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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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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Data-driven wind turbine wake modeling via probabilistic machine learning

Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions and the interaction between wakes. Physics-based models that capture the wake flow field with high-fidelity are computationally very expensive to perform layout optimization of wind farms, and, thus, data-driven reduced-order models can represent an efficient alternative for simulating wind farms. In this work, we use real-world light detection and ranging (LiDAR) measurements of wind-turbine wakes to construct predictive surrogate models using machine learning. Specifically, we first demonstrate the use of deep autoencoders to find a low-dimensional latent space that gives a computationally tractable approximation of the wake LiDAR measurements. Then, we learn the mapping between the parameter space and the (latent space) wake flow fields using a deep neural network. Additionally, we also demonstrate the use of a probabilistic machine learning technique, namely, Gaussian process modeling, to learn the parameter-space-latent-space mapping in addition to the epistemic and aleatoric uncertainty in the data. Finally, to cope with training large datasets, we demonstrate the use of variational Gaussian process models that provide a tractable alternative to the conventional Gaussian process models for large datasets. Furthermore, we introduce the use of active learning to adaptively build and improve a conventional Gaussian process model predictive capability. Overall, we find that our approach provides accurate approximations of the wind-turbine wake flow field that can be queried at an orders-of-magnitude cheaper cost than those generated with high-fidelity physics-based simulations.

Deep neural networks↗
NASA NTRS1983

Compiling the space shuttle wind tunnel data base: An exercise in technical and managerial innovators

Engineers evaluating Space Shuttle flight data and performance results are using a massive data base of wind tunnel test data. A wind tunnel test data base of the magnitude attained is a major accomplishment. The Apollo program spawned an automated wind tunnel data analysis system called SADSAC developed by the Chrysler Space Division. An improved version of this system renamed DATAMAN was used by Chrysler to document analyzed wind tunnel data and data bank the test data in standardized formats. These analysis documents, associated computer graphics and standard formatted data were disseminated nationwide to the Shuttle technical community. These outputs became the basis for substantiating and certifying the flight worthiness of the Space Shuttle and for improving future designs. As an aid to future programs this paper documents the lessons learned in compiling the massive wind tunnel test data base for developing the Space Shuttle. In particular, innovative managerial and technical concepts evolved in the course of conceiving and developing this successful DATAMAN system and the methods and organization for applying the system are presented.

Kemp, N. D.↗
NASA NTRS1999

Data Corrections and Wind-Tunnel Data Comparisons of a 5% TCA Model in the NASA Ames 12-ft Pressure Tunnel

The objectives of this research are: 1) To determine the effect of geometric variations near the inboard leading-edge flap on high-lift and stability and control performance data; 2) To determine Re effects on TCA (Technology Concept Aircraft) high-lift configuration for optimum high-lift and stability and control performance at takeoff, climbout, approach and landing conditions; and 3) To obtain flow-visualization data on upper surface of wing for CFD validations. This paper is presented in viewgraph form.

Zuniga, Fanny A.↗
DOE OSTI2021

Radar - ESRL Wind Profiler with RASS, Wasco Airport - Raw Data

**Winds** A radar wind profiler measures the Doppler shift of electromagnetic energy scattered back from atmospheric turbulence and hydrometeors along 3-5 vertical and off-vertical point beam directions. Back-scattered signal strength and radial-component velocities are remotely sensed along all beam directions and combined to derive the horizontal wind field over the radar. These data typically are sampled and averaged hourly and usually have 6-m and/or 100-m vertical resolutions up to 4 km for the 915 MHz and 8 km for the 449 MHz systems. **Temperature** To measure atmospheric temperature, a radio acoustic sound system (RASS) is used in conjunction with the wind profile. These data typically are sampled and averaged for five minutes each hour and have a 60-m vertical resolution up to 1.5 km for the 915 MHz and 60-m up to 3.5k m for the 449 MHz.

17 WIND ENERGY↗
DOE OSTI2023

WIND Toolkit Raw Data Reformatted

Wind Integration National Dataset Toolkit The Wind Integration National Dataset (WIND) Toolkit is an update and expansion of the Eastern Wind Integration Data Set and Western Wind Integration Data Set. It supports the next generation of wind integration studies.

17 WIND ENERGY↗
NASA NTRS2000

BOREAS AFM-06 Mean Wind Profile Data

The Boreal Ecosystem-Atmosphere Study (BOREAS) Airborne Fluxes and Meteorology (AFM)-6 team from the National Oceanic and Atmospheric Administration/Environment Technology Laboratory (NOAA/ETL) operated a 915-MHz wind/Radio Acoustic Sounding System (RASS) profiler system in the Southern Study Area (SSA) near the Old Jack Pine (OJP) tower from 21 May 1994 to 20 Sep 1994. The data set provides wind profiles at 38 heights, containing the variables of wind speed; wind direction; and the u-, v-, and w-components of the total wind. The data are stored in tabular ASCII files. The mean wind profile data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Wilczak, James↗
NASA NTRS1987

A study of the feasibility of using sea and wind information from the ERS-1 satellite. Part 1: Wind scatterometer data

The use of scatterometer and altimeter data in wind and wave assimilation, and the benefits this offers for quality assurance and validation of ERS-1 data were examined. Real time use of ERS-1 data was simulated through assimilation of Seasat scatterometer data. The potential for quality assurance and validation is demonstrated by documenting a series of substantial problems with the scatterometer data, which are known but took years to establish, or are new. A data impact study, and an analysis of the performance of ambiguity removal algorithms on real and simulated data were conducted. The impact of the data on analyses and forecasts is large in the Southern Hemisphere, generally small in the Northern Hemisphere, and occasionally large in the Tropics. Tests with simulated data give more optimistic results than tests with real data. Errors in ambiguity removal results occur in clusters. The probabilities which can be calculated for the ambiguous wind directions on ERS-1 contain more information than is given by a simple ranking of the directions.

Anderson, D.↗
DOE OSTI2024

Bias Correcting NOAA's High-Resolution Rapid Refresh (HRRR) Wind Resource Data for Grid Integration Applications [Slides]

Many weather years of high-quality wind data are widely accepted in the grid integration community to be important for studying wind energy technical potential, energy system operations, and grid resilience. NREL makes high-quality wind and solar resource data available. NREL's Grid-Atmosphere workshop (March 2024) identified NREL National Solar Radiation Database as widely used in grid integration modeling, but there is less agreement on commonly used wind datasets. One important factor identified by ESIG's 2023 report 'Weather Dataset Needs for Planning and Analyzing Modern Power Systems' for gold standard wind data is regular updates. To address the need for regular updates, NREL's team can now process all currently available and regularly updated High-Resolution Rapid Refresh (HRRR) outputs. HRRR is an hourly-updated operational forecast product produced by the National Oceanic and Atmospheric Administration (NOAA) (Dowell et al., 2022). One barrier to NREL using HRRR is systematic bias and consistency with NREL's existing wind datasets (e.g. WIND Toolkit, 'WTK') across weather years. To address this barrier, we show that the HRRR can be interpolated and bias-corrected to be consistent with NRE's existing datasets. We call the new dataset BC-HRRR (bias-corrected HRRR). As with historical datasets like the WTK, BC-HRRR is intended for use in grid integration modeling (e.g., capacity expansion, production cost, and resource adequacy modeling). BC-HRRR's (2015-present) consistency with WTK (2007-2013) allows NREL to extend internal grid integration tooling with 15+ weather years of wind data with low-overhead extensibility to future years as they are made available by NOAA. The rest of this slide deck documents the BC-HRRR processing methods, validation, and its implications for intended use.

17 WIND ENERGY↗
NASA NTRS2002

Prediction of Aerodynamic Coefficients for Wind Tunnel Data using a Genetic Algorithm Optimized Neural Network

A fast, reliable way of predicting aerodynamic coefficients is produced using a neural network optimized by a genetic algorithm. Basic aerodynamic coefficients (e.g. lift, drag, pitching moment) are modelled as functions of angle of attack and Mach number. The neural network is first trained on a relatively rich set of data from wind tunnel tests of numerical simulations to learn an overall model. Most of the aerodynamic parameters can be well-fitted using polynomial functions. A new set of data, which can be relatively sparse, is then supplied to the network to produce a new model consistent with the previous model and the new data. Because the new model interpolates realistically between the sparse test data points, it is suitable for use in piloted simulations. The genetic algorithm is used to choose a neural network architecture to give best results, avoiding over-and under-fitting of the test data.

Rajkumar, T.↗