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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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NASA NTRS1984

Remote sensing of the marginal ice zone during Marginal Ice Zone Experiment (MIZEX) 83

The remote sensing techniques utilized in the Marginal Ice Zone Experiment (MIZEX) to study the physical characteristics and geophysical processes of the Fram Strait Region of the Greenland Sea are described. The studies, which utilized satellites, aircraft, helicopters, and ship and ground-based remote sensors, focused on the use of microwave remote sensors. Results indicate that remote sensors can provide marginal ice zone characteristics which include ice edge and ice boundary locations, ice types and concentration, ice deformation, ice kinematics, gravity waves and swell (in the water and the ice), location of internal wave fields, location of eddies and current boundaries, surface currents and sea surface winds.

Shuchman, R. A.↗
NASA NTRS1999

Climate Change and the Brewer-Dobson Circulation

Using GCM (General Circulation Model) simulations of the troposphere-stratosphere circulation in different climate regimes, the sensitivity of the Brewer-Dobson Circulation to climate change will be discussed. Included will be simulations of the stratosphere with increasing/doubled CO2 as done with different versions of the GISS (Goddard Institute for Space Studies) Global Climate Middle Atmosphere Model, with and without ozone response. To put the results in a broader perspective, stratospheric circulation changes from several paleoclimate simulations will also be presented, including the Last Glacial Maximum and the Paleocene, each with several different (plausible) latitudinal sea surface temperature gradients. Hence the climate changes investigated will include strong variations in baroclinic and topographic tropospheric planetary wave and gravity wave forcing, as well as global mean temperature and atmospheric CO2 levels. Results will be analyzed in terms of wave driving via E-P flux convergences and gravity wave effects, and will be shown to be strongly dependent on wave propagation characteristics.

Rind, David↗
NASA NTRS1999

Climate Change and the Brewer-Dobson Circulation

Using GCM simulations of the troposphere-stratosphere circulation in different climate regimes, the sensitivity of the Brewer-Dobson Circulation to climate change will be discussed. Included will be simulations of the stratosphere with increasing/doubled C02 as done with different versions of the GISS Global Climate Middle Atmosphere Model, with and without ozone response. To put the results in a broader perspective, stratospheric circulation changes from several paleoclimate simulations will also be presented, including the Last Glacial Maximum and the Paleocene, each with several different (plausible) latitudinal sea surface temperature gradients. Hence the climate changes investigated will include strong variations in baroclinic and topographic tropospheric planetary wave and gravity wave forcing, as well as global mean temperature and atmospheric C02 levels. Results will be analyzed in terms of wave driving via E-P flux convergences and gravity wave effects, and will be shown to be strongly dependent on wave propagation characteristics.

Rind, David↗
NASA NTRS2003

WVD2 and WDL1 modulate helical organ growth and anisotropic cell expansion in Arabidopsis

Wild-type Arabidopsis roots develop a wavy pattern of growth on tilted agar surfaces. For many Arabidopsis ecotypes, roots also grow askew on such surfaces, typically slanting to the right of the gravity vector. We identified a mutant, wvd2-1, that displays suppressed root waving and leftward root slanting under these conditions. These phenotypes arise from transcriptional activation of the novel WAVE-DAMPENED2 (WVD2) gene by the cauliflower mosaic virus 35S promoter in mutant plants. Seedlings overexpressing WVD2 exhibit constitutive right-handed helical growth in both roots and etiolated hypocotyls, whereas the petioles of WVD2-overexpressing rosette leaves exhibit left-handed twisting. Moreover, the anisotropic expansion of cells is impaired, resulting in the formation of shorter and stockier organs. In roots, the phenotype is accompanied by a change in the arrangement of cortical microtubules within peripheral cap cells and cells at the basal end of the elongation zone. WVD2 transcripts are detectable by reverse transcriptase-polymerase chain reaction in multiple organs of wild-type plants. Its predicted gene product contains a conserved region named "KLEEK," which is found only in plant proteins. The Arabidopsis genome possesses seven other genes predicted to encode KLEEK-containing products. Overexpression of one of these genes, WVD2-LIKE 1, which encodes a protein with regions of similarity to WVD2 extending beyond the KLEEK domain, results in phenotypes that are highly similar to wvd2-1. Silencing of WVD2 and its paralogs results in enhanced root skewing in the wild-type direction. Our observations suggest that at least two members of this gene family may modulate both rotational polarity and anisotropic cell expansion during organ growth.

NASA Discipline Plant Biology↗
NASA NTRS1991

Response of gravity level fluctuations on the Gravity Probe-B Spacecraft propellant system

The dynamical behavior of fluids, in particular the effect of surface tension on partially-filled fluids in a rotating dewar under microgravity environment have been investigated. Results show that there is a group of wave trains, both in longitudinal and transverse modes, with various frequencies and wavelengths of slosh waves generated by the restoring force field of gravity jitters and centrifugal forces in this study. The longest wave periods of slosh waves, either the longitudinal or transverse modes, are responsible for the production of wave modes with highest ratio of maximum wave amplitude to wavelength. Also, the lower frequency slosh waves are the wave modes with higher wave energy than that of the higher frequency slosh waves.

Hung, R. J.↗
NASA NTRS1992

Effect of asymmetric gravity jitter excited slosh waves at liquid-vapor interface under microgravity

The dynamical behavior of fluids affected by the asymmetric gravity jitter oscillations, in particular the effect of surface tension on partially-filled rotating fluids (cryogenic liquid helium and helium vapor) in a sub-scale Gravity Probe-B Spacecraft propellant dewar tank imposed by time-dependent various directions of background gravity environment have been investigated. Results show that lower frequency gravity jitter imposed on the time-dependent variations of the direction of background gravity induced a greater amplitude of oscillations and a stronger degree of asymmetry in liquid-vapor interface geometry than that made by the higher frequency gravity jitter. Furthermore, the greater the components of background gravity in radial and circumferential directions will provide a greater contribution in driving more to the increasing amplitude and degrees of symmetry of liquid-vapor interface profiles which, in turn, modify the disturbance of moment of inertia and angular momentum of spacecraft.

Hung, R. J.↗
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↗
DOE OSTI2025

Synoptic and Mesoscale Modulation of Dynamic and Thermodynamic Impacts on Central Arctic Sea Ice During MOSAiC

The primary objectives of this proposed project are to determine 1) to what extent dynamic and thermodynamic air‐ice interactions in the Central Arctic are driven by atmospheric synoptic and mesoscale phenomena, and 2) the key processes involved in these interactions. These phenomena include large‐scale features such as fronts, high‐ and low‐pressure systems, low‐level jets, and cloud bands, but may also include smaller‐scale phenomena such as mesoscale instabilities (e.g., symmetric instability or slantwise convection), transient meso‐lows and gravity waves. Dynamic air‐ice interactions include direct atmospheric stress and atmospheric stress divergence/deformation; thermodynamic air‐ice interactions involve the net surface energy budget, especially in association with non‐stratocumulus clouds generally occurring with stronger dynamical forcing.

58 GEOSCIENCES↗
NASA NTRS1987

Measuring ocean waves from space; Proceedings of the Symposium, Johns Hopkins University, Laurel, MD, Apr. 15-17, 1986

Papers are presented on ocean-wave prediction; the quasi-universal form of the spectra of wind-generated gravity waves at different stages of their development; the limitations of the spectral measurements and observations of the group structure of surface waves; the effect of swell on the growth of wind wave; operational wave forecasting; ocean-wave models, and seakeeping using directional wave spectra. Consideration is given to microwave measurements of the ocean-wave directional spectra; SIR research; estimating wave energy spectra from SAR imagery, with the radar ocean-wave spectrometer, and SIR-B; the wave-measurement capabilities of the surface contour radar and the airborne oceanographic lidar; and SIR-B ocean-wave enhancement with fast-Fourier transform techniques. Topics discussed include wave-current interaction; the design and applicability of Spectrasat; the need for a global wave monitoring system; the age and source of ocean swell observed in Hurricane Josephine; and the use of satellite technology for insulin treatment.

Beal, Robert C.↗
NASA NTRS2021

Compact Thermal Imager (CTI) for Atmospheric Remote Sensing

The demonstration of a newly developed compact thermal imager (CTI) on the International Space Station (ISS) has provided not only a technology advancement but a rich high-resolution dataset on global clouds, atmospheric and land emissions. This study showed that the free-running CTI instrument could be calibrated to produce scientifically useful radiance imagery of the atmosphere, clouds, and surfaces with a vertical resolution of ~460 m at limb and a horizontal resolution of ~80 m at nadir. The new detector demonstrated an excellent sensitivity to detect the weak limb radiance perturbations modulated by small-scale atmospheric gravity waves. The CTI’s high-resolution imaging was used to infer vertical cloud temperature profiles from a side-viewing geometry. For nadir imaging, the combined high-resolution and high-sensitivity capabilities allowed the CTI to better separate cloud and surface emissions, including those in the planetary boundary layer (PBL) that had small contrast against the background surface. Finally, based on the ISS’s orbit, the stable detector performance and robust calibration algorithm produced valuable diurnal observations of cloud and surface emissions with respect to solar local time during May–October 2019, when the CTI had nearly continuous operation.

thermal imager↗
NASA NTRS1973

Venus - Radar determination of gravity potential.

We describe a method for the determination of the gravity potential of Venus from multiple-frequency radar measurements. The method is based on the strong frequency dependence of the absorption of radio waves in Venus' atmosphere. Comparison of the differing radar reflection intensities at several frequencies yields the height of the surface relative to a reference pressure contour; combination with measurements of round-trip echo delays allows the pressure, and hence the gravity potential contour, to be mapped relative to the mean planet radius. Since calibration data from other frequencies are unavailable, the absorption-sensitive Haystack Observatory data have been analyzed under the assumption of uniform surface reflectivity to yield a gravity equipotential contour for the equatorial region and a tentative upper bound of .0006 on the fractional difference of Venus' principal equatorial moments of inertia.

Shapiro, I. I.↗
NASA NTRS1991

Impact cratering calculations

In the course of carrying out finite difference calculations, it was discovered that for large craters, a previously unrecognized type of crater (diameter) growth occurred which was called lip wave propagation. This type of growth is illustrated for an impact of a 1000 km (2a) silicate bolide at 12 km/sec (U) onto a silicate half-space at earth gravity (1 g). The von Misses crustal strength is 2.4 kbar. The motion at the crater lip associated with this wave type phenomena is up, outward, and then down, similar to the particle motion of a surface wave. It is shown that the crater diameter has grown d/a of approximately 25 to d/a of approximately 4 via lip propagation from Ut/a = 5.56 to 17.0 during the time when rebound occurs. A new code is being used to study partitioning of energy and momentum and cratering efficiency with self gravity for finite-sized objects rather than the previously discussed planetary half-space problems. These are important and fundamental subjects which can be addressed with smoothed particle hydrodynamic (SPH) codes. The SPH method was used to model various problems in astrophysics and planetary physics. The initial work demonstrates that the energy budget for normal and oblique impacts are distinctly different than earlier calculations for silicate projectile impact on a silicate half space. Motivated by the first striking radar images of Venus obtained by Magellan, the effect of the atmosphere on impact cratering was studied. In order the further quantify the processes of meteor break-up and trajectory scattering upon break-up, the reentry physics of meteors striking Venus' atmosphere versus that of the Earth were studied.

Ahrens, Thomas J.↗
NASA NTRS1992

Asymmetric gravity jitter excited slosh waves at a liquid-vapor-solid interface in microgravity

The dynamical behavior of fluids affected by the asymmetric gravity jitter oscillations is investigated focusing on the surface tension effect on partially filled rotating fluids in a sub-scale gravity probe-B spacecraft propellant dewar tank. Data obtained revealed that the lower frequency gravity jitter imposed on the time-dependent variations of the background gravity direction induced a greater amplitude of oscillations and a stronger degree of asymmetry in liquid-vapor interface geometry than that caused by the higher frequency gravity jitter. It is also found that the greater the components of background gravity in radial and circumferential directions the greater the contribution to driving more toward increasing amplitude and degrees of asymmetry of the liquid-vapor interface profiles, which in turn modify the disturbance of moment of inertia and angular momentum of spacecraft.

Hung, R. J.↗
NASA NTRS1982

Recent advances in remote sensing; Proceedings of the First International Geoscience and Remote Sensing Symposium, Washington, DC, June 8-10, 1981

The state of the art in remote sensing of the earth and the planets was discussed in terms of sensor performance, signal processing, and data interpretation. Particular attention was given to lidar for characterizing atmospheric particulates, the modulation of short waves by long ocean gravity waves, and runoff modeling for snow-covered areas. The use of NOAA-6 spacecraft AVHRR data to explore hydrologic land surface features, the effects of soil moisture and vegetation canopies on microwave and thermal microwave emissions, and regional scale evapotranspiration rate determination through satellite IR data are examined. A Shuttle experiment to demonstrate high accuracy global time and frequency transfer is described, along with features of the proposed Gravsat, radar image processing for rock-type discrimination, and passive microwave sensing of temperature and salinity in coastal zones.

Mcintosh, R.↗
NASA NTRS2000

Downward Link of Solar Activity Variations Through Wave Driven Equatorial Oscillations (QBO and SAO)

Signatures of the 11-year solar activity/irradiance cycle are observed in the Quasi Biennial Oscillation (QBO) of the lower stratosphere. At these altitudes, the QBO is understood to be the result of "downward control" exerted by the wave mean flow interactions that drive the phenomenon. It is reasonable then to speculate that the QBO is a natural conduit to lower altitudes of solar activity variations in radiance (SAV). To test this hypothesis, we conducted experiments with a 2D version of our Numerical Spectral Model that incorporates Hines' Doppler Spread Parameterization for small-scale gravity waves (GW). To account for the SAV, we change the solar heating rate on a logarithmic scale from 0.1% at the surface to 1% at 50 kin to 10% at 100 km. With the same GW flux, we then conduct numerical experiments to evaluate the magnitude of the solar activity irradiance effect (SAE) on the zonal circulation at low latitudes. The numerical results obtained show that, under certain conditions, the SAE is significant in the zonal circulation and does extend to lower altitudes where the SAV is small. The differences in the wind velocities can be as large as 5 m/s at 20 kin. We carried out two numerical experiments with integrations over more than 20 years: 1) With the QBO period "tuned" to be 30 months, of academic interest but instructive, the seasonal cycle in the solar forcing [through the Semi-annual Oscillation (SAO)] acts as a strong pacemaker to produce a firm lock on the period and phase of the QBO. The SAE then shows up primarily as a distinct but relatively weak amplitude modulation. 2) With the QBO period between 30 and 34 (or less than 30, presumably) months, the seasonal phase lock is weak compared with (1). The SAV in the seasonal cycle then causes variations in the QBO period and phase, and this amplifies the SAE to produce relatively large variations in the wind field. We conclude that, under realistic conditions as in (2), the solar seasonal forcing, with activity variations in radiance, causes the phase and period of the QBO to change to produce a relatively large solar activity effect in the zonal circulation at low latitudes and low altitudes.

Mengel, J. G.↗
DOE OSTI2024

Using AI for Wave-front Estimation with the Rubin Observatory Active Optics System

Abstract The Vera C. Rubin Observatory will, over a period of 10 yr, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an active-optics system (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this, Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wave front. We have designed and integrated a deep-learning (DL) model for wave-front estimation into the AOS pipeline. In this paper, we compare the performance of this DL approach to Rubin’s baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the DL approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the DL model is 40× faster, the median error 2× better under ideal conditions, 5× better in the presence of vignetting by the Rubin camera, and 14× better in the presence of blending in crowded fields. In addition, the DL model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1983

Satellite altimetry

Progress in satellite altimetry accuracy, together with geoid surface measurements, is reviewed, and future programs are outlined. The Skylab, Geos-3, and Seasat spacecraft all carried altimeters for observing ocean topography, wind speed, and wave height from space. Data were gathered on the geoid and gravity anomalies over ocean areas, leading to determinations of the mesoscale variability of the oceans and a preliminary data base on ocean circulation. Satellite altimeters are currently accurate to 2 cm over a period of several years, and radial errors have been reduced to 50 cm with Seasat. Mean sea surfaces are measured with decimeter accuracy, a level of precision expected for altimetry when improved Doppler tracking, laser tracking, and the introduction of a GPS-based tracking system are implemented. The Shuttle is regarded as a prime vehicle for testing new altimetry techniques.

Marsh, J. G.↗
NASA NTRS1989

Coherent radar measurement of ocean currents from geostationary orbit

A coherent HF radar system developed by Barrick has successfully measured ocean surface currents near shore. This innovative system, called CODAR, can map the current vector for coastal areas as large as 10,000 sq km. CODAR's range is limited owing to the strong attenuation suffered by HF ground waves. An alternate technique was proposed by Schuler, in which the cross-product power spectrum of two (different frequency) microwave signals is processed. The frequency of the resonant peak corresponds close by to the Doppler shift of an ocean gravity wave traveling toward the radar at the phase velocity, v(sub p). The slight difference between the frequency of the measured resonant delta K peak and the Doppler frequency shift caused by the motion of the gravity wave is attributed to be the current velocity in the pointing direction of the radar. The Microwave Remote Sensing Laboratory (MIRSL) has considered the feasibility of using this technique to measure ocean surface currents from geostationary satellite platforms. Problems are discussed that must be overcome if a satellite current measurement system is to be realized. MIRSL research activities that address some of these problem areas are discussed. Current measurements are presented that were made using a specially-designed C-Band, step-frequency delta K radar. These measurements suggest that progress is being achieved in detecting ocean surface current motion for a wide variety of ocean surface conditions.

Mcintosh, R. E.↗