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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 1,567 records · Page 87
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Polarization in the Lagoon nebula

A V-band polarimetric survey of stars associated with the Lagoon nebula was conducted. The data were combined with existing photometric and spectroscopic observations in order to investigate the alignment of magnetic field lines with identifiable symmetry axes and to evaluate the nature of dust in the immediate vicinity. Although stars are not in general highly polarized, electric vectors align with the minor axis of the Lagoon nebula, perpendicular to the major axis of the spatial distribution of massive stars. The observations indicate that the collapse of the molecular cloud progenitor was inhibited along directions perpendicular to magnetic field lines. Considering the low polarization efficiency and the high ratio of total to selective extinction, smaller grains of intranebular dust appear to have been destroyed.

Mccall, Marshall L.↗
NASA NTRS1991

A two-dimensional, time-dependent, near-earth magnetotail

A kinetic approach is presented for obtaining slowly time-dependent, self-consistent plasma, and magnetic field configurations for this region, using anisotropic particle distributions. At present, a 2D configuration is used. The time variation of the plasma/field configuration is assumed to be driven by a slowly varying, externally imposed electric field (representing, for example, a change in solar wind conditions). The numerical approach involves solving a 2D nonlinear Poisson equation for the magnetic vector potential at each time step. The local current density is calculated from particle velocity distributions under the assumption of adiabatic particle motion. The obtained equations of state are compared with those of double-adiabatic theory. Numerical examples of self-consistent responses of the near-earth plasma and field to an externally imposed electric field pulse are presented.

Whipple, Elden↗
NASA NTRS1978

Evidence for a non-random magnetization of the moon

An approximate inversion, using a specific model, of Apollo subsatellite vector magnetometer data has provided estimates for the locations, scale sizes, and magnetization characteristics of strongly magnetized lunar crustal materials across a portion of the lunar far side. The nearly model-independent local bulk directions of magnetization, while otherwise random, exhibit a tendency to lie in or near planes perpendicular to the present lunar spin axis. The apparent existence of such a preferred plane of magnetization must be regarded as unfavorable for proposed local, presumably random sources of large-scale, coherent magnetization and implies that any assumed internal magnetizing dipole or external linear magnetizing field must also have been oriented mainly perpendicular to the present spin axis. The otherwise random orientations of these vectors when projected onto the equatorial plane suggests a simple explanation for the negligible lunar global dipole moment.

Hood, L.↗
DOE OSTI2025

Photon acceleration of high-intensity vector vortex beams into the extreme ultraviolet

Extreme ultraviolet (XUV) light sources allow for the probing of bound electron dynamics on attosecond scales, interrogation of high-energy-density matter, and access to novel regimes of strong-field quantum electrodynamics. Despite the importance of these applications, coherent XUV sources remain relatively rare, and those that do exist are limited in their peak intensity and spatio-polarization structure. Here, we demonstrate that photon acceleration of an optical vector vortex pulse in the moving density gradient of an electron beam–driven plasma wave can produce a high-intensity, tunable-wavelength XUV pulse with the same vector vortex structure as the original pulse. Quasi-3D, boosted-frame particlein- cell simulations show the transition of optical vector vortex pulses with 800-nm wavelengths and intensities below 10 18 W/cm 2 to XUV vector vortex pulses with 36-nm wavelengths and intensities exceeding 10 20 W/cm 2 over a distance of 1.2 cm. The XUV pulses have sub-femtosecond durations and nearly flat phase fronts. The production of such high-quality, high-intensity XUV vector vortex pulses could expand the utility of XUV light as a diagnostic and driver of novel light–matter interactions.

43 PARTICLE ACCELERATORS↗
NASA NTRS1979

Models of an early lunar dynamo

The hypothesis that a dynamo once existed in the moon, and hence was the source of lunar paleomagnetism, is tested using a model for the global thermoremanent magnetization of spherical lithospheres. Various models of an ancient lunar dipole field are used which incorporate intensity variations and reversals, and which are consistent with lunar sample paleointensities. It is found that non-reversing lunar dynamos which have simple exponential decay histories beginning at 4.6 b.y. ago are inconsistent with this limit, unless the moon has been thoroughly demagnetized to a depth of tens of kilometers by impacts or other processes. An early lunar dynamo cannot be excluded by global scalar measurements unless the permanent lunar dipole moment is shown to be significantly less than 10 to the 13th power G/cu cm by future spacecraft measurements, which may be technologically impossible; vector measurements of lunar magnetic anomalies over the whole moon offer a possibility of determining the source of the field which magnetized the lunar crust.

Srnka, L. J.↗
DOE OSTI2024

Search for W′ bosons decaying to a top and a bottom quark in leptonic final states in proton-proton collisions at $ \sqrt{s} $ = 13 TeV

A search for W′ bosons decaying to a top and a bottom quark in final states including an electron or a muon is performed with the CMS detector at the LHC. The analyzed data correspond to an integrated luminosity of 138 fb$^{−1}$ of proton-proton collisions at a center-of-mass energy of 13 TeV. Good agreement with the standard model expectation is observed and no evidence for the existence of the W′ boson is found over the mass range examined. The largest observed deviation from the standard model expectation is found for a W′ boson mass ($ {m}_{{\textrm{W}}^{\prime }} $) hypothesis of 3.8 TeV with a relative decay width of 1%, with a local (global) significance of 2.6 (2.0) standard deviations. Upper limits on the production cross sections of W′ bosons decaying to a top and a bottom quark are set. Left- and right-handed W′ bosons with $ {m}_{{\textrm{W}}^{\prime }} $ below 3.9 and 4.3 TeV, respectively, are excluded at the 95% confidence level, under the assumption that the new particle has a narrow decay width. Limits are also set for relative decay widths up to 30%.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Vectorlike leptons and long-lived bosons at the LHC

We study a renormalizable theory that includes a heavy weak-singlet vectorlike lepton, $\tau$', that decays into a pseudoscalar boson and a tau lepton. We show that this can be the dominant decay mode of $\tau$' provided the pseudoscalar couplings deviate from the case of a Nambu-Goldstone boson. For a range of parameters, the pseudoscalar is long-lived, and mainly decays into a photon pair at one loop, or into a tau pair at tree level. Electroweak production of $\tau$' + $\tau$' - leads to a rich phenomenology at colliders, including signals with many taus or photons. We analyze in detail the LHC signal involving two prompt taus and two displaced photon pairs. Particularly interesting is the case where the pseudoscalar has a decay length of a few meters and thus would typically deposit energy in the muon chambers of the CMS or ATLAS detectors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Beyond the two-point correlation: Constraining primordial non-Gaussianity with density-perturbation moments

Constraining primordial non-Gaussianity (PNG) on the large-scale cosmic structure (LSS) is an important step in understanding properties of the early Universe, specifically in distinguishing between different inflationary models. Measuring PNG relies on evaluating the scale-dependent correlations in the density field. New summary statistics beyond the two- and three-point correlation functions in configuration space and their Fourier-space counterparts, the power- and bispectrum may provide increased sensitivity. We introduce a new method for extracting the PNG signal imprinted on the LSS by using the first three Gaussian moments of the normalized correlation in density perturbations, evaluated on varying distance scales. We aim to assess this method’s sensitivity to local PNG, parameterized by f NL . We performed spherical convolutions on a range of scales on dark-matter-halo simulations to measure the scale-dependent correlations in the density field. From these, we computed the first three moments and compared them to a model expectation vector, parameterized to the second power in f NL . Our method provides about 21% improvement in sensitivity to f NL with respect to using the two-point correlation function alone. Notably, we find that the second moment alone carries nearly as much constraining power as the mean, highlighting the potential of higher order statistics. Given its simplicity and efficiency, this framework is well suited for application to current and upcoming large-scale surveys such as the Dark Energy Spectroscopic Instrument (DESI).

early universe↗
DOE OSTI2021

Spin waves and high-frequency response in layered superconductors with helical magnetic structure

Here, we evaluate the spin-wave spectrum and dynamic susceptibility in a layered superconductor with helical interlayer magnetic structure. We especially focus on the structure in which the moments rotate 90 degrees from layer to layer realized in the iron pnictide RbEuFe 4 As 4 . While in nonmagnetic superconductors low-frequency magnetic field decays on the distance of the order of the London penetration depth, spin waves mediate its propagation to much larger distances limited by external dissipation mechanisms. The spin-wave spectrum in superconductors is strongly renormalized due to the long-range electromagnetic interactions between the oscillating magnetic moments. This leads to strong enhancement of the frequency of the mode coupled with uniform field and this enhancement exists only within a narrow range of the c-axis wave vectors of the order of the inverse London penetration depth. The key feature of materials like RbEuFe 4 As 4 is that this uniform mode corresponds to the maximum frequency of the spin-wave spectrum with respect to the c-axis wave vector. As a consequence, the high-frequency surface resistance acquires a very distinct asymmetric feature spreading between the bare and renormalized frequencies. Finally, we also consider excitation of spin waves with the Josephson effect in a tunneling contact between helical-magnetic and conventional superconductors and study the interplay between the spin-wave features and geometrical cavity resonances in the current-voltage characteristics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Pair-breaking scattering interference as a mechanism for superconducting gap modulation

Here we propose the pair-breaking scattering interference as a general source of coherence peak modulations in superconductors. Assuming this mechanism, we present a simple physical picture for the coherence peak modulations in overdoped cuprate Bi 2 Sr 2 Ca 2 Cu 3 O 10+δ (Bi-2223), ferromagnetic iron pnictide EuRbFe 4 As 4 (Eu-1144), and kagome metals AV 3 Sb 5 (A = K, Rb, and Cs). Specifically, we explain the wave vectors, the particle-hole symmetry, and the dependence on the internal or external Zeeman-field of the coherence peak modulations. This paper is intended as a cautious reminder to the scientific community when asserting the existence of a pair density wave phenomenon in the absence of tunneling conductance modulations in the normal state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Kimberlina 1.2 CCUS Geophysical Models and Synthetic Data Sets

This synthetic multi-scale and multi-physics data set was produced in collaboration with teams at the Lawrence Berkeley National Laboratory, National Energy Technology Laboratory, Los Alamos National Laboratory, and Colorado School of Mines through the Science-informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications (SMART) Initiative. Data are associated with the following publication: Alumbaugh, D., Gasperikova, E., Crandall, D., Commer, M., Feng, S., Harbert, W., Li, Y., Lin, Y., and Samarasinghe, S., “The Kimberlina Synthetic Geophysical Model and Data Set for CO2 Monitoring Investigations”, The Geoscience Data Journal, 2023, DOI: 10.1002/gdj3.191. The dataset uses the Kimberlina 1.2 CO2 reservoir flow model simulations based on a hypothetical CO2 storage site in California (Birkholzer et al., 2011; Wainwright et al., 2013). Geophysical properties models (P- and S-wave seismic velocities, saturated density, and electrical resistivity) were produced with an approach similar to that of Yang et al. (2019) and Gasperikova et al. (2022) for 100 Kimberlina 1.2 reservoir models. Links to individual resources are provided below: [CO2 Saturation Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-co2-saturation-models); Resistivity Models – [part 1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-1), [part 2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-2), and [part 3](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-resistivity-models-part-3); [Vp Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vp-velocity-models); [Vs Velocity Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-vs-velocity-models); [Density Models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-density-models). The 3D distributions of geophysical properties for the 33 time stamps of the SIM001 model were used to generate synthetic seismic, gravity, and electromagnetic (EM) responses for 33 times between zero and 200 years. Synthetic surface seismic data were generated using 2D and 3D finite-difference codes that simulate the acoustic wave equation (Moczo et al., 2007). 2D data were simulated for six point-pressure sources along a 2D line with 10 m receiver spacing and a time spacing of 0.0005 s. 3D simulations were completed for 25 surface pressure sources using a source separation of 1 km in both the x and y directions and a time spacing of 0.001 s. Links to individual resources are provided below: [2D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-velocity-models) and [2D surface seismic data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-2d-surface-seismic-data). [3D velocity models](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-velocity-models), and 3D seismic data [year0](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year0), [year1](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year1), [year2](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year2), [year5](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year5), [year10](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year10), [year15](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year15), [year20](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year20), [year25](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year25), [year30](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year30), [year35](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year35), [year40](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year40), [year45](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year45), [year49](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year49), [year50](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year50), [year51](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year51), [year52](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year52), [year55](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year55), [year60](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year60), [year65](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year65), [year70](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year70), [year75](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year75), [year80](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year80), [year85](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year85), [year90](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year90), [year95](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year95), [year100](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year100), [year110](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year110), [year120](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year120), [year130](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year130), [year140](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year140), [year150](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year150), [year175](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year175), [year200](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-seismic-data-year200). The Python scripts to read these models and data are provided [here](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-python-scripts). EM simulations used a borehole-to-surface survey configuration, with the source located near the reservoir level and receivers on the surface using the code developed by Commer and Newman (2008). Pseudo-2D data for the source at [2500 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz2500m) and [3025 m](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-pseudo-2d-csem-data-tz3025m), used a 2D inline receiver configuration to simulate a response over 3D resistivity models. The [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-3d-csem-data) contain electric fields generated by borehole sources at monitoring well locations and measured over a surface receiver grid. Vector gravity data, both on the surface and in boreholes, were simulated using a modeling code developed by Rim and Li (2015). The simulation scenarios were parallel to those used for the EM: [pseudo-2D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were calculated along the same lines and within the same boreholes, and [3D data](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-gravity-data) were simulated over 3D models on the surface and in three monitoring wells. A series of [synthetic well logs](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-well-logs) of CO2 saturation, acoustic velocity, density, and induction resistivity in the injection well and three monitoring wells are also provided at 0, 1, 2, 5, 10, 15, and 20 years after the initiation of injection. These were constructed by combining the low-frequency trend of the geophysical models with the high-frequency variations of actual well logs collected in the Kimberlina 1 well that was drilled at the proposed site. Measurements of permeability and pore connectivity were made on cores of Vedder Sandstone, which forms the primary reservoir unit: [CT micro scans](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-ct-micro-scans-of-vedder-formation) and [Industrial CT Images](https://edx.netl.doe.gov/dataset/kimberlina-1-2-ccus-geophysical-models-and-synthetic-data-sets-industrial-ct-images-vedder-formation). These measurements provide the range of scales in the otherwise synthetic data set to be as close to a real-world situation as possible. References: Birkholzer, J.T., Zhou, Q., Cortis, A. and Finsterle, S., 2011. A sensitivity study on regional pressure buildup from large-scale CO2 storage projects. Energy Procedia, 4, 4371-4378. Commer, M., and Newman, G.A., 2008. New advances in three-dimensional controlled-source electromagnetic inversion, Geophysical Journal International, 172, 513-535. Gasperikova, E., Appriou, D., Bonneville, A., Feng, Z., Huang, L., Gao, K., Yang, X., Daley, T., 2022, Sensitivity of geophysical techniques for monitoring secondary CO2 storage plumes, Int. J. Greenh. Gas Control, Volume 114, 103585, ISSN 1750-5836, https://doi.org/10.1016/j.ijggc.2022.103585. Moczo, P., J.O. Robertsson and L. Eisner, 2007, The finite-difference time-domain method for modeling of seismic wave propagation: Advances in geophysics, 48, 421-516. Rim, H., and Y. Li, 2015, Advantages of borehole vector gravity in density imaging, Geophysics, 80, G1-G13. Wainwright, H. M.; Finsterle, S.; Zhou, Q.; Birkholzer, J. T., 2013. Modeling the Performance of Large-Scale CO2 Storage Systems: A Comparison of Different Sensitivity Analysis Methods. International Journal of Greenhouse Gas Control, 17, 189205. https://doi.org/10.1016/j.ijggc.2013.05.007, DOI: 10.18141/1603331. Yang, X., Buscheck, T.A., Mansoor, K., Wang, Z., Gao, K., Huang, L., Appriou, D., and Carroll, S.A., 2019. Assessment of geophysical monitoring methods for detection of brine and CO2 leakage in drinking water aquifers, International Journal of Greenhouse Gas Control, 90, 102803, https://doi.org/10.1016/j.ijggc.2019.102803.

CCUS↗
NASA NTRS1973

Application of the scalar and vector potentials to the aerodynamics of jets

The applicability of a method based on the Stokes potentials (vector and scalar potentials) to computations associated with the aerodynamics of jets was examined. The aerodynamic field near the nozzle could be represented and that the influence of a nonuniform velocity profile at the nozzle exit plane could be determined. Also computations were made for an axisymmetric jet exhausting into a quiescient atmosphere. The velocity at the axis of the jet, and the location of the half-velocity points along the jet yield accurate aerodynamic field computations. Inconsistencies among the different theoretical characterizations of jet flowfields are shown.

Russell, H. L.↗
NASA NTRS1979

Earth and Jupiter: Comparison of magnetopause structure

Using published data from the Jovian magnetopause crossings of Pioneers 10 and 11, the apparent crossing times as determined from the solar wind plasma detector and from the vector magnetometer are comapred. There were 13 published crossings for which both plasma and magnetic-field signatures were clearly defined. The occurrence distribution of these 13 crossings with respect to the relative timing of plasma and field signatures is shown. Results indicate that Jupiter's magnetopause is more turbulent than the Earth's possibly as the result of the large velocity shear between the magnetosheath and the partially corotating Jovian plasma. However, it is noted that the data set considered is too small to justify a definitive interpretation.

Reiff, P. H.↗
NASA NTRS1989

Analysis of airborne Doppler lidar measurements of the extended California sea breeze

Airborne Doppler lidar data obtained by NASA near the top of the PBL in the central valley of California are analyzed. The experimental conditions and quality of the data are described. Wind vectors are produced on a geographic grid divided into 1-km square cells. The resulting wind field has features similar to those seen in ground level wind observations. It is suggested that, although the analysis is labor intensive and cumbersome, it produces a previously unattainable high-resolution depiction of air flow over a broad region.

Carroll, John J.↗
NASA NTRS1991

A new method for determining which stars are near a star sensor field-of-view

A new method is described for determining which stars in a navigation star catalog are near a star sensor field of view (FOV). This method assumes that an estimate of spacecraft inertial attitude is known. Vector component ranges for the star sensor FOV are computed, so that stars whose vector components lie within these ranges are near the star sensor FOV. This method requires no presorting of the navigation star catalog, and is more efficient than tradition methods.

Yates, Russell E., Jr.↗
NASA NTRS1991

Three dimensional numerical investigation of gravitational and solutal effects in a cylindrical cell

Three-dimensional numerical computations of natural convection in a cylindrical ampoule (L/R = 4) are presented for a Ga-doped Ge melt. The ampoule is maintained at isothermal end conditions, and the g vector is oriented at an angle gamma with the cylinder axis. Detailed velocity, temperature, and concentration field distributions are presented for different gravity levels and a range of inclination angles (gamma between 0 and 180 deg). For terrestrial conditions, complex, multicellular flow is found to occur for gamma between 0 and 180 deg. The strong convection results in significant isotherm distortions and enhanced heat transfer. As the gravity level is reduced to 0.001 g(0), the overall convection strength decays but the 3D multicellular flow persists, causing appreciable mass transfer. For a further reduction in the gravity level to 0.00001 g(0), the system thermo/solutal characteristics are close to a purely diffusive behavior. Flow and thermal and solutal distributions are presented in the principal, orthogonal, and cross sectional planes, and the average system heat and mass transfer are calculated.

Ramachandran, N.↗
NASA NTRS1998

Polar Hydra Data Analysis

The science activities are: 1) Hydra is still operating successfully on orbit. 2) A large amount of analysis and discovery has occurred with the Hydra ground data processing this past year. 3) Full interdetector calibration has been implemented and documented. This intercalibration was necessitated by the incorrect installation of bias resistors in the pre-acceleration stage to the electron channeltrons. This had the effect of making the counting efficiency for electrons energy dependent as well as channeltron specific. The nature of the error had no impact on the ion detection efficiency since they have a different bias arrangement. This intercalibration is so effective, that the electron and ion moment densities are routinely produced with a level of agreement better than 20%. 4) The data processing routinely removes glint in the sensors and produces public energy time spectrograms on the web overnight. 6) Routine, but more intensive computer processing codes are operational that determine for electrons and ions, the density, the flow vector, the pressure tensor and the heat flux by numerical integration. These codes use the magnetic field to sustain the quality of their output. To gain access to this high quality magnetic field within our data stream we have monitored Russell's web page for zero levels and timing files (since his data acquisition is not telemetry synchronous) and have a local reconstruction of B for our use. We have also detected a routine anomaly in the magnetometer data stream that we have documented to Chris Russell and developed an editing algorithm to intercept these "hits" and remove them from the geophysical analysis.

Scudder, J. D.↗