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At least 1,225 records · Page 68

Effects of Changing Atmospheric Conditions on Wind Turbine Performance (Poster)

Multi-megawatt, utility-scale wind turbines operate in turbulent and dynamic winds that impact turbine performance in ways that are gradually becoming better understood. This poster presents a study made using a turbulent flow field simulator (TurbSim) and a Turbine aeroelastic simulator (FAST) of the response of a generic 1.5 MW wind turbine to changing inflow. The turbine power output is found to be most sensitive to wind speed and turbulence intensity, but the relationship depends on the wind speed with respect to the turbine's rated wind speed. Shear is found to be poorly correlated to power. A machine learning method called 'regression trees' is used to create a simple model of turbine performance that could be used as part of the wind resource assessment process. This study has used simple flow fields and should be extended to more complex flows, and validated with field observations.

17 WIND ENERGY↗

Quantifying the Effect of Lidar Turbulence Error on Wind Power Prediction

Currently, cup anemometers on meteorological towers are used to measure wind speeds and turbulence intensity to make decisions about wind turbine class and site suitability; however, as modern turbine hub heights increase and wind energy expands to complex and remote sites, it becomes more difficult and costly to install meteorological towers at potential sites. As a result, remote-sensing devices (e.g., lidars) are now commonly used by wind farm managers and researchers to estimate the flow field at heights spanned by a turbine. Although lidars can accurately estimate mean wind speeds and wind directions, there is still a large amount of uncertainty surrounding the measurement of turbulence using these devices. Errors in lidar turbulence estimates are caused by a variety of factors, including instrument noise, volume averaging, and variance contamination, in which the magnitude of these factors is highly dependent on measurement height and atmospheric stability. As turbulence has a large impact on wind power production, errors in turbulence measurements will translate into errors in wind power prediction. The impact of using lidars rather than cup anemometers for wind power prediction must be understood if lidars are to be considered a viable alternative to cup anemometers.In this poster, the sensitivity of power prediction error to typical lidar turbulence measurement errors is assessed. Turbulence estimates from a vertically profiling WINDCUBE v2 lidar are compared to high-resolution sonic anemometer measurements at field sites in Oklahoma and Colorado to determine the degree of lidar turbulence error that can be expected under different atmospheric conditions. These errors are then incorporated into a power prediction model to estimate the sensitivity of power prediction error to turbulence measurement error. Power prediction models, including the standard binning method and a random forest method, were developed using data from the aeroelastic simulator FAST for a 1.5 MW turbine. The impact of lidar turbulence error on the predicted power from these different models is examined to determine the degree of turbulence measurement accuracy needed for accurate power prediction.

17 WIND ENERGY↗

Analysis of axial‐induction‐based wind plant control using an engineering and a high‐order wind plant model

Abstract Wind turbines are typically operated to maximize their performance without considering the impact of wake effects on nearby turbines. Wind plant control concepts aim to increase overall wind plant performance by coordinating the operation of the turbines. This paper focuses on axial‐induction‐based wind plant control techniques, in which the generator torque or blade pitch degrees of freedom of the wind turbines are adjusted. The paper addresses discrepancies between a high‐order wind plant model and an engineering wind plant model. Changes in the engineering model are proposed to better capture the effects of axial‐induction‐based control shown in the high‐order model. Copyright © 2015 John Wiley & Sons, Ltd.

Annoni, Jennifer↗

Turbulent kinetic energy over large offshore wind farms observed and simulated by the mesoscale model WRF (3.8.1)

Abstract. Wind farms affect local weather and microclimates;hence, parameterizations of their effects have been developed for numerical weather prediction models.While most wind farm parameterizations (WFPs) include drag effects of wind farms, models differ on whether or not an additionalturbulent kinetic energy (TKE) source should be included in theseparameterizations to simulate the impact of wind farms on the boundary layer.Therefore, we use aircraft measurements above large offshore wind farms in stable conditions to evaluateWFP choices. Of the three case studies we examine, we find the simulated ambient backgroundflow to agree with observations of temperature stratification and winds. This agreement allows us toexplore the sensitivity of simulated wind farm effects with respect to modeling choicessuch as whether or not to include a TKE source, horizontal resolution, vertical resolutionand advection of TKE. For a stably stratified marine atmospheric boundary layer (MABL), a TKE source and a horizontal resolutionon the order of 5 km or finer are necessary to represent the impact of offshore wind farms on the MABL.Additionally, TKE advection results in excessively reduced TKE over the wind farms, which in turn causesan underestimation of the wind speed deficit above the wind farm. Furthermore, using fine verticalresolution increases the agreement of the simulated wind speed with satellite observations of surface wind speed.

17 WIND ENERGY↗

A numerical study of polar ionospheric currents.

Numerical calculations for the electric current in the polar ionosphere have been made by assuming some realistic distributions of the electric field and conductivity. Two dynamo actions are taken into account; one of which is induced by ionospheric winds and the other by the solar wind. For the solar wind dynamo action, it is found that the secondary polarization field caused by nonuniform distribution of ionospheric conductivity is much larger than the primary field induced by the solar wind, suggesting its important effect on charged particles in the magnetosphere, and that the irrotational current having a source and sink is of the same order of magnitude as the solenoidal current closing its circuit in the ionosphere. It is also found that the solar wind is, in general, more effective than the ionospheric winds in producing polar current systems such as DP 1 and 2, but in some cases the ionospheric winds have a significant effect on the current distribution.

Maeda, H.↗

The Discharging of Roving Objects in the Lunar Polar Regions

In 2007, the National Academy of Sciences identified the lunar polar regions as special environments: very cold locations where resources can be trapped and accumulated. These accumulated resources not only provide a natural reservoir for human explorers, but their very presence may provide a history of lunar impact events and possibly an indication of ongoing surface reactive chemistry. The recent LCROSS impacts confirm that polar crater floors are rich in material including approx 5%wt of water. An integral part of the special lunar polar environment is the solar wind plasma. Solar wind protons and electrons propagate outward from the Sun, and at the Moon's position have a nominal density of 5 el/cubic cm, flow speed of 400 km/sec, and temperature of 10 eV (approx. equal 116000K). At the sub-solar point, the flow of this plasma is effectively vertically incident at the surface. However, at the poles and along the lunar terminator region, the flow is effectively horizontal over the surface. As recently described, in these regions, local topography has a significant effect on the solar wind flow. Specifically, as the solar wind passes over topographic features like polar mountains and craters, the plasma flow is obstructed and creates a distinct plasma void in the downstream region behind the obstacle. An ion sonic wake structure forms behind the obstacle, not unlike that which forms behind a space shuttle. In the downstream region where flow is obstructed, the faster moving solar wind electrons move into the void region ahead of the more massive ions, thereby creating an ambipolar electric field pointing into the void region. This electric field then deflects ion trajectories into the void region by acting as a vertical inward force that draws ions to the surface. This solar wind 'orographic' effect is somewhat analogous to that occurring with terrestrial mountains. However, in the solar wind, the ambipolar E-field operating in the collision less plasma replaces the gradient in pressure that would act in a collisional neutral gas. Human systems (roving astronauts or robotic systems created by humans) may be required to gain access to the crater floor to collect resources such as water and other cold-trapped material. However, these human systems are also exposed to the above-described harsh thermal and electrical environments in the region. Thus, the objective of this work is to determine the nature of charging and discharging for a roving object in the cold, plasma-starved lunar polar regions. To accomplish this objective, we first define the electrical charging environment within polar craters. We then describe the subsequent charging of a moving object near and within such craters. We apply a model of an astronaut moving in periodic steps/cadence over a surface regolith. In fact the astronaut can be considered an analog for any kind of moving human system. An astronaut stepping over the surface accumulates charge via contact electrification (tribocharging) v.lith the lunar regolith. We present a model of this tribo-charge build-up. Given the environmental plasma in the region, we determine herein the dissipation time for the astronaut to bleed off its excess charge into the surrounding plasma.

Jackson, T. L.↗

Mission 119 passive microwave results

Passive microwave measurements of the sea surface were made for determining surface wind speeds from the NP3A aircraft (NASA-927). Observations were made at frequencies of 1.4, 10.6, and 31.4 GHz during NASA mission 119, undertaken off Bermuda in the vicinity of Argus Island sea tower during January 1970. Passive microwave observations from Argus Island ocean showed that the surface roughness effect, dependent on wind speed, is also dependent on observational frequency, increasing with increasing frequency. The roughness effect appears to be dominant for wind speeds less than 30 to 40 knots (2).

Hollinger, J. P.↗

Effect of rain on Ku-band scatterometer wind measurements

The impact of precipitation on scatterometer wind measurements is investigated. A model is developed which includes the effects of rain attenuation, rain backscatter, and storm horizontal structure. Rain attenuation is found to be the dominant error source at low radar incidence angles and high wind speeds. Volume backscatter from the rain-loaded atmosphere, however, is found to dominate for high incidence angles and low wind speeds.

Spencer, Michael↗

Acceleration of the Fast Solar Wind by Solitary Waves in Coronal Holes

The purpose of this investigation is to develop a new model for the acceleration of the fast solar wind by nonlinear, time-dependent multidimensional MHD simulations of waves in solar coronal holes. Preliminary computational studies indicate that solitary-like waves are generated in coronal holes nonlinearly by torsional Alfven waves. These waves in addition to thermal conduction may contribute considerably to the accelerate the solar wind. Specific goals of this proposal are to investigate the generation of nonlinear solitary-like waves and their effect on solar wind acceleration by numerical 2.5D MHD simulation of coronal holes with a broad range of plasma and wave parameters; to study the effect of random disturbances at the base of a solar coronal hole on the fast solar wind acceleration with a more advanced 2.5D MHD model and to compare the results with the available observations; to extend the study to a full 3D MHD simulation of fast solar wind acceleration with a more realistic model of a coronal hole and solar boundary conditions. The ultimate goal of the three year study is to model the fast solar wind in a coronal hole, based on realistic boundary conditions in a coronal hole near the Sun, and the coronal hole structure (i.e., density, temperature, and magnetic field geometry) that will become available from the recently launched SOHO spacecraft.

Ofman, Leon↗

Acceleration of the Fast Solar Wind by Solitary Waves in Coronal Holes

The purpose of this investigation is to develop a new model for the acceleration of the fast solar wind by nonlinear. time-dependent multidimensional MHD simulations of waves in solar coronal holes. Preliminary computational studies indicate that nonlinear waves are generated in coronal holes by torsional Alfv\'{e}n waves. These waves in addition to thermal conduction may contribute considerably to the accelerate the solar wind. Specific goals of this proposal are to investigate the generation of nonlinear solitary-like waves and their effect on solar wind acceleration by numerical 2.5D MHD simulation of coronal holes with a broad range of plasma and wave parameters; to study the effect of random disturbances at the base of a solar coronal hole on the fast solar wind acceleration with a more advanced 2.5D MHD model and to compare the results with the available observations; to extend the study to a full 3D MHD simulation of fast solar wind acceleration with a more realistic model of a coronal hole and solar boundary conditions. The ultimate goal of the three year study is to model the, fast solar wind in a coronal hole, based on realistic boundary conditions in a coronal hole near the Sun, and the coronal hole structure (i.e., density, temperature. and magnetic field geometry,) that will become available from the recently launched SOHO spacecraft.

Ofman, Leon↗

Synergistic Effects of Turbine Wakes and Atmospheric Stability on Power Production at an Onshore Wind Farm

This report examines the complex interactions between atmospheric stability and turbine-induced wakes on downwind turbine wind speed and power production at a West Coast North American multi-MW wind farm. Wakes are generated when the upwind flow field is distorted by the mechanical movement of the wind turbine blades. This has two consequences for downwind turbines: (1) the downwind turbine encounters wind flows with reduced velocity and (2) the downwind turbine encounters increased turbulence across multiple length scales via mechanical turbulence production by the upwind turbine. This increase in turbulence on top of ambient levels may increase aerodynamic fatigue loads on the blades and reduce the lifetime of turbine component parts. Furthermore, ambient atmospheric conditions, including atmospheric stability, i.e., thermal stratification in the lower boundary layer, play an important role in wake dissipation. Higher levels of ambient turbulence (i.e., a convective or unstable boundary layer) lead to higher turbulent mixing in the wake and a faster recovery in the velocity flow field downwind of a turbine. Lower levels of ambient turbulence, as in a stable boundary layer, will lead to more persistent wakes. The wake of a wind turbine can be divided into two regions: the near wake and far wake, as illustrated in Figure 1. The near wake is formed when the turbine structure alters the shape of the flow field and usually persists one rotor diameter (D) downstream. The difference between the air inside and outside of the near wake results in a shear layer. This shear layer thickens as it moves downstream and forms turbulent eddies of multiple length scales. As the wake travels downstream, it expands depending on the level of ambient turbulence and meanders (i.e., travels in non-uniform path). Schepers estimates that the wake is fully expanded at a distance of 2.25 D and the far wake region begins at 2-5 D downstream. The actual distance traveled before the wake recovers to its inflow velocity is dependent on the amount ambient turbulence, the amount of wind shear, and topographical and structural effects. The maximum velocity deficit is estimated to occur at 1-2 D but can be longer under low levels of ambient turbulence. Our understanding of turbine wakes comes from wind tunnel experiments, field experiments, numerical simulations, and from studies utilizing both experimental and modeling methods. It is well documented that downwind turbines in multi-Megawatt wind farms often produce less power than upwind turbine rows. These wake-induced power losses have been estimated from 5% to up to 40% depending on the turbine operating settings (e.g., thrust coefficient), number of turbine rows, turbine size (e.g., rotor diameter and hub-height), wind farm terrain, and atmospheric flow conditions (e.g., ambient wind speed, turbulence, and atmospheric stability). Early work by Elliott and Cadogan suggested that power data for different turbulent conditions be segregated to distinguish the effects of turbulence on wind farm power production. This may be especially important for downwind turbines within wind farms, as chaotic and turbulent wake flows increase stress on downstream turbines. Impacts of stability on turbine wakes and power production have been examined for a flat terrain, moderate size (43 turbines) wind farm in Minnesota and for an offshore, 80 turbine wind farm off the coast of Denmark. Conzemius found it difficult to distinguish wakes (i.e., downwind velocity deficits) when the atmosphere was convective as large amounts of scatter were present in the turbine nacelle wind speed data. This suggested that high levels of turbulence broke-up the wake via large buoyancy effects, which are generally on the order of 1 km in size. On the other hand, they found pronounced wake effects when the atmosphere was very stable and turbulence was either suppressed or the length scale was reduced as turbulence in this case was mechanically produced (i.e., friction forces). This led to larger reductions at downwind turbines and maximum velocity (power) deficits reached up to 50% (70%) during strongly stable conditions. At an offshore Danish wind farm, Hansen et al. found a strong negative correlation between power deficit and ambient turbulence intensity (i.e., atmospheric stability). Under convective conditions, when turbulence levels were relatively high, smallest power deficits were observed. Power deficits approaching 35 to 40% were found inside the wind farm during stable conditions.

17 WIND ENERGY↗

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis – Simulated Wind

The U.S. Department of Energy and the National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis, hydrogen compression and storage, and variable hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) initiative. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence (AI) data centers and other variable loads. This dataset entry describes hydrogen production using a single, simulated wind turbine. The electrolyzer is a 1.25-MW proton exchange membrane type MC250 system manufactured by Nel Hydrogen . While the unit supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack. For the simulated wind energy profiles, NLR used OpenFAST to simulate a 3.4-MW International Energy Agency (IEA) reference wind turbine. The hour-long wind energy profiles varied over wind turbulence intensity (Class A or Class C) and average wind speed (5, 7, or 9 m/s). To match the power limits of the 1.25-MW electrolyzer and 3.4-MW IEA wind turbine most effectively and to maximize the efficiency of hydrogen production at a given average wind speed, the profiles were sometimes scaled by two times. This means that, in some cases, the experimental setup assumed two 1.25-MW electrolyzers were coupled with the wind turbine, representing a total maximum electrolysis load of 2.5 MW. Finally, NLR experimented with two settings for the electrolyzer power supply minimum and maximum current ramp rates (gain and slew): 200 and 400 amperes per second. The simulated profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single wind turbine electrolysis experiment and is formatted as follows: {technology}-{average wind speed}-{turbulence class}_{number of 1.25 MW electrolyzers connected}-{electrolyzer ramp rate in amperes/second} For instance, “windIEA3.4-5ms-C_2-400.zip” represents the hour-long experiment using the IEA 3.4-MW turbine, subjected to an average wind speed of 5 m/s and Class C wind turbulence, and connected to two 1.25-MW electrolyzers with the power supply set to a maximum current ramp rate (gain and slew) of 400 A/s. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production in kilograms per hour, electrolysis power consumption, and input wind turbine power. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30 minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all simulated wind experiments combined into one dataset labeled "combined_wind_experiments.csv". NLR also built an AI/machine-learning predictive model based on these datasets. The model ingests the electrolyzer current command in amperes, as well as various pressures and temperatures across the system, and predicts hydrogen output in kilograms per hour. The complete model can be found at https://huggingface.co/NatLabRockies/ptmelt-hydrogen-electrolysis .

08 HYDROGEN↗

Terrestrial Analogs to Wind-Related Features at the Viking and Pathfinder Landing Sites on Mars

Features in the Mojave Desert and Iceland provide insight into the characteristics and origin of Martian wind-related landforms seen by the Viking and Pathfinder landers. The terrestrial sites were chosen because they exhibit diverse wind features that are generally well understood. These features have morphologies comparable to those on Mars and include origins by deposition and erosion, with erosional processes modifying both soils and rocks. Duneforms and drifts are the most common depositional features seen at the Martian landing sites and indicate supplies of sand-sized particles blown by generally unidirectional winds. Erosional features include lag deposits, moat-like depressions around some rocks, and exhumed soil horizons. They indicate that wind can deflate at least some sediments and that this process is particularly effective where the wind interacts with rocks. The formation of ripples and wind tails involves a combination of depositional and erosional processes. Rock erosional features, or ventifacts, are recognized by their overall shapes, erosional flutes, and characteristic surface textures resulting from abrasion by windblown particles. The physics of saltation requires that particles in ripples and duneforms are predominantly sand-sized (60-2000 microns). The orientations of duneforms, wind tails, moats, and ventifacts are correlated with surface winds above particle threshold. Such winds are influenced by local topography and are correlated with winds at higher altitudes predicted by atmospheric models.

Greeley, Ronald↗

Coupled Ocean Wave-Atmosphere Models for Offshore Wind Energy Applications

Coupling ocean wave models to mesoscale atmospheric models is necessary to represent the effect of waves on wind turbine hub-height winds. In this report we provide a review of the most widely used ocean waves models and the phased-averaged spectral wave modeling paradigm that they are based on. Methodologies used to couple these wave models to mesoscale atmospheric models are described along with details of existing coupled modeling systems. We summarize impacts on offshore wind resource assessments that have been investigated with such coupled modeling systems to-date. Specifically, coupling in the North and Baltic Seas was shown to have a small negative effect on the offshore wind energy resource at weak-to-moderate wind speeds, with little impact at higher wind speeds. Finally, limitations of these existing coupled modeling systems and impact assessments are discussed, including examples of and the potential for improved parameterizations of the air-sea fluxes, and the use of fine-scaled simulations using phased-resolved wave models.

17 WIND ENERGY↗

Plume and Shock Interaction Effects on Sonic Boom in the 1-foot by 1-foot Supersonic Wind Tunnel

The desire to reduce or eliminate the operational restrictions of supersonic aircraft over populated areas has led to extensive research at NASA. Restrictions are due to the disturbance of the sonic boom, caused by the coalescence of shock waves formed by the aircraft. A study has been performed focused on reducing the magnitude of the sonic boom N-wave generated by airplane components with a focus on shock waves caused by the exhaust nozzle plume. Testing was completed in the 1-foot by 1-foot supersonic wind tunnel to study the effects of an exhaust nozzle plume and shock wave interaction. The plume and shock interaction study was developed to collect data for computational fluid dynamics (CFD) validation of a nozzle plume passing through the shock generated from the wing or tail of a supersonic vehicle. The wing or tail was simulated with a wedgeshaped shock generator. This test entry was the first of two phases to collect schlieren images and off-body static pressure profiles. Three wedge configurations were tested consisting of strut-mounted wedges of 2.5- degrees and 5-degrees. Three propulsion configurations were tested simulating the propulsion pod and aft deck from a low boom vehicle concept, which also provided a trailing edge shock and plume interaction. Findings include how the interaction of the jet plume caused a thickening of the shock generated by the wedge (or aft deck) and demonstrate how the shock location moved with increasing nozzle pressure ratio.

Shock wave interaction↗

Analysis of Transportation and Logistics Challenges Affecting the Deployment of Larger Wind Turbines: Summary of Results

There is relatively little literature that characterizes transportation and logistics challenges and the associated effects on U.S. wind markets. The objectives of this study were to identify the transportation and logistics challenges, assess the associated impacts, and provide recommendations for strategies and specific actions to address the challenges. The authors primarily relied on interviews with wind industry project developers, original equipment manufacturers, and transportation and logistics companies to obtain the information and industry perspectives needed for this study. They also reviewed published literature on trends and developments in increasing wind turbine size, logistics, and transportation issues.

17 WIND ENERGY↗