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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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These data are from a short deployment of the UC Davis Halo Streamline scanning lidar. The scanning lidar was deployed 880 m south of the NEON tower and in the same location as the field profiling lidar that was deployed later in spring 2024.
These data are collected 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).
This dataset contains high-frequency vertical velocity recorded by fixed-point vertical scans done by the Lawrence Livermore National Laboratory Halo Streamline scanning lidar at Tree Tops. The quality control is performed according to the algorithm of Goring & Nikora (2002).
This dataset contains high-frequency vertical velocity recorded by fixed-point vertical scans done by the UC Davis Halo Streamline scanning lidar. The quality control is performed according to the algorithm of Goring & Nikora (2002).
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).
These data are from a deployment of the LLNL Halo Streamline scanning lidar in Summer 2025. The scanning lidar was deployed in a clearing area 880 m south of the NEON tower.
These data are from a deployment of the LLNL Halo Streamline scanning lidar in Summer 2025. The scanning lidar was deployed in a clearing area 880 m south of the NEON tower.
Biofuels are anticipated to enable a shift from fossil fuels for renewable transportation and manufacturing fuels, with biohydrogen considered attractive since it could offer the largest reduction of global carbon budgets. Currently, lignocellulosic biohydrogen production remains inefficient with pretreatments that are heavily fossil fuel-dependent. However, bacteria using alkali-treated biomass could streamline biofuel production while reducing costs and fossil fuel needs. An alkaliphilic bacterium, Halanaerobium hydrogeniformans, is described that is capable of biohydrogen production at levels rivaling neutrophilic strains, but at pH 11 and hypersaline conditions. H. hydrogeniformans ferments a variety of 5- and 6-carbon sugars derived from hemicellulose and cellulose including cellobiose, and forms the end products hydrogen, acetate, and formate. Further, it can also produce biohydrogen from switchgrass and straw pretreated at temperatures far lower than any previously reported and in solutions compatible with growth. Hence, this bacterium can potentially increase the efficiency and efficacy of biohydrogen production from renewable biomass resources.
Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.
Fortran computer program for calculating velocities and streamlines for two-dimensional incompressible flow in axial blade rows
Computer program calculates the velocity distribution and streamlines over widely separated blades of turbomachines. It gives the solutions of a two dimensional, subsonic, compressible nonviscous flow problem for a rotating or stationary circular cascade of blades on a blade-to-blade surface of revolution.
FORTRAN program for calculating velocities and streamlines on blade to blade stream surfaces of turbomachinery
Sound propagation in cylindrical pipe with streamlined impedance walls
Integral equations to calculate free streamline flow behind axisymmetric bodies at zero and small angles of attack
Hydrodynamic internal streamline flow analysis for turboprop inducer blades under cavitating and noncavitating conditions
Laminar boundary layer transition, separation and streamline direction on rotating helicopter blades
Computer program for calculating inviscid surface streamlines and heat transfer on space shuttle configurations
A description of the computer program used for heating rate calculation for blunt bodies in hypersonic flow is given. The main program and each subprogram are described by defining the pertinent symbols involved and presenting a detailed flow diagram and complete computer program listing. Input and output parameters are discussed in detail. Listings are given for the computation of heating rates on (1) a blunted 15 deg half-angle cone at 20 deg incidence and Mach 10.6, (2) a blunted 70 deg slab delta wing at 10 deg incidence and Mach 8, and (3) the HL-10 lifting body at 20 deg incidence and Mach 10. In addition, the computer program output for two streamlines on the blunted 15 deg half-angle cone is listed. For Part 1, see N71-36186.