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Hamilton, Nicholas

Publications and source records attributed to Hamilton, Nicholas.

At least 55 records · Page 3
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DOE OSTI2021

Aeroacoustic Assessment of Wind Plant Controls

The Aeroacoustic Assessment project aims to quantify changes in aeroacoustic noise generation by a utility scale wind turbine operating under imposed yaw offsets common for wake steering and wind plant control strategies. Active plant control utilizing wake deflection control strategies have been shown experimentally and computationally to reliably produce 1-2% of additional annual energy production (AEP) without significant changes in turbine structural loads, the potential impact on aeroacoustic emissions has yet to be quantified or completely understood. Yawed operation of a wind turbine changes the three-dimensional aerodynamic interaction between the rotor blades and the incoming atmospheric flow, leading to changes in noise generation. This work quantifies the extent to which active control induces additional aeroacoustic emissions from additional separation and other flow interaction dynamic effects. Given public concerns about wind turbine noise and the need for observational data required for regulators to establish noise restrictions, we must understand potential acoustic emissions resulting from active control prior to commercial deployment and the development of practical noise reduction methods and technologies. The work outlined in this report details the new aeroacoustic measurement capability developed at NREL and its application to making full-field observations of noise from a utility-scale wind turbine. Preliminary noise modeling with the aeroacoustics module in OpenFAST helped to establish the design of the experiment, specifically targeting the noise observed at observers distributed around the DOE-owned GE 1.5 MW wind turbine. Low-frequency and infrasound measurement capability implemented in this project establish an operational baseline for the wind turbine, setting the stage for future control, rotor blade, and turbine design research.

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DOE OSTI2021

The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows

Abstract. Wind turbine wake models typically require approximations, such as wake superposition and deflection models, to accurately describe wake physics. However, capturing the phenomena of interest, such as the curled wake and interaction of multiple wakes, in wind power plant flows comes with an increased computational cost. To address this, we propose a new hybrid method that uses analytical solutions with an approximate form of the Reynolds-averaged Navier–Stokes equations to solve the time-averaged flow over a wind plant. We compare results from the solver to supervisory control and data acquisition data from the Lillgrund wind plant obtaining wake model predictions which are generally within 1 standard deviation of the mean power data. We perform simulations of flow over the Columbia River Gorge to demonstrate the capabilities of the model in complex terrain. We also apply the solver to a case with wake steering, which agreed well with large-eddy simulations. This new solver reduces the time – and therefore the related cost – it takes to simulate a steady-state wind plant flow (on the order of seconds using one core). Because the model is computationally efficient, it can also be used for different applications including wake steering for wind power plants and layout optimization.

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DOE OSTI2020

Comparison of modular analytical wake models to the Lillgrund wind plant

Efficient and accurate wake models are required for wind plant performance modeling and the suite of engineering processes that support wind plant layout, control, and monitoring. Although many analytical and engineering wake models with low computational costs have been proposed, their ability to represent the power production of large wind plants in a wide range of atmospheric conditions is not completely understood. The following validation study reviews the underlying theory for analytical wake models, outlines quality control procedures for observational data, and compares model results with observational data from the Lillgrund Wind Plant. Lillgrund makes a valuable case study for wake modeling because of its regular arrangement and the relatively close spacing of constituent wind turbines, which lead to regular and significant wake interactions within the wind plant and the development of deep array flow conditions. Formulations for the velocity deficit, wake-added turbulence, and wake superposition methods are considered in a modular sense, yielding many possible configurations to represent wind turbine wakes, of which seven are examined in detail. Velocity deficit models that account for flow conditions in the near wake are better able to reproduce power production for wind turbines in the transitional region of the wind plant, where wind turbines experience as many as five wakes from upstream turbines. Finally, in the deep array, where power production reaches asymptotic values and wake statistics become quasi-periodic, wake superposition schemes become the largest driver in error reduction; using the linear or maximum wake superposition methods can reduce the relative root mean square error by as much as 40% in the deep array.

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DOE OSTI2020

Overview of FLORIS updates

In this paper, we review updates made to the FLORIS (FLOw Redirection and Induction in Steady State) open-source framework for wind farm control modeling, design and analysis. The updates are focused on improvements for large arrays of turbines - including heterogenous inflows and incorporating second-order wake steering effects - design, and analysis tools for practical application of wind farm control, and improvements to the software architecture for best practices in modularization and cooperative development.

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DOE OSTI2020

Modelling and assessing the near-wake representation and turbulence behaviour of control-oriented wake models

Due to the interaction between the wake of an upstream turbine on a downstream turbine, power losses and increased fatigue loads occur. By yawing the upstream turbine with regard to the wind direction, one can potentially reduce the power losses of the downstream turbine and reduce the fatigue loads. The evolution of the wake depends on the pressure gradient within the near-wake region and the turbulent kinetic energy and must be incorporated in existing wake steering algorithms to provide an accurate estimation of the wake flow. This paper will show a first approach to implement a near-wake model and a turbulence model in the curled wake model within the controls-oriented framework FLORIS. The near-wake model is based on an analytical expression of the velocity profile to model the pressure gradient. Furthermore, two turbulence models are incorporated within the curled wake model based on a Gaussian-distribution and a mixing length formulation. The adapted curled wake model is then assessed with the measurement data acquired in the wind tunnel at ForWind – University of Oldenburg. The evaluation of the models show good agreement for the velocity deficit and representation of the near-wake region. Furthermore, the turbulent kinetic energy behaved as expected in comparison to other work, showing a ring of high turbulent kinetic energy at non-yawed condition which is deflected to a curled shape at large yaw angles with the turbulence model based on a mixing length formulation.

FLORIS↗
DOE OSTI2020

Longitudinal coherence and short-term wind speed prediction based on a nacelle-mounted Doppler lidar

The spatial structure of turbulence in atmospheric boundary layer flows is highly relevant to wind energy. In particular, wind turbine control strategies based on inflow preview measurements require knowledge of the longitudinal evolution of turbulent flow as it approaches the rotor. These upstream measurements are usually obtained with nacelle-mounted wind lidars. In contrast to traditional in situ anemometry, lidars collect measurements within a probe volume which varies in size depending on the technology of the commercial system being used. Here, we address two issues related to the use of wind lidar to measure the incoming flow to a wind turbine: (i) whether existing longitudinal coherence models can be used to predict flow at the rotor, based on measurements performed at a distance away from the rotor; and (ii) what effect probe-volume averaging has on the inflow predictions. These two questions are critical to the design and implementation of robust wind turbine control strategies. To address these questions, we perform field measurements and large-eddy simulations to determine which incoming flow structures can be readily predicted with existing coherence models, and which require additional corrections to account for lidar volumetric averaging effects. Results reveal that the wind turbine induction zone has a negligible impact on the longitudinal coherence and first-order turbulence quantities, such as the standard deviation of velocity fluctuations. However, the phase of the signal, from which advection time periods of the turbulent structures are derived, is affected by the rotor blockage effect.

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DOE OSTI2020

Multimodel validation of single wakes in neutral and stratified atmospheric conditions

Previous research has revealed the need for a validation study that considers several wake quantities and code types so that decisions on the trade-off between accuracy and computational cost can be well informed and appropriate to the intended application. In addition to guiding code choice and setup, rigorous model validation exercises are needed to identify weaknesses and strengths of specific models and guide future improvements. Here, we consider 13 approaches to simulating wakes observed with a nacelle-mounted lidar at the Scaled Wind Technology Facility (SWiFT) under varying atmospheric conditions. We find that some of the main challenges in wind turbine wake modeling are related to simulating the inflow. In the neutral benchmark, model performance tracked as expected with model fidelity, with large-eddy simulations performing the best. In the more challenging stable case, steady-state Reynolds-averaged Navier–Stokes simulations were found to outperform other model alternatives because they provide the ability to more easily prescribe noncanonical inflows and their low cost allows for simulations to be repeated as needed. Dynamic measurements were only available for the unstable benchmark at a single downstream distance. These dynamic analyses revealed that differences in the performance of time-stepping models come largely from differences in wake meandering. This highlights the need for more validation exercises that take into account wake dynamics and are able to identify where these differences come from: mesh setup, inflow, turbulence models, or wake-meandering parameterizations. In addition to model validation findings, we summarize lessons learned and provide recommendations for future benchmark exercises.

17 WIND ENERGY↗
DOE OSTI2020

American WAKE experimeNt (AWAKEN)

The national laboratories, directed by the U.S. Department of Energy Wind Energy Technologies Office, will organize, design, and execute a landmark international wake observation and validation campaign known as the American WAKE experimeNt or AWAKEN. This document describes the vision and purpose for AWAKEN and describes some of the organizational activities that have taken place to date. The driving need for this experimental campaign is that wake interactions are among the least understood physical phenomena in wind plants today, leading to unexpected power and financial losses. New observation data gathered will be used to further validate wind plant models and lead to both improved layout and more optimal operation of wind farms with greater power production and improved reliability, ultimately leading to lower wind energy costs. This campaign will occur in the U.S. Midwest, where the largest concentration of wind farms in the U.S. is located and where significant future growth is most likely. The field campaign will provide a data set that is unique among wake studies, based on location, scope, and observational fidelity: most wake studies have largely focused on offshore wind farms, which are not currently critical to the U.S. wind energy supply, and with a limited number of observations. While AWAKEN is focused on land-based wind farms in flat terrain, many of the observations will help researchers understand fundamental wake behavior applicable in both offshore and complex terrain environments.

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DOE OSTI2020

American WAKE experimeNt (AWAKEN)

The American WAKE experimeNt (AWAKEN) is an international multi-institutional wind energy field campaign to better understand wake losses within operational wind farms. Wake interactions are among the least understood physical interactions in wind plants today, leading to unexpected power and profit losses. For example, Ørsted, the world’s largest offshore wind farm developer, recently announced a downward revision in energy estimates across their energy generation portfolio, primarily caused by underprediction of energy losses from wind farm blockage and wakes. In their announcement, they noted that the standard industry models used for their original energy estimates were inaccurate, and this was likely an industry-wide issue. To help further improve and validate wind plant models across scales from individual turbines as well as interfarm interactions between plants, new observations, such as those planned for AWAKEN, are critical. These model improvements will enable both improved layout and more optimal operation of wind farms with greater power production and improved reliability, ultimately leading to lower wind energy costs.

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DOE OSTI2020

Highlighting the Impact of Yaw Control by Parsing Atmospheric Conditions Based on Total Variation: Preprint

Identification of atmospheric conditions within a multivariate atmospheric dataset is a necessary step in the validation of wind plant control strategies. Most often, operating conditions are characterized in terms of aggregated observations and assume that the atmosphere is 'quasi-steady'. Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of wind plant operating conditions. Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation sets the stage for more advanced model validation and the development of real-time control and operation strategies. Controlling observational data for statistical stationarity highlights significant enhancements to the power production of waked turbines under wake steering wind plant control. Considering the combined energy ratio of the turbine to which yaw offsets are prescribed as well as the waked turbine show only moderate improvements when filtering for total validation. The same quality control that highlights wake steering also serves to underpin the off-nominal operation of the controlled turbine.

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DOE OSTI2020

Atmospheric condition identification in multivariate data through a metric for total variation

Identification of atmospheric conditions within a multivariable atmospheric data set is a necessary step in the validation of emerging and existing high-fidelity models used to simulate wind plant flows and operation.Atmospheric conditions relevant for wind energy research include stationary conditions, given the need for well-converged statistics for model validation, as well as conditions observed less frequently, such as extreme atmospheric events, which are used in wind turbine and wind plant design.Aggregation of observations without regard to covariance between time series discounts the dynamical nature of the atmosphere and is not sufficiently representative of atmospheric conditions.Identification and characterization of continuous time periods with atmospheric conditions that have a high value for analysis or simulation set the stage for more advanced model validation and the development of real-time control and operational strategies.The current work explores a single metric for variation in a multivariate data sample that quantifies variability within each channel as well as covariance between channels.The total variation is used to identify conditions of interest that conform to desired objective functions, such as stationary conditions, ramps or waves of wind speed, and changes in wind direction.Total variation is somewhat sensitive to the presence of outliers in the input data, and the method is best complemented by quality-control procedures to ensure reliable results.The direct detection and classification of events or conditions of interest within atmospheric data sets is vital to developing our understanding of wind plant response and to the formulation of forecasting and control models.

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DOE OSTI2019

National Wind Technology Center-Characterization of Atmospheric Conditions

Data provided by the instrumentation located at the National Wind Technology Center (NWTC) is compiled below for the purposes of characterizing the atmospheric operating conditions at the National Renewable Energy Laboratories (NREL) wind site. Conditions are presented conditioned on month of the year to show seasonal variation, and based on characteristic quantities seen in the atmospheric flow including, bulk wind speed, direction, and atmospheric stability. All data reported and analyzed in the following document were collected from instrumentation mounted on the 135 m-tall masts erected at the NWTC near Boulder, Colorado. Each of the two met-masts (named M4 and M5) were sited specifically to measure the inflow wind to several of the utility-scale turbines at the NWTC. The construction, operation, and maintenance of the masts has been supported since their commissioning in 2011 by the Department of Energy, Office of Energy Efficiency and Renewable Energy, Wind and Water Power Program. Data from the masts are publicly available for download or basic visualization, hosted on NREL's website (https://nwtc.nrel.gov/135mData). Data from the masts have been used in the past for a wide range of projects including wind turbine power performance measurements, high fidelity model development and validation, and atmospheric science research. An interactive visualization platform has been developed on NREL's Open Energy Information platform and can be used to reproduce many of the graphics included in the current work. This report describes in detail the atmospheric conditions recorded by M5, the inflow mast for the DOE-owned GE 1.5 MW wind turbine. The report focuses on data recorded from August 2012 to May 2018 and includes: an introduction to the wind climate at the NWTC; a physical description of the met masts; a review of installed instrumentation; and data collection, processing, and quality control methods. In addition to a concise description of the instrumentation from the met masts and the data channels they provide, this report characterizes the behavior of the atmospheric resource first in a cumulative sense, discussing mainly the distributions wind speeds, flow directions and thermodynamic properties at several heights along the towers. Periodicity in atmospheric conditions is then detailed through analysis of diurnal and annual cycles of the data streams. Periods of both low turbulence and extreme atmospheric events that are of interest in terms of wind turbine operation and wind energy research are detailed in the final chapter of this report. Low turbulence events are somewhat rare at the NWTC and are of interest from the perspective of validating the operation of new wind turbine and blade designs. Similarly, the NTWC is known for a relatively higher rate of occurrence of extreme weather events.

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DOE OSTI2019

Validation of Wind Power Plant Modeling Approaches in Complex Terrain: Preprint

The effect of terrain on wind-plant aerodynamics is often neglected due to the complex dynamics that make up terrain-atmosphere-turbine interactions and the computational effort required to adequately resolve the relevant physics. In this work, a comprehensive set of measurements (from the Wind Forecast Improvement Project 2) and two distinct modeling approaches are used to investigate the effects of topography on plant performance. The first approach consists of high-fidelity large-eddy simulations conducted for a one- hour period on a terrain-resolving mesh, and producing unsteady, heterogeneous flow that is sensitive to the local topography. The second approach makes use of a steady-state engineering model that has been modified to account for terrain effects. In situ and remote- sensing measurements upstream of the wind plant are used to prescribe initial and boundary conditions to the models, while wind turbine power measurements are used to assess the ability of the different models to capture the effects of terrain on turbine wake dynamics and therefore power production. The full paper will quantify the degree to which our high-fidelity and engineering models are able to capture realistic wind plant aerodynamics in complex terrain, and through improved understanding of the underlying physics, inform our model development efforts.

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DOE OSTI2019

FAST.Farm Response to Varying Wind Inflow Techniques: Preprint

FAST.Farm is a newly developed multiphysics, midfidelity engineering tool that can be used to predict turbine power and structural loads of wind turbines in a wind farm. Previous studies have shown the similarities and differences between FAST.Farm and large-eddy simulations (LES) using the same LES-precursor-generated ambient wind inflow. The ability to generate ambient wind inflow using a synthetic turbulence engineering model (e.g., TurbSim or the Mann model) has recently been integrated into FAST.Farm to potentially enable more computationally efficient usage of the tool. This work aims to: 1) compare FAST.Farm simulations using LES-generated inflow to those using synthetically generated inflow from TurbSim, and 2) establish guidelines by which TurbSim-generated inflow should be generated for wind farm analysis. It was found that properly setting spatial coherence parameters for the transverse wind velocity components is necessary to accurately predict wake meandering. It is shown that TurbSim-generated inflow can be used in FAST.Farm to accurately predict thrust, power, speed, and torque for waked and unwaked turbines; wake meandering behavior across different atmospheric conditions; and averaged wake-deficit advection and evolution effects.

17 WIND ENERGY↗
DOE OSTI2019

Turbulence kinetic energy budget and conditional sampling of momentum, scalar, and intermittency fluxes in thermally stratified wind farms

A description of the turbulence kinetic energy budget is investigated to understand the dynamics and turbulence energy transfer between the atmospheric boundary layer and large wind farms. The turbulence kinetic energy budget terms are a dominant factor in explaining the energy supply process in wind farms. Large eddy simulations are used to generate the thermally stratified wind turbine array boundary layer, where stable and unstable conditions are considered. The wakes represent a sink of mean kinetic energy, and turbulence transport redistributes energy in the wake region. The energy balance between the budget terms is dependent on the physical location in the wind farm. During the unstable scenario, turbulence is driven by the production, transport, and buoyancy terms. Further, the contribution of the turbulence kinetic energy budget terms is reduced with increasing atmospheric stability, especially above the shear layer, where the mechanical production is the dominant term. Pressure diffusion redistributes the turbulent energy from the streamwise direction into the other components of the normal Reynolds stresses. This distinctive feature of the changing thermal stratification is presented through the quadrant analysis. The momentum ejection and sweep frequencies are both comparable and dominant. The scalar flux shows an increase in the contribution of the negative correlation in the quadrants. The quadrant analysis is extended to account for intermittency based on the pointwise Holder exponents. As a result, positive correlations are dominant in the wake and in higher layers of the domain for both considered cases.

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DOE OSTI2019

FAST.Farm Response to Varying Wind Inflow and Discretization

FAST.Farm is a newly developed multiphysics, midfidelity engineering tool that can be used to predict turbine power and structural loads of wind turbines in a wind farm. Previous studies have shown the similarities and differences between FAST.Farm and large-eddy simulations (LES) using the same LES-precursor-generated ambient wind inflow. The ability to generate ambient wind inflow using a synthetic turbulence engineering model (e.g., TurbSim or the Mann model) has recently been integrated into FAST.Farm to potentially enable more computationally efficient usage of the tool. This work aims to: 1) compare FAST.Farm simulations using LES-generated inflow to those using synthetically generated inflow from TurbSim, and 2) establish guidelines by which TurbSim-generated inflow should be generated for wind farm analysis. It was found that properly setting spatial coherence parameters for the transverse wind velocity components is necessary to accurately predict wake meandering. It is shown that TurbSim-generated inflow can be used in FAST.Farm to accurately predict thrust, power, speed, and torque for waked and unwaked turbines; wake meandering behavior across different atmospheric conditions; and averaged wake-deficit advection and evolution effects.

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DOE OSTI2018

Validation of FAST.Farm Against Large-Eddy Simulations

FAST.Farm is a new midfidelity, multiphysics engineering tool for modeling the power performance and structural loads of wind turbines within a wind farm, including wake and array effects. Previous calibration of the tuneable model parameters of FAST.Farm has shown that its prediction of wake dynamics for a single wind turbine across different atmospheric stability conditions and nacelle-yaw errors matches well with high-fidelity large-eddy simulation at a small fraction of the computational expense. This paper presents a validation of FAST.Farm against large-eddy simulation for a series of cases - independent from those used to support the calibration - considering single-turbine and small wind-farm scenarios, which are both subject to variations in inflow and control. The validation has demonstrated that FAST.Farm reasonably accurately predicts: (1) thrust and power for individual turbines both in isolation and down the row of the small wind farm, (2) wake meandering behavior across different atmospheric conditions, and (3) averaged wake-deficit advection, evolution, and merging effects. As a result, the validation also highlights potential physics that could be improved in FAST.Farm in the future.

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