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Schreck, Scott

Publications and source records attributed to Schreck, Scott.

12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Measurements and predictions of wind turbine tower shadow and fairing effects

Downwind two-bladed rotor configurations can have advantages in reducing rotor mass for wind turbines, compared with three-bladed upwind designs. However, the tower shadow adds an aerodynamic complication that can be difficult to quantify and predict. This study presents and analyzes a previously unpublished subset of data collected during an extensive wind tunnel campaign, the Unsteady Aerodynamic Experiment (UAE). At high tip speed ratios, the tower shadow is a dominating contributor to bending moment oscillations but can be mitigated by the use of a tower fairing when such a fairing is aligned with the flow. At lower tip speed ratios where the blades can undergo aerodynamic stall and hysteresis, tower shadow was only a secondary contributor to bending moments and the tower fairing did not significantly impact bending moments. The aeroelastic simulation code called FAST was used to predict the same experimental conditions. In general, simulations reasonably predicted most of the cycle-averaged aspects, but only qualitatively predicted the unsteady variations due to tower shadow. To improve simulation predictions inside the tower wake, it is suggested that future work model the unsteady wake component associated with cylinder shedding and to consider a wake model for tower fairings at various wind incidence angles.

17 WIND ENERGY↗
DOE OSTI2017

A validation and code-to-code verification of FAST for a megawatt-scale wind turbine with aeroelastically tailored blades

Abstract. This paper presents validation and code-to-code verification of the latest version of the U.S. Department of Energy, National Renewable Energy Laboratory wind turbine aeroelastic engineering simulation tool, FAST v8. A set of 1141 test cases, for which experimental data from a Siemens 2.3 MW machine have been made available and were in accordance with the International Electrotechnical Commission 61400-13 guidelines, were identified. These conditions were simulated using FAST as well as the Siemens in-house aeroelastic code, BHawC. This paper presents a detailed analysis comparing results from FAST with those from BHawC as well as experimental measurements, using statistics including the means and the standard deviations along with the power spectral densities of select turbine parameters and loads. Results indicate good agreement among the predictions using FAST, BHawC, and experimental measurements. These agreements are discussed in detail in this paper, along with some comments regarding the differences seen in these comparisons relative to the inherent uncertainties in such a model-based analysis.

Guntur, Srinivas↗
DOE OSTI2017

A Validation and Code-to-Code Verification of FAST for a Megawatt-Scale Wind Turbine with Aeroelastically Tailored Blades

This paper presents validation and code-to-code verification of the latest version of the U.S. Department of Energy, National Renewable Energy Laboratory wind turbine aeroelastic engineering simulation tool, FAST v8. A set of 1,141 test cases, for which experimental data from a Siemens 2.3 MW machine have been made available and were in accordance with the International Electrotechnical Commission 61400-13 guidelines, were identified. These conditions were simulated using FAST as well as the Siemens in-house aeroelastic code, BHawC. This paper presents a detailed analysis comparing results from FAST with those from BHawC as well as experimental measurements, using statistics including the means and the standard deviations along with the power spectral densities of select turbine parameters and loads. Results indicate a good agreement among the predictions using FAST, BHawC, and experimental measurements. Here, these agreements are discussed in detail in this paper, along with some comments regarding the differences seen in these comparisons relative to the inherent uncertainties in such a model-based analysis.

17 WIND ENERGY↗
DOE OSTI2017

Full-Scale Field Test of Wake Steering

Wind farm control, in which turbine controllers are coordinated to improve farmwide performance, is an active field of research. One form of wind farm control is wake steering, in which a turbine is yawed to the inflow to redirect its wake away from downstream turbines. Wake steering has been studied in depth in simulations as well as in wind tunnels and scaled test facilities. This work performs a field test of wake steering on a full-scale turbine. In the campaign, the yaw controller of the turbine has been set to track different yaw misalignment set points while a nacelle-mounted lidar scans the wake at several ranges downwind. The lidar measurements are combined with turbine data, as well as measurements of the inflow made by a highly instrumented meteorological mast. In conclusion, these measurements are then compared to the predictions of a wind farm control-oriented model of wakes.

17 WIND ENERGY↗
DOE OSTI2017

An Advanced Actuator Line Method for Wind Energy Applications and Beyond: Preprint

The actuator line method to represent rotor aerodynamics within computational fluid dynamics has been in use for over a decade. This method applies a body force to the flow field along rotating lines corresponding to the individual rotor blades and employs tabular airfoil data to compute the force distribution. The actuator line method is attractive because compared to blade-resolved simulations, the required mesh is much simpler and the computational cost is lower. This work proposes a higher fidelity variant of the actuator line method meant to fill the space between current actuator line and blade-resolved simulations. It contains modifications in two key areas. The first is that of freestream velocity vector estimation along the line, which is necessary to compute the lift and drag along the line using tabular airfoil data. Most current methods rely on point sampling in which the location of sampling is ambiguous. Here we test a velocity sampling method that uses a properly weighted integral over space, removing this ambiguity. The second area of improvement is the function used to project the one-dimensional actuator line force onto the three-dimensional fluid mesh as a body force. We propose and test a projection function that spreads the force over a region that looks something like a real blade with the hope that it will produce the blade local and near wake flow features with more accuracy and higher fidelity. Our goal is that between these two improvements, not only will the flow field predictions be enhanced, but also the spanwise loading will be made more accurate. We refer to this combination of improvements as the advanced actuator line method. We apply these improvements to two different wind turbine cases. Although there is a strong wind energy motivation in our work, there is no reason these advanced actuator line ideas cannot be used in other applications, such as helicopter rotors.

17 WIND ENERGY↗
DOE OSTI2016

Trailed vorticity modeling for aeroelastic wind turbine simulations in stand still

Current fast aeroelastic wind turbine codes suitable for certification lack an induction model for standstill conditions. A trailed vorticity model previously used as addition to a blade element momentum theory based aerodynamic model in normal operation has been extended to allow computing the induced velocities in standstill. The model is validated against analytical results for an elliptical wing in constant inflow and against stand still measurements from the NREL/NASA Phase VI unsteady experiment. The extended model obtains good results in case of the elliptical wing, but underpredicts the steady loading for the Phase VI blade in attached flow. The prediction of the dynamic force coefficient loops from the Phase VI experiment is improved by the trailed vorticity modeling in both attached flow and stall in most cases. The exception is the tangential force coefficient in stall, where the codes and measurements deviate and no clear improvement is visible.

17 WIND ENERGY↗
DOE OSTI2016

Detailed field test of yaw-based wake steering

This study describes a detailed field-test campaign to investigate yaw-based wake steering. In yaw-based wake steering, an upstream turbine intentionally misaligns its yaw with respect to the inflow to deflect its wake away from a downstream turbine, with the goal of increasing total power production. In the first phase, a nacelle-mounted scanning lidar was used to verify wake deflection of a misaligned turbine and calibrate wake deflection models. In the second phase, these models were used within a yaw controller to achieve a desired wake deflection. This paper details the experimental design and setup. Lastly, all data collected as part of this field experiment will be archived and made available to the public via the U.S. Department of Energy's Atmosphere to Electrons Data Archive and Portal.

17 WIND ENERGY↗
DOE OSTI2016

Hurricane Resilient Wind Plant Concept Study Final Report

Hurricanes occur over much of the U.S. Atlantic and Gulf coasts, from Long Island to the U.S.-Mexico border, encompassing much of the nation's primary offshore wind resource. Category 5 hurricanes have made landfall as far north as North Carolina, with Category 3 hurricanes reaching New York with some frequency. Along the US West coast, typhoons strike with similar frequency and severity. At present, offshore wind turbine design practices do not fully consider the severe operating conditions imposed by hurricanes. Although universally applied to most turbine designs, International Electrotechnical Commission (IEC) standards do not sufficiently address the duration, directionality, magnitude, or character of hurricanes. To assess advanced design features that could mitigate hurricane loading in various ways, this Hurricane-Resilient Wind Plant Concept Study considered a concept design study of a 500-megawatt (MW) wind power plant consisting of 10-MW wind turbines deployed in 25-meter (m) water depths in the Western Gulf of Mexico. This location was selected because hurricane frequency and severity provided a unique set of design challenges that would enable assessment of hurricane risk and projection of cost of energy (COE) changes, all in response to specific U.S. Department of Energy (DOE) objectives. Notably, the concept study pursued a holistic approach that incorporated multiple advanced system elements at the wind turbine and wind power plant levels to meet objectives for system performance and reduced COE. Principal turbine system elements included a 10-MW rotor with structurally efficient, low-solidity blades; a lightweight, permanent-magnet, direct-drive generator, and an innovative fixed substructure. At the wind power plant level, turbines were arrayed in a large-scale wind power plant in a manner aimed at balancing energy production against capital, installation, and operation and maintenance (O&M) costs to achieve significant overall reductions in COE.

17 WIND ENERGY↗
DOE OSTI2016

FAST v8 Verification and Validation for a Megawatt-Scale Wind Turbine with Aeroelastically Tailored Blades: Preprint

This paper presents findings from a verification and validation exercise on the latest version of the U.S. Department of Energy/National Renewable Energy Laboratory's in-house wind turbine aeroelastic design code FAST v8. Results from a set of 1141 FAST simulations were compared to those from Siemens' BHawC design code results, as well as experimental data from a heavily instrumented 2.3-MW Siemens wind turbine located at the National Wind Technology Center. The code validation was performed following the IEC-61400-13 standard, where a set of select quantities of interest from simulations at various wind speed and atmospheric turbulence conditions were used for a three-way comparison between FAST, BHawC, and the measurements. Results highlight many improvements of the latest version of FAST over its previous versions. This paper also provides comments from the authors on the data quality, and avenues for potential future work using these results.

17 WIND ENERGY↗