructed from elevation data centered around a 100 m met-mast located on the southwest ridge. A 30 min averaged stationary period is simulated, which corresponds to near-neutral conditions at met-mast Tower 20 located at the southwest ridge. The period corresponds to the wind coming from southwest at 231∘ at 100 m height above ground at Tower 20. Five case setups are simulated using a combination of different source terms, turbulence models and inflow profiles. The prediction capability of these models is analyzed for different groups of towers on the southwest ridge and, on the towers further downstream inside the valley, on the northeast ridge. Including a canopy model improves predictions close to the ground for most of the towers on the southwest ridge and inside the valley. Large uncertainties are seen in field measurement data inside the valley, which is a recirculation zone, and large prediction errors are seen in the wind velocity, wind direction and turbulent kinetic profiles for most of the models. The predictions on the northeast ridge are dependent on the extent of recirculation predicted inside the valley. The inflow wind direction plays an important role in wind profile predictions.
{"title":"Effect of different source terms and inflow direction in atmospheric boundary modeling over the complex terrain site of Perdigão","authors":"K. Venkatraman, Trond-Ola Hågbo, S. Buckingham, Knut Erik Teigen Giljarhus","doi":"10.5194/wes-8-85-2023","DOIUrl":"https://doi.org/10.5194/wes-8-85-2023","url":null,"abstract":"Abstract. Assessing wind conditions in complex terrain requires computational fluid dynamics (CFD) simulations incorporating an accurate parameterization of forest canopy effects and Coriolis effects. This study investigates how incorporating source terms such as the presence of trees and the Coriolis force can improve flow predictions. Furthermore, the study examines the impact of using different sets of atmospheric boundary layer inflow profiles, including idealized profiles with a logarithmic velocity profile, and a set of fully developed profiles from a pressure-driven precursor simulation. A three-dimensional steady Reynolds-averaged Navier–Stokes (RANS) equations model is set up using OpenFOAM to simulate the flow over a complex terrain site comprising two parallel ridges near Perdigão, Portugal. A 7.5 km×7.5 km terrain of the Perdigão site is constructed from elevation data centered around a 100 m met-mast located on the southwest ridge. A 30 min averaged stationary period is simulated, which corresponds to near-neutral conditions at met-mast Tower 20 located at the southwest ridge. The period corresponds to the wind coming from southwest at 231∘ at 100 m height above ground at Tower 20. Five case setups are simulated using a combination of different source terms, turbulence models and inflow profiles. The prediction capability of these models is analyzed for different groups of towers on the southwest ridge and, on the towers further downstream inside the valley, on the northeast ridge. Including a canopy model improves predictions close to the ground for most of the towers on the southwest ridge and inside the valley. Large uncertainties are seen in field measurement data inside the valley, which is a recirculation zone, and large prediction errors are seen in the wind velocity, wind direction and turbulent kinetic profiles for most of the models. The predictions on the northeast ridge are dependent on the extent of recirculation predicted inside the valley. The inflow wind direction plays an important role in wind profile predictions.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47583718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Taruffi, Simone Di Carlo, S. Muggiasca, M. Belloli
Abstract. In the field of floating wind energy, large-scale wind turbine models deployed in natural environments represent a key link between small-scale laboratory tests and full-scale prototypes. While implying smaller cost, design and installation effort than a full-scale prototype, large-scale models are technologically very similar to prototypes, can be tested in natural sea and wind conditions, and reduce by a consistent amount the dimensional scaling issues arising in small-scale experiments. In this framework the presented work report the aerodynamic and control system assessment of a 1:15 model of the DTU 10 MW wind turbine installed on a multipurpose-platform model for fish farming and energy production. The model has operated for 6 months in a natural laboratory and has been exposed to fully natural and uncontrolled environmental conditions. Assessment is performed in terms of rotor thrust force and power controller parameters such as rotor speed, blade pitch and rotor power as a function of incoming wind speed.
{"title":"A large-scale wind turbine model installed on a floating structure: experimental validation of the numerical design","authors":"F. Taruffi, Simone Di Carlo, S. Muggiasca, M. Belloli","doi":"10.5194/wes-8-71-2023","DOIUrl":"https://doi.org/10.5194/wes-8-71-2023","url":null,"abstract":"Abstract. In the field of floating wind energy, large-scale wind\u0000turbine models deployed in natural environments represent a key link between\u0000small-scale laboratory tests and full-scale prototypes. While implying\u0000smaller cost, design and installation effort than a full-scale prototype,\u0000large-scale models are technologically very similar to prototypes, can be\u0000tested in natural sea and wind conditions, and reduce by a consistent amount\u0000the dimensional scaling issues arising in small-scale experiments. In this\u0000framework the presented work report the aerodynamic and control system\u0000assessment of a 1:15 model of the DTU 10 MW wind turbine installed on a\u0000multipurpose-platform model for fish farming and energy production. The\u0000model has operated for 6 months in a natural laboratory and has been\u0000exposed to fully natural and uncontrolled environmental conditions.\u0000Assessment is performed in terms of rotor thrust force and power controller\u0000parameters such as rotor speed, blade pitch and rotor power as a function of\u0000incoming wind speed.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48219302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Vimalakanthan, Harald van der Mijle Meijer, Iana Bakhmet, G. Schepers
Abstract. Leading edge erosion (LEE) is one of the most critical degradation mechanisms that occur with wind turbine blades (WTBs), generally starting from the tip section of the blade. A detailed understanding of the LEE process and the impact on aerodynamic performance due to the damaged leading edge (LE) is required to select the most appropriate leading edge protection (LEP) system and optimize blade maintenance. Providing accurate modeling tools is therefore essential. This paper presents a two-part study investigating computational fluid dynamics (CFD) modeling approaches for different orders of magnitudes in erosion damage. The first part details the flow transition modeling for eroded surfaces with roughness on the order of 0.1–0.2 mm, while the second part focuses on a novel study modeling high-resolution scanned LE surfaces from an actual blade with LEE damage on the order of 10–20 mm (approx. 1 % chord); 2D and 3D surface-resolved Reynolds-averaged Navier–Stokes (RANS) CFD models have been applied to investigate wind turbine blade sections in the Reynolds number (Re) range of 3–6 million. From the first part, the calibrated CFD model for modeling flow transition accounting for roughness shows good agreement of the aerodynamic forces for airfoils with leading-edge roughness heights on the order of 140–200 µm while showing poor agreement for smaller roughness heights on the order of 100 µm. Results from the second part of the study indicate that up to a 3.3 % reduction in annual energy production (AEP) can be expected when the LE shape is degraded by 0.8 % of the chord, based on the NREL5MW turbine. The results also suggest that under fully turbulent conditions, the degree of eroded LE shapes studied in this work show the minimal effect on the aerodynamic performances, which results in a negligible difference to AEP.
{"title":"Computational fluid dynamics (CFD) modeling of actual eroded wind turbine blades","authors":"K. Vimalakanthan, Harald van der Mijle Meijer, Iana Bakhmet, G. Schepers","doi":"10.5194/wes-8-41-2023","DOIUrl":"https://doi.org/10.5194/wes-8-41-2023","url":null,"abstract":"Abstract. Leading edge erosion (LEE) is one of the most critical degradation mechanisms that occur with wind turbine blades (WTBs), generally starting from the tip section of the blade. A detailed understanding of the LEE process and the impact on aerodynamic performance due to the damaged leading edge (LE) is required to select the most appropriate leading edge protection (LEP) system and optimize blade maintenance. Providing accurate modeling tools is therefore essential. This paper presents a two-part study investigating computational fluid dynamics (CFD) modeling approaches for different orders of magnitudes in erosion damage. The first part details the flow transition modeling for eroded surfaces with roughness on the order of 0.1–0.2 mm, while the second part focuses on a novel study modeling high-resolution scanned LE surfaces from an actual blade with LEE damage on the order of 10–20 mm (approx. 1 % chord); 2D and 3D surface-resolved Reynolds-averaged Navier–Stokes (RANS) CFD models have been applied to investigate wind turbine blade sections in the Reynolds number (Re) range of 3–6 million. From the first part, the calibrated CFD model for modeling flow transition accounting for roughness shows good agreement of the aerodynamic forces for airfoils with leading-edge roughness heights on the order of 140–200 µm while showing poor agreement for smaller roughness heights on the order of 100 µm. Results from the second part of the study indicate that up to a 3.3 % reduction in annual energy production (AEP) can be expected when the LE shape is degraded by 0.8 % of the chord, based on the NREL5MW turbine. The results also suggest that under fully turbulent conditions, the degree of eroded LE shapes studied in this work show the minimal effect on the aerodynamic performances, which results in a negligible difference to AEP.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43098171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract. Wind turbines are designed using a set of simulations to determine the fatigue and ultimate loads, which are typically focused solely on unwaked wind turbine operation. These structural loads can be significantly influenced by the wind inflow conditions. Turbines experience altered inflow conditions when placed in the wake of upstream turbines, which can additionally influence the fatigue and ultimate loads. It is important to understand the impact of uncertainty on the resulting loads of both unwaked and waked turbines. The goal of this work is to assess which wind-inflow-related and wake-related parameters have the greatest influence on fatigue and ultimate loads during normal operation for turbines in a three-turbine wind farm. Twenty-eight wind inflow and wake parameters are screened using an elementary effects sensitivity analysis approach to identify the parameters that lead to the largest variation in the fatigue and ultimate loads of each turbine. This study uses the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine, simulated with OpenFAST and synthetically generated inflow based on the International Electrotechnical Commission (IEC) Kaimal turbulence spectrum with the IEC exponential coherence model using the NREL tool TurbSim. The focus is on sensitivity to individual parameters, though interactions between parameters are considered, and how sensitivity differs between waked and unwaked turbines. The results of this work show that for both waked and unwaked turbines, ambient turbulence in the primary wind direction and shear are the most sensitive parameters for turbine fatigue and ultimate loads. Secondary parameters of importance for all turbines are identified as yaw misalignment, streamwise integral length, and the exponent and streamwise components of the IEC coherence model. The tertiary parameters of importance differ between waked and unwaked turbines. Tertiary effects account for up to 9.0 % of the significant events for waked turbine ultimate loads and include veer, non-streamwise components of the IEC coherence model, Reynolds stresses, wind direction, air density, and several wake calibration parameters. For fatigue loads, tertiary effects account for up to 5.4 % of the significant events and include vertical turbulence standard deviation, lateral and vertical wind integral lengths, non-streamwise components of the IEC coherence model, Reynolds stresses, wind direction, and all wake calibration parameters. This information shows the increased importance of non-streamwise wind components and wake parameters in the fatigue and ultimate load sensitivity of downstream turbines.
{"title":"Sensitivity analysis of the effect of wind and wake characteristics on wind turbine loads in a small wind farm","authors":"K. Shaler, A. Robertson, J. Jonkman","doi":"10.5194/wes-8-25-2023","DOIUrl":"https://doi.org/10.5194/wes-8-25-2023","url":null,"abstract":"Abstract. Wind turbines are designed using a set of simulations to determine the fatigue and ultimate loads, which are typically focused solely on unwaked wind turbine operation. These structural loads can be significantly influenced by the wind inflow conditions. Turbines experience altered inflow conditions when placed in the wake of upstream turbines, which can additionally influence the fatigue and ultimate loads. It is important to understand the impact of uncertainty on the resulting loads of both unwaked and waked turbines. The goal of this work is to assess which wind-inflow-related and wake-related parameters have the greatest influence on fatigue and ultimate loads during normal operation for turbines in a three-turbine wind farm. Twenty-eight wind inflow and wake parameters are screened using an elementary effects sensitivity analysis approach to identify the parameters that lead to the largest variation in the fatigue and ultimate loads of each turbine. This study uses the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine, simulated with OpenFAST and synthetically generated inflow based on the International Electrotechnical Commission (IEC) Kaimal turbulence spectrum with the IEC exponential coherence model using the NREL tool TurbSim. The focus is on sensitivity to individual parameters, though interactions between parameters are considered, and how sensitivity differs between waked and unwaked turbines. The results of this work show that for both waked and unwaked turbines, ambient turbulence in the primary wind direction and shear are the most sensitive parameters for turbine fatigue and ultimate loads. Secondary parameters of importance for all turbines are identified as yaw misalignment, streamwise integral length, and the exponent and streamwise components of the IEC coherence model. The tertiary parameters of importance differ between waked and unwaked turbines. Tertiary effects account for up to 9.0 % of the significant events for waked turbine ultimate loads and include veer, non-streamwise components of the IEC coherence model, Reynolds stresses, wind direction, air density, and several wake calibration parameters. For fatigue loads, tertiary effects account for up to 5.4 % of the significant events and include vertical turbulence standard deviation, lateral and vertical wind integral lengths, non-streamwise components of the IEC coherence model, Reynolds stresses, wind direction, and all wake calibration parameters. This information shows the increased importance of non-streamwise wind components and wake parameters in the fatigue and ultimate load sensitivity of downstream turbines.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2023-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46133330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stephanie Redfern, Mike Optis, Geng Xia, Caroline Draxl
Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.
{"title":"Offshore wind energy forecasting sensitivity to sea surface temperature input in the Mid-Atlantic","authors":"Stephanie Redfern, Mike Optis, Geng Xia, Caroline Draxl","doi":"10.5194/wes-8-1-2023","DOIUrl":"https://doi.org/10.5194/wes-8-1-2023","url":null,"abstract":"Abstract. As offshore wind farm development expands, accurate wind resource forecasting over the ocean is needed. One important yet relatively unexplored aspect of offshore wind resource assessment is the role of sea surface temperature (SST). Models are generally forced with reanalysis data sets, which employ daily SST products. Compared with observations, significant variations in SSTs that occur on finer timescales are often not captured. Consequently, shorter-lived events such as sea breezes and low-level jets (among others), which are influenced by SSTs, may not be correctly represented in model results. The use of hourly SST products may improve the forecasting of these events. In this study, we examine the sensitivity of model output from the Weather Research and Forecasting model (WRF) 4.2.1 to different SST products. We first evaluate three different data sets: the Multiscale Ultrahigh Resolution (MUR25) SST analysis, a daily, 0.25∘ × 0.25∘ resolution product; the Operational Sea Surface Temperature and Ice Analysis (OSTIA), a daily, 0.054∘ × 0.054∘ resolution product; and SSTs from the Geostationary Operational Environmental Satellite 16 (GOES-16), an hourly, 0.02∘ × 0.02∘ resolution product. GOES-16 is not processed at the same level as OSTIA and MUR25; therefore, the product requires gap-filling using an interpolation method to create a complete map with no missing data points. OSTIA and GOES-16 SSTs validate markedly better against buoy observations than MUR25, so these two products are selected for use with model simulations, while MUR25 is at this point removed from consideration. We run the model for June and July of 2020 and find that for this time period, in the Mid-Atlantic, although OSTIA SSTs overall validate better against in situ observations taken via a buoy array in the area, the two products result in comparable hub-height (140 m) wind characterization performance on monthly timescales. Additionally, during hours-long flagged events (< 30 h each) that show statistically significant wind speed deviations between the two simulations, both simulations once again demonstrate similar validation performance (differences in bias, earth mover's distance, correlation, and root mean square error on the order of 10−1 or less), with GOES-16 winds validating nominally better than OSTIA winds. With a more refined GOES-16 product, which has been not only gap-filled but also assimilated with in situ SST measurements in the region, it is likely that hub-height winds characterized by GOES-16-informed simulations would definitively validate better than those informed by OSTIA SSTs.","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135653910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. G. Mendonça, Lars Pilgaard Mikkelsen, Xiao Chen, J. Bode, F. Mortensen, P. Haselbach, K. Branner
Abstract. This work presents a methodology to evaluate the effect of wrinkle defects in the stiffness response of laminate characteristic of wind turbine blades. The assessment is carried out through numerical models and experimental tests with coupon specimens embedded with artificial wrinkles. Specimens are manufactured with two types of defects, prone to arising along the manufacturing process of wind turbine blades. Image-based numerical models were built to enclose the actual features of the cross-sectional wrinkling of each defect type. Experimental quasi-static tension and compression tests were performed, where extensometers collect the strain distribution about the wrinkle section as around the flat section of the test specimens. Two-dimensional finite element simulations carried out in Abaqus/Standard captured the stiffness behaviour of the two types of wrinkles. The numerical approach is validated against the quasi-static tests retrieving a fair agreement with experimental data. A significant knock-down in the stiffness response was found due to the wrinkle with larger aspect ratio of amplitude / half-wavelength.
{"title":"Methodology to predict stiffness knock-down in laminates for wind turbine blades with artificial wrinkles","authors":"H. G. Mendonça, Lars Pilgaard Mikkelsen, Xiao Chen, J. Bode, F. Mortensen, P. Haselbach, K. Branner","doi":"10.5194/wes-7-2513-2022","DOIUrl":"https://doi.org/10.5194/wes-7-2513-2022","url":null,"abstract":"Abstract. This work presents a methodology to evaluate the effect of wrinkle defects in the stiffness response of laminate characteristic of wind turbine blades. The assessment is carried out through numerical models and experimental tests with coupon specimens embedded with artificial wrinkles. Specimens are manufactured with two types of defects, prone to arising along the manufacturing process of wind turbine blades. Image-based numerical models were built to enclose the actual features of the cross-sectional wrinkling of each defect type. Experimental quasi-static tension and compression tests were performed, where extensometers collect the strain distribution about the wrinkle section as around the flat section of the test specimens. Two-dimensional finite element simulations carried out in Abaqus/Standard captured the stiffness behaviour of the two types of wrinkles. The numerical approach is validated against the quasi-static tests retrieving a fair agreement with experimental data. A significant knock-down in the stiffness response was found due to the wrinkle with larger aspect ratio of amplitude / half-wavelength.\u0000","PeriodicalId":46540,"journal":{"name":"Wind Energy Science","volume":" ","pages":""},"PeriodicalIF":4.0,"publicationDate":"2022-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46482978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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