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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Tree Tops Site - Halo Streamline Scanning Lidar High-Frequency Wind Profile / Derived data

This dataset contains wind profiles retrieved from 6-beam Velocity Azimuth Display (VAD) scans done by a Streamline XR Doppler Lidar operated by Lawrence Livermore National Laboratory and deployed at the Tree Tops site (1.5 km South-West of MLBS site). The wind components (expressed as zonal, meridional and vertical) are retrieved through the algorithm of Paschke et al. (2015). The quality control of the radial wind speed is performed following the algorithm of Foken et al. (2004).

17 WIND ENERGY↗
DOE OSTI2025

MLBS Site - Halo Scanning Lidar High-frequency Wind Profile / Derived data

This dataset contains wind profiles retrieved from 6-beam Velocity Azimuth Display (VAD) scans done by a Streamline XR Doppler Lidar operated by the University of Virginia and deployed at the MLBS site. The wind components (expressed as zonal, meridional and vertical) are retrieved through the algorithm of Paschke et al. (2015). The quality control of the radial wind speed is performed following the algorithm of Foken et al. (2004).

17 WIND ENERGY↗
DOE OSTI2025

WREF Halo Streamline scanning lidar / Derived data / High-frequency wind profiles

This dataset contains wind profiles retrieved from 6-beam Velocity Azimuth Display (VAD) scans done by the UC Davis scanning Lidar. The wind components (expressed as zonal, meridional and vertical) are retrieved through the algorithm of Paschke et al. (2015). The quality control of the radial wind speed is performed following the algorithm of Goring & Nikora (2002).

17 WIND ENERGY↗
DOE OSTI2025

Site E - Virtual Tower High-Frequency Wind Velocity / Derived Data

This dataset contains Dual-Doppler wind reconstruction from stare lidar scans two rotor diameters south of the King Plains G02 turbine. The line-of-sight velocity data can be found in sa5.lidar.z03.b0 and sa1.lidar.z03.b0. The reconstruction is done neglecting the contribution from vertical velocity.

17 WIND ENERGY↗
DOE OSTI2021

Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data

Automatic boundary detection of 4D ultrasound (4DUS) cardiac data is a promising yet challenging application at the intersection of machine learning and medicine. Using recently developed murine 4DUS cardiac imaging data, we demonstrate here a set of three machine learning models that predict left ventricular wall kinematics along both the endo- and epi-cardial boundaries. Each model is fundamentally built on three key features: (1) the projection of raw US data to a lower dimensional subspace, (2) a smoothing spline basis across time, and (3) a strategic parameterization of the left ventricular boundaries. Model 1 is constructed such that boundary predictions are based on individual short-axis images, regardless of their relative position in the ventricle. Model 2 simultaneously incorporates parallel short-axis image data into their predictions. Model 3 builds on the multi-slice approach of model 2, but assists predictions with a single ground-truth position at end-diastole. To assess the performance of each model, Monte Carlo cross validation was used to assess the performance of each model on unseen data. For predicting the radial distance of the endocardium, models 1, 2, and 3 yielded average R2 values of 0.41, 0.49, and 0.71, respectively. Monte Carlo simulations of the endocardial wall showed significantly closer predictions when using model 2 versus model 1 at a rate of 48.67%, and using model 3 versus model 2 at a rate of 83.50%. These finding suggest that a machine learning approach where multi-slice data are simultaneously used as input and predictions are aided by a single user input yields the most robust performance. Subsequently, we explore the how metrics of cardiac kinematics compare between ground-truth contours and predicted boundaries. We observed negligible deviations from ground-truth when using predicted boundaries alone, except in the case of early diastolic strain rate, providing confidence for the use of such machine learning models for rapid and reliable assessments of murine cardiac function. To our knowledge, this is the first application of machine learning to murine left ventricular 4DUS data. Future work will be needed to strengthen both model performance and applicability to different cardiac disease models.

4D ultrasound↗