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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This short report provides a time-sensitive update on the 20-year wind resource data set (named “CA20”) produced in 2020 by the National Renewable Energy Laboratory (NREL) for the outer continental shelf (OCS) off the coast of California.
Deployment off the coast of Hawaii
Permanent magnets today are used in a wide range of transportation, industrial, residential/commercial, consumer electronics, defense, domestic, data storage, wind energy, and medical markets and applications. There are five classes of commercial permanent magnet materials; however, magnets based on Nd-Fe-B account for over 60% of the global magnet production by value. They typically contain around 31 wt.% of rare earth elements (REEs), principally, Nd and Pr, plus Dy for higher-temperature performance. Nd-Fe-B magnets are forecast to grow throughout this decade, largely driven by the growth in electric vehicles of all types. However, several studies forecast a shortfall of the primary REEs from mined resources. In this paper, the sourcing, processing, and recycling of REEs are discussed. Additionally, presented are the advantages and disadvantages of the major recovery and recycling technologies for REEs.
We revisit the theory of stochastic heating of ions and investigate its phase-space signatures in kinetic turbulence of relevance to low- β portions of the solar wind. In particular, we retain a full scale-dependent approach in our treatment, and we explicitly consider the case in which electric-field fluctuations can be described by a generalized Ohm's law that includes Hall and thermoelectric effects. These two electric-field terms provide the dominant contributions to stochastic ion heating when the ion-Larmor scale is much smaller than the ion skin depth, ρ i << d i , which is the case at β << 1. Employing well-known spectral scaling laws for Alfvén-wave and kinetic-Alfvén-wave turbulent fluctuations, we obtain scaling relations characterizing the field-perpendicular particle-energization rate and energy diffusion coefficient associated with stochastic heating in these two regimes. Phase-space signatures of ion heating are then investigated using three-dimensional hybrid-kinetic simulations of continuously driven Alfvénic turbulence at low β (namely, β i = β e = 0.3 and β i = β e = 1/9). In these simulations, energization of ions parallel to the magnetic field is subdominant compared to its perpendicular counterpart ( Q ∥,i << Q ⊥,i ), and the fraction of turbulent energy that goes into ion heating is ≈75% at β i = 0.3 and ≈40% at β i ≃ 0.1. The phase-space signatures of ion energization are consistent with Landau-resonant collisionless damping and a ( β -dependent) combination of ion-cyclotron and stochastic heating. We demonstrate good agreement between our scale-dependent theory and various signatures associated with the stochastic portion of the heating. We discuss briefly the effect of intermittency on stochastic heating and the implications of our work for the interpretation of stochastic heating in solar-wind spacecraft data.
Low-cost simulations providing accurate predictions of transport of airborne material in urban areas, vegetative canopies, and complex terrain are demanding because of the small-scale heterogeneity of the features influencing the mean flow and turbulence fields. Common models used to predict turbulent transport of passive scalars are based on the Lagrangian stochastic dispersion model. The Quick Environmental Simulation (QES) tool is a low-computational-cost framework developed to provide high-resolution wind and concentration fields in a variety of complex atmospheric-boundary-layer environments. Part of the framework, QES-Plume, is a Lagrangian dispersion code that uses a time-implicit integration scheme to solve the generalized Langevin equations which require mean flow and turbulence fields. Here, QES-Plume is driven by QES-Winds, a 3D fast-response model that computes mass-consistent wind fields around buildings, vegetation, and hills using empirical parameterizations, and QES-Turb, a local-mixing-length turbulence model. In this paper, the particle dispersion model is presented and validated against analytical solutions to examine QES-Plume’s performance under idealized conditions. In particular, QES-Plume is evaluated against a classical Gaussian plume model for an elevated continuous point-source release in uniform flow, the Lagrangian scaling of dispersion in isotropic turbulence, and a non-Gaussian plume model for an elevated continuous point-source release in a power-law boundary-layer flow. In these cases, QES-Plume yields a maximum relative error below 6 % when compared with analytical solutions. In addition, the model is tested against wind-tunnel data for a uniform array of cubical buildings. QES-Plume exhibits good agreement with the experiment with 99 % of matched zeros and 59 % of the predicted concentrations falling within a factor of 2 of the experimental concentrations. Furthermore, results also emphasize the importance of using high-quality turbulence models for particle dispersion in complex environments. Finally, QES-Plume demonstrates excellent computational performance.
Wake steering is a wind farm control strategy in which upstream turbines operate with a yaw misalignment to deflect their wakes away from downstream turbines, yielding a net power gain for the wind plant. But the inability of wake steering controllers to perfectly track the wind direction leads to suboptimal performance. In this paper, we propose the use of preview wind direction measurements upstream of the turbine to improve controller performance by anticipating wind direction changes. Further, data from an operational wind plant are used to determine realistic preview measurement accuracy. Using the FLORIS engineering wind farm control tool, we compare the performance of standard and preview-enabled baseline and wake steering control for a two-turbine array during below-rated operation. Assuming perfect preview information, preview-based wake steering increases energy production by the equivalent of 9.5% of the baseline wake losses, compared to a wake loss recovery of 5.6% with standard wake steering. However, when realistic measurement accuracy is included, the preview-based controller provides no advantage over standard control, motivating the need for more sophisticated control and wind direction forecasting strategies.
Field data collected from the NREL/GE 1.5MW wind turbine and met tower at the NREL Wind Technology Center near Boulder, Colorado from June-October 2018 were analyzed to quantify the impacts of turbulence eddies on the load responses measured from sensors on the main shaft, blade and tower. The passage of individual mountain-generated eddies from the met tower to the wind turbine were critically determined by correlating the optimal time shifts in signal between met tower and nacelle anemometers with mean advection time. Loading responses from mountain eddy passage were compared with atmospheric eddies from the north/south, unimpeded by the mountains, and found to be similar. Whereas time variations in torque were highly correlated with time changes in horizontal eddy velocity, the out-of-plane bending moments on the main shaft (directly forcing the main bearing) were uncorrelated with horizontal eddy velocity. This result is consistent with a previous LES study indicating that the main bearing is forced by asymmetrical interactions between the rotor and turbulence eddies, while power fluctuations respond primarily to advective eddy velocity. Surprisingly, the nacelle anemometer produced statistics very similar to the met tower.
Deployment off the coast of Morro Bay, California
This dataset contains simulation data of a twelve turbine wind farm simulation performed with AMR-Wind. The simulation is documented in Kuhn, M. B., Henry de Frahan, M. T., Mohan, P., Deskos, G., Churchfield, M., Cheung, L., ... & Sprague, M. (2025). AMR‐Wind: A Performance‐Portable, High‐Fidelity Flow Solver for Wind Farm Simulations. Wind Energy, 28(5), e70010 (https://doi.org/10.1002/we.70010).
This study reports the results of the second round of analyses of the Offshore Code Comparison, Collaboration, Continued, with Correlation and unCertainty (OC6) project Phase III. While the first round investigated rotor aerodynamic loading, here, focus is given to the wake behavior of a floating wind turbine under large motion. Wind tunnel experimental data from the UNsteady Aerodynamics for FLOating Wind (UNAFLOW) project are compared with the results of simulations provided by participants with methods and codes of different levels of fidelity. The effect of platform motion on both the near and the far wake is investigated. More specifically, the behavior of tip vortices in the near wake is evaluated through multiple metrics, such as streamwise position, core radius, convection velocity, and circulation. Additionally, the onset of velocity oscillations in the far wake is analyzed because this can have a negative effect on stability and loading of downstream rotors. Results in the near wake for unsteady cases confirm that simulations and experiments tend to diverge from the expected linearized quasi-steady behavior when the rotor reduced frequency increases over 0.5. Additionally, differences across the simulations become significant, suggesting that further efforts are required to tune the currently available methodologies in order to correctly evaluate the aerodynamic response of a floating wind turbine in unsteady conditions. Regarding the far wake, it is seen that, in some conditions, numerical methods overpredict the impact of platform motion on the velocity fluctuations. Moreover, results suggest that the effect of platform motion on the far wake, differently from original expectations about a faster wake recovery in a floating wind turbine, seems to be limited or even oriented to the generation of a wake less prone to dissipation.
This report was prepared for the Wind Energy Technology Office for the FY 2022, quarter 2 deliverable. This details the use of dynamic line rating technology to rate a series of gen tie lines connecting wind plants to the regional transmission lines. This consists of two primary study regions, the first on the desert west of Idaho Falls, and the second in the region east of the Cascades along the Columbia River Gorge. The TREAD program that was developed at INL was used to create generated gen-tie lines based on nearby regional transmission line connections. The capacity availability of the gen tie lines are compared with the power production of the wind farms. Overall, the INL Site location shows a much greater capacity for the gen-tie lines due to the higher wind speeds. Across both locations, the HRRR data shows higher wind speeds than observed at the observational weather stations. For both the Columbia Gorge and Idaho areas, the sites show that a statically rate gen-tie could carry additional capacity far above the rated during periods of high wind due to the concurrent cooling effects.
Quantifying the impacts on turbine loads during wind farm control is an important consideration in assessing power production benefits. Wake steering controls aim to improve total wind farm performance by coordinating the control actions of individual turbines, wherein an upstream turbine is intentionally yawed at an offset angle from the measured wind direction. Consequently, this redirects its wake for improved power production and potentially reduces fatigue loads of the downstream turbines. This paper studies the lifetime fatigue loads associated with wake steering by using utility-scale wind turbine experimental data to conduct an analysis on a pair of wind turbines. This study was part of a large field experiment in which a group of five GE 1.5-MW SLE CWE turbines were selected as targets for conducting wake steering research. A standard loads instrumentation package and data acquisition system were installed on two turbines within the cluster to measure turbine fundamental loads. The time-series databases were used to calculate loads statistics as well as short-term and lifetime damage equivalent loads. Fatigue calculations followed the guidance in Annex H of the International Electrotechnical Commission standard 61400-1, Edition 4. Lifetime fatigue calculation results are presented in this analysis; three methods of assessing lifetime fatigue were used to determine percent difference for blade root moments, main shaft moments, main shaft torque, and tower top torque. For all three fatigue treatments, some components’ lifetime fatigue increases for the controlled turbine; however, the downwind turbine experienced a reduction in lifetime fatigue and combined effect for the turbine pair results in a reduction of fatigue when wake steering controls are applied.
Condition-based maintenance using routinely collected Supervisory Control and Data Acquisition (SCADA) data is a promising strategy to reduce downtime and costs associated with wind farm operations and maintenance. New approaches are continuously being developed to improve the condition monitoring for wind turbines. Development of normal behaviour models is a popular approach in studies using SCADA data. This paper first presents a data-driven framework to apply normal behaviour models using an artificial neural network approach for wind turbine gearbox prognostics. A one-class support vector machine classifier, combining different error parameters, is used to analyse the normal behaviour model error to develop a robust threshold to distinguish anomalous wind turbine operation. A detailed sensitivity study is then conducted to evaluate the potential of using high-frequency SCADA data for wind turbine gearbox prognostics. The results based on operational data from one wind turbine show that, compared to the conventionally used 10-min averaged SCADA data, the use of high-frequency data is valuable as it leads to improved prognostic predictions. High-frequency data provides more insights into the dynamics of the condition of the wind turbine components and can aid in earlier detection of faults.
This dataset provides fast response wind and virtual sonic temperature data.
This dataset provides fast response wind and virtual sonic temperature data.
This dataset provides fast response wind and virtual sonic temperature data.
This dataset provides fast response wind and virtual sonic temperature data.
This dataset provides fast response wind and virtual sonic temperature data.