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Terminal area energy management regime investigations utilizing an 0.030-scale model (47-0) of the space shuttle vehicle orbiter configuration 140A/B/C/R in the Ames Research Center 11 x 11 foot transonic wind tunnel (OA148), volume 12
Plots and tabular listings are presented for both force and pressure data for the upper and lower body flap.
Antennas Lower Cost of Satellite Access
Whether for scientific inquiry, weather forecasting, or public safety, the world relies upon the data gathered by satellite remote sensing. Some of NASA s most valuable work is in its remote sensing capabilities - the ability to retrieve data acquired at great distances - affording a height and scope not available from the ground. NASA satellites in low Earth orbit (LEO) monitor ocean health by taking large-scale pictures of phytoplankton blooms and measuring surface temperatures; snap photographs of full hurricanes from above, teaching researchers about how these giant storms form; and capture images of cloud formation and air pollution, all allowing researchers to further develop understanding of the planet s health. NASA remote sensing satellites also monitor shifts in the Earth s crust, analyze wind patterns around the world to develop efficient wind energy, help people around the world recover from natural disasters, and monitor diminishing sea ice levels. Just as researchers are more heavily relying on this data from space to conduct their work, the instruments carried on satellites are getting more sophisticated and capable of capturing increasingly complex and accurate measurements. The satellites are covering larger areas, from farther away, and generating more and more valuable data. The ground-based receivers for this wealth of satellite data have grown increasingly capable of handling greater bandwidth and higher power levels. They have also become less expensive, through a NASA research partnership, with the creation of a high-rate X-band data receiver system that is now in widespread use around the globe.
Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf
This report provides the results of a detailed analysis into the causes of high wind speed bias in the 20-year wind resource data set for offshore California the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting (WRF) model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California outer continental shelf (OCS), near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a one-year period. Upon the discovery of this bias, the Department of Energy (DOE) and BOEM funded NREL and PNNL to investigate the causes of, impacts of, and solutions to the bias in the CA20 data set. This report summarizes the findings of this research. We first investigated whether different WRF model setups could lead to reduced bias. We found that the choice of planetary boundary layer (PBL) scheme - which controls the vertical turbulent mixing of momentum, heat, and moisture in the lowermost part of the atmosphere - greatly affected hub-height wind speeds in the region. Specifically, switching from the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme used in CA20 (and widely used across a range of operational and research weather models) to the less common Yonsei University (YSU) scheme nearly eliminated the bias at both the Humboldt and Morro Bay lidar locations. The large discrepancy between the MYNN- and YSU-modeled hub-height winds pointed towards the role of atmospheric stability. In general, PBL schemes agree well in conditions of high turbulence and mixing, normally referred to as "unstable" conditions. By contrast, PBL schemes start to diverge in "stable" conditions, where turbulence is low and thermal stratification (i.e., higher temperature air sitting on top of colder air) greatly suppresses vertical mixing. Under such conditions, winds aloft can decouple from surface effects and greatly accelerate, causing high wind speeds at hub-height and frequent low-level jets (LLJs). We determined that these stable conditions are in fact dominant in offshore California. The region is characterized by moderate-to-extreme stable stratification with a LLJ on average around 200 meters above sea-level. To our knowledge, no wind energy area globally has as strongly stable stratification as offshore California. Under these extreme conditions, we determined that the MYNN scheme models higher stability than YSU, resulting in less vertical turbulent mixing than YSU, allowing for the acceleration of hub-height winds, more intense LLJs, and higher-amplitude inertial oscillations. Using surface observations, we found that MYNN overestimates near-surface stability, whereas YSU tends to model stability better. We then considered several short-term case studies to assess additional meteorological drivers of the bias at Humboldt. We found that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN case studies contributes to the modeled wind speed bias by altering the boundary layer thermodynamics via a thermal wind mechanism. Given the strong performance of the YSU-based runs in offshore California, NREL has produced and published an updated version of the CA20 data set with YSU as the PBL scheme. This updated data set is now part of NREL's 2023 National Offshore Wind (NOW-23) data set, which covers all the U.S. offshore waters. The development and final validation of the NOW-23 data set in offshore California is documented in this report.
Distributed Wind: Supporting the Clean Energy Transition
Distributed wind technologies have advanced a great deal over the last decade, developing from a very selective market to one that is poised to play a significant role in supporting a clean energy transition. Summarizing technical and modeling advances, most of them supported by program efforts undertaken by the Wind Energy Technology Office of the U.S. Department of Energy, this presentation provides an overview of the current state of distributed wind technologies and how the technology could support the wider distributed energy development. The presentation also provides an short introduction to the challenges and ongoing research to address these challenges.
Acoustic Travel-Time Tomography for Wind Energy
New remote sensing technologies for direct observation of the atmospheric flow in the proximity of operating wind turbines are necessary for the advancement of wind turbine design, wind plant control, and the validation of high-fidelity numerical models; fundamental goals of the DOE Wind Energy Technology Office's (WETO's) A2e program. Modern remote sensing technologies for commercial and research applications rely on the backscatter of light or sound waves, which limits spatial and temporal resolution and cannot produce reliable flow estimates near solid bodies or reflective surfaces. Acoustic tomography (AT) is an innovative sensing technology based on fundamental thermodynamic properties of the atmosphere and an inverse algorithm that can reconstruct both velocity and temperature fields within a network of acoustic transducers. AT relies on the direct travel path of acoustic signals between speakers and microphones in the network and can achieve much higher resolutions than conventional backscatter technologies. For wind energy R&D applications, AT represents an extremely economical option over other remote sensing technologies, integrating mature and commonly available acoustic and signal processing hardware. This report is intended as an update on the development of the AT array on the NREL Flatirons Campus (FC) and includes a brief review of the instrumentation, the calibration of equipment, signal processing, and resulting flow field estimates. Designs for future AT systems and applications are detailed in this report, including summaries of new capabilities, engineering and theoretical challenges, and anticipated costs help to outline future directions for AT technology R\&D. The potential for application of AT to utility-scale turbines is also discussed. Investing in the development of the proposed instrument will establish AT as a transformational RS technique that represents a significant advance in measurement instrumentation, enabling new fundamental insights into the structure of the atmospheric boundary layer and supporting applications for wind energy, pollution transport, and resource characterization. AT is also an extremely economical option over other RS instruments, integrating mature and commonly available acoustic and signal processing hardware.
Grand-scale Atmospheric Imaging Apparatus (GAIA) and Wind LiDAR Multi-scale Measurements in the Atmospheric Surface Layer
Understanding the organization and dynamics of turbulence structures in the atmospheric surface layer (ASL) is important for fundamental and applied research in different fields, including weather prediction, snow settling, particle and pollutant transport, and wind energy. The main challenges associated with probing and modeling turbulence in the ASL are: i) the broad range of turbulent scales associated with the different eddies present in high Reynolds-number boundary layers ranging from the viscous scale ($\mathcal{O}$(mm)) up to large energy-containing structures ($\mathcal{O}$(km)); ii) the non-stationarity of the wind conditions and the variability associated with the daily cycle of the atmospheric stability; iii) the interactions among eddies of different sizes populating different layers of the ASL, which contribute to momentum, energy, and scalar turbulent fluxes. Creative and innovative measurement techniques are required to probe near-surface turbulence by generating spatio-temporally-resolved data in the proximity of the ground and, at the same time, covering the entire ASL height with large enough streamwise extent to characterize the dynamics of larger eddies evolving aloft. To this aim, the U.S. National Science Foundation sponsored the development of the Grand-scale Atmospheric Imaging Apparatus (GAIA) enabling super-large snow particle image velocimetry (SLPIV) in the near-surface region of the ASL. This inaugural version of GAIA provides a comprehensive measuring system by coupling SLPIV and two scanning Doppler LiDARs to probe the ASL at an unprecedented resolution. A field campaign performed in 2021–2022 and its preliminary results are presented herein elucidating new research opportunities enabled by the GAIA measuring system.
Manufacturing and Additive Design of Electric Machines by 3D Printing (MADE3D) (Final Technical Report)
As the U.S clean energy transition hinges on the growth of offshore wind, this is expected to be a major driver for innovations in manufacturing, material and design advancements to enable robust and cost-competitive wind turbines. The race to build larger and taller turbines rated 10 megawatts (MW) and beyond, has intensified pressure on OEMs in terms of logistics of handling and transporting large wind components, securing critical raw material as well as scaling up necessary domestic production infrastructure to meet the demand. There is increased interest in building lightweight, more efficient, and high-power dense drivetrains that will ease the burden on installation, minimize the raw material demand, increasing domestic sourceability. Despite good efficiency and reliability, existing drivetrain technologies such as direct-drive permanent magnet generators are heavy (> 300 tons), expensive, and often rely on large quantities of rare-earth permanent magnets (PMs), steel and copper.
Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)
The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.
INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements
The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...
Simulated wind farm wake sensitivity to configuration choices in the Weather Research and Forecasting model version 3.8.1
Wakes from wind farms can extend over 50 km downwind in stably stratified conditions. These wakes can undermine power production at downwind turbines, adversely impacting revenue. As such, wind farm wake impacts must be considered in wind resource assessments, especially in regions of dense wind farm development. The open-source Weather Research and Forecasting (WRF) numerical weather prediction model includes a wind farm parameterization to estimate wind farm wake effects, but model configuration choices can influence the resulting predictions of wind farm wakes. These choices include vertical resolution, horizontal resolution, and whether or not to include the addition of turbulent kinetic energy generated by the rotating wind turbines. Despite the sensitivity to model configuration, no clear guidance currently exists for these options. Here we compare simulated wind farm wakes produced by varying model configurations with meteorological observations near a land-based wind farm in flat terrain over several diurnal cycles. A WRF configuration comprised of horizontal resolutions of 3 km or 1 km paired with a vertical resolution of 10 m provides the most accurate representation of wind farm wake effects, such as the correct surface warming and elevated wind speed deficit. The inclusion of turbine-generated turbulence is also critical to produce accurate surface warming and should not be omitted.
Design, construction, and testing of no-insulation small subscale solenoids for compact tokamaks
Fusion energy systems studies (FESS) for next-step devices based on the most promising magnetic configurations indicate that high magnetic fields and high current density for magnet coil systems may reduce device size and lower the cost. High current density and radiation resistant fusion magnets are particularly beneficial for low cost, low aspect ratio compact reactor designs such as Fusion Nuclear Science Facility (FNSF), Fusion Pilot Plant (FPP) of low-aspect ratio spherical tokamak (ST) or compact stellarators. Unlike typical high field magnets for nuclear magnetic resonance (NMR), magnetic resonance imaging (MRI), or high energy physics research applications, neutron irradiation damage to organic insulations in the coil winding pack is a critical issue for next generation fusion reactors where orders of magnitude higher neutron fluence than those in present experimental reactors such as ITER are expected. Moreover, a high coil winding pack current density is needed for high field magnets in low cost, compact radial build next step fusion reactors. The slow current charging time, however, is an issue in fully non-insulated coils. In this work, we present the design, construction and testing of subscale Nb3Sn solenoids, up to half the diameter of the central solenoid (CS) for National Spherical Torus Experiment (NSTX), with and without inter-layer insulation for enhancing radiation resistance while improving the winding pack current density and coil performance. The major radius for the NSTX/NSTX-Upgrade (reference for compact ST) is 0.854/0.934 meter, and 4.8 meters for FNSF. The magnetic field for NSTX/NSTX-U at the plasma center is 0.6/1 Tesla and 9 Tesla for FNSF. The inner diameter of the central solenoid coil is 0.2/0.4 meter for NSTX/NSTX-U and 1.2 meters for FNSF. The current charging and discharging behavior of the prototype solenoids was investigated to quantify performance advantages of a simplified coil fabrication without error-prone vacuum pressure impregnation (VPI) for the removal of epoxy organic insulation. Scalability of the no insulation concept for fusion can be addressed via engineered coating for novel insulation on contact resistance or intra-layer no insulations. Radiation resistance and coil winding efficiency were significantly improved in the no insulation coils for next step compact fusion reactors.
Scalable Electrolytic Systems for Renewable Hydrogen Production (Cooperative Research and Development Final Report, CRADA Number CRD-18-747)
The project focuses on offshore wind energy which is an emergent renewable energy source in the United States. Onsite hydrogen production for this sector is not available. The overall goal of GTA's work is development of scalable multi-megawatt water electrolysis systems that exclude platinum group metals, are constructed from readily available commodity materials and are mass produced using advanced manufacturing and robotics. They fabricated and successfully tested a scalable TRL 4 laboratory prototype that demonstrated promising results for achieving the goal. They further performed calculations and prepared scale drawings for an envisioned onsite TRL 8 electrolytic hydrogen production system at an offshore 8 MW wind turbine. The work plan of the CRADA proposal between NREL and GTA focuses on validation of the current TRL 4 prototype with potential development and refinement for advancement to a TRL 5 system. GTA, Inc. proposes joining the H2@Scale consortium to leverage DOE funds, benefit from and contribute to the common knowledge-base and work with NREL as a CRADA partner.
Demonstration of Wake Steering Through Yaw Control in a Wind Plant Field Experiment: Cooperative Research and Development Final Report, CRADA Number CRD-16-00629
Over the last few decades, wind energy has evolved into a large international industry involving major players in the manufacturing, construction, and utility sectors. Coinciding with the industry’s growth, significant innovation in the technology has resulted in larger turbines with lower associated costs of energy and more complex designs in all subsystems. However, as the deployment of the technology has grown and its role within the electricity sector become more prominent, so have the expectations of the technology in terms of performance, reliability, and cost. The industry currently partitions its efforts into separate paths for turbine design, plant design and development, finance, grid interaction and operation, mitigation of adverse community and environmental impacts, and other areas. One prominent area where this partition is evident is in wind turbine control. Traditionally, each wind turbine in a wind plant has been controlled separately – via its own internal controller using only its own sensors. However, wind turbines in a plant interact with each other through the plant-level fluid dynamics. Wake losses (due to upstream turbines extracting energy from the winds and “waking” downstream turbines) can be up to 10% or even 20% of the gross energy production (if each turbine experienced the free stream wind inflow to the plant). A series of studies and experiments have demonstrated that there is potential for improving energy output at existing plants through plant control methods which seek to optimize total wind plant energy production over the current “greedy” approach where each turbine maximizes its own production. Wake steering induced by yaw offsets (turning the turbine to be out of the plane perpendicular to wind inflow) for upstream turbines has shown significant promise in simulations and wind tunnel experiments. In simulation studies, annual energy production has been shown to increase by 2% or more depending on the particular aspects of the wind plant (turbine spacing, meteorological conditions, etc). This project seeks to demonstrate the potential of plant-level controls via wake steering at a commercial wind plant. This is an important step towards commercialization and industry adoption of this plant-level modeling and analysis capability.
DSMC Simulations of Shock Interactions About Sharp Double Cones
This paper presents the results of a numerical study of shock interactions resulting from Mach 10 flow about sharp double cones. Computations are made by using the direct simulation Monte Carlo (DSMC) method of Bird. The sensitivity and characteristics of the interactions are examined by varying flow conditions, model size, and configuration. The range of conditions investigated includes those for which experiments have been or will be performed in the ONERA R5Ch low-density wind tunnel and the Calspan-University of Buffalo Research Center (CUBRC) Large Energy National Shock (LENS) tunnel.
DSMC Simulation of Separated Flows About Flared Bodies at Hypersonic Conditions
This paper describes the results of a numerical study of interacting hypersonic flows at conditions that can be produced in ground-based test facilities. The computations are made with the direct simulation Monte Carlo (DSMC) method of Bird. The focus is on Mach 10 flows about flared axisymmetric configurations, both hollow cylinder flares and double cones. The flow conditions are those for which experiments have been or will be performed in the ONERA R5Ch low-density wind tunnel and the Calspan-University of Buffalo Research Center (CUBRC) Large Energy National Shock (LENS) tunnel. The range of flow conditions, model configurations, and model sizes provides a significant range of shock/shock and shock/boundary layer interactions at low Reynolds number conditions. Results presented will highlight the sensitivity of the calculations to grid resolution, contrast the differences in flow structure for hypersonic cold flows and those of more energetic but still low enthalpy flows, and compare the present results with experimental measurements for surface heating, pressure, and extent of separation.
The Future of Land-Based Wind Turbine Rotor Technology: The Perspective from NREL
The Big Adaptive Rotor (BAR) Project looks at potential innovative pathways for future land-based wind turbine technology. BAR is led by the National Renewable Energy Laboratory and is sponsored by the Wind Energy Technology Office of the US Department of Energy. This talk will describe innovations and challenges faced by the teams working on BAR. The innovations include numerical models predicting the stability and performance of highly flexible blades, downwind rotors to maximize wind farm power, and controlled bending of 100-meter-long blades during rail transport. The challenges of developing predictive numerical models and formulating successful value propositions supporting new technologies such as distributed aero control devices will also be discussed.