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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 NTRS2012

The Primordial Inflation Explorer

The Primordial Inflation Explorer is an Explorer-class mission to measure the gravity-wave signature of primordial inflation through its distinctive imprint on the linear polarization of the cosmic microwave background. PIXIE uses an innovative optical design to achieve background-limited sensitivity in 400 spectral channels spanning 2.5 decades in frequency from 30 GHz to 6 THz (1 cm to 50 micron wavelength). The principal science goal is the detection and characterization of linear polarization from an inflationary epoch in the early universe, with tensor-to-scalar ratio r < 10(exp -3) at 5 standard deviations. The rich PIXIE data set will also constrain physical processes ranging from Big Bang cosmology to the nature of the first stars to physical conditions within the interstellar medium of the Galaxy. I describe the PIXIE instrument and mission architecture needed to detect the inflationary signature using only 4 semiconductor bolometers.

Kogut, Alan J.↗

NASA NTRS2013

Standard Practices for Usage of Inductive Magnetic Field Probes with Application to Electric Propulsion Testing

Inductive magnetic field probes (also known as B-dot probes and sometimes as B-probes or magnetic probes) are often employed to perform field measurements in electric propulsion applications where there are time-varying fields. Magnetic field probes provide the means to measure these magnetic fields and can even be used to measure the plasma current density indirectly through the application of Ampere's law. Measurements of this type can yield either global information related to a thruster and its performance or detailed, local data related to the specific physical processes occurring in the plasma. Results of the development of a standard for B-dot probe measurements are presented, condensing the available literature on the subject into an accessible set of rules, guidelines, and techniques to standardize the performance and presentation of future measurements.

Polzin, Kurt A.↗

NASA NTRS1995

Estimating the urban bias of surface shelter temperatures using upper-air and satellite data. Part 1: Development of models predicting surface shelter temperatures

Multiple regression techniques were used to predict surface shelter temperatures based on the time period 1986-89 using upper-air data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to represent the background climate and site-specific data to represent the local landscape. Global monthly mean temperature models were developed using data from over 5000 stations available in the Global Historical Climate Network (GHCN). Monthly maximum, mean, and minimum temperature models for the United States were also developed using data from over 1000 stations available in the U.S. Cooperative (COOP) Network and comparative monthly mean temperature models were developed using over 1150 U.S. stations in the GHCN. Three-, six-, and full-variable models were developed for comparative purposes. Inferences about the variables selected for the various models were easier for the GHCN models, which displayed month-to-month consistency in which variables were selected, than for the COOP models, which were assigned a different list of variables for nearly every month. These and other results suggest that global calibration is preferred because data from the global spectrum of physical processes that control surface temperatures are incorporated in a global model. All of the models that were developed in this study validated relatively well, especially the global models. Recalibration of the models with validation data resulted in only slightly poorer regression statistics, indicating that the calibration list of variables was valid. Predictions using data from the validation dataset in the calibrated equation were better for the GHCN models, and the globally calibrated GHCN models generally provided better U.S. predictions than the U.S.-calibrated COOP models. Overall, the GHCN and COOP models explained approximately 64%-95% of the total variance of surface shelter temperatures, depending on the month and the number of model variables. In addition, root-mean-square errors (rmse's) were over 3 C for GHCN models and over 2 C for COOP models for winter months, and near 2 C for GHCN models and near 1.5 C for COOP models for summer months.

Epperson, David L.↗

NASA NTRS1974

Sequential filter design for precision orbit determination and physical constant refinement

Earth-based spacecraft tracking data have historically been processed with classical least squares filtering techniques both for navigation purposes and for physical constant determination. The small, stochastic nongravitational forces acting on the spacecraft are described to motivate the use of sequential estimation as an alternative to the least squares fitting procedures. The stochastic forces are investigated both in terms of their effect on the tracking data and their influence on estimation accuracy. A flexible sequential filter design which leaves the existing trajectory, variational equations, data observable and partial computations undisturbed is described. A detailed filter design is presented that meets the precision demands and flexibility requirements of deep space navigation and of scientific problems.

Curkendall, D. W.↗

NASA NTRS2017

Estimating the Distribution of Colored Dissolved Organic Matter During the Southern Ocean Gas Exchange Experiment Using Four-Dimensional Variational Data Assimilation

We diagnosed the effect of various physical processes on the distribution of mixed-layer colored dissolved organic matter (CDOM) and a sulfur hexauoride (SF6) tracer during the Southern Ocean Gas Exchange Experiment (SO GasEx). The biochemical upper ocean state estimate uses in situ and satellite biochemical and physical data in the study region, including CDOM (absorption coefcient and spectral slope), SF6, hydrography, and sea level anomaly. Modules for photobleaching of CDOM and surface transport of SF6 were coupled with an ocean circulation model for this purpose. The observed spatial and temporal variations in CDOM were captured by the state estimate without including any new biological source term for CDOM, assuming it to be negligible over the 26 days of the state estimate. Thermocline entrainment and photobleaching acted to diminish the mixed-layer CDOM with time scales of 18 and 16 days, respectively. Lateral advection of CDOM played a dominant role and increased the mixed-layer CDOM with a time scale of 12 days, whereas lateral diffusion of CDOM was negligible. A Lagrangian view on the CDOM variability was demonstrated by using the SF6 as a weighting function to integrate the CDOM elds. This and similar data assimilation methods can be used to provide reasonable estimates of optical properties, and other physical parameters over the short-term duration of a research cruise, and help in the tracking of tracer releases in large-scale oceanographic experiments, and in oceanographic process studies.

Del Castillo, C. E.↗

NASA NTRS2014

Data Albums: An Event Driven Search, Aggregation and Curation Tool for Earth Science

One of the largest continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available. Approaches used in Earth science research such as case study analysis and climatology studies involve gathering discovering and gathering diverse data sets and information to support the research goals. Research based on case studies involves a detailed description of specific weather events using data from different sources, to characterize physical processes in play for a specific event. Climatology-based research tends to focus on the representativeness of a given event, by studying the characteristics and distribution of a large number of events. This allows researchers to generalize characteristics such as spatio-temporal distribution, intensity, annual cycle, duration, etc. To gather relevant data and information for case studies and climatology analysis is both tedious and time consuming. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the datasets of interest can obtain the specific files they need using these systems. However, in cases where researchers are interested in studying a significant event, they have to manually assemble a variety of datasets relevant to it by searching the different distributed data systems. In these cases, a search process needs to be organized around the event rather than observing instruments. In addition, the existing data systems assume users have sufficient knowledge regarding the domain vocabulary to be able to effectively utilize their catalogs. These systems do not support new or interdisciplinary researchers who may be unfamiliar with the domain terminology. This paper presents a specialized search, aggregation and curation tool for Earth science to address these existing challenges. The search tool automatically creates curated "Data Albums", aggregated collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; and information about the event contained in news reports, images or videos to supplement research analysis. Curation in the tool is driven via an ontology based relevancy ranking algorithm to filter out non-relevant information and data.

Ramachandran, Rahul↗

NASA NTRS2006

A Coupled GCM-Cloud Resolving Modeling System to Study Precipitation Processes

Recent GEWEX Cloud System Study (GCSS) model comparison projects have indicated that cloud resolving models (CRMs) agree with observations better than traditional single column models in simulating various types of clouds and cloud systems from different geographic locations. Current and future NASA satellite programs can provide cloud, precipitation, aerosol and other data at very fine spatial and temporal scales. It requires a coupled global circulation model (GCM) and cloud-scale model (termed a super-parameterization or multi-scale modeling framework, MMF) to use these satellite data to improve the understanding of the physical processes that are responsible for the variation in global and regional climate and hydrological systems. The use of a GCM will enable global coverage and the use of a CRM will allow for better and more sophisticated physical parameterization. NASA Satellite and field campaign cloud related data sets can provide initial conditions as well as validation for both the MMF and CRMs. Also we have implemented a Land Information System (LIS that includes the CLM and NOAH land surface models into the MMF. The Goddard MMF is based on the 2D Goddard Cumulus Ensemble (GCE) model and the Goddard finite volume general circulation model (fvGCM) This modeling system has been applied and tested its performance for two different climate scenarios, El Nino (1998) and La Nina (1999). The coupled new modeling system produced more realistic propagation and intensity of tropical rainfall systems and intraseasonal oscillations, and diurnal variation of precipitation that are very difficult to forecast using even the state-of-the-art GCMs. In this talk I will present: (1) a brief review on GCE model and its applications on precipitation processes (both Microphysical and land processes) and (2) The Goddard MMF and the Major difference between two existing MMFs (CSU MMF and Goddard MMF) and preliminary results (the comparison with traditional GCMs).

Tao, Wei-Kuo↗

NASA NTRS1994

Theoretical studies of spectroscopic problems of importance for atmospheric radiation measurements

Many of the instruments used to deduce the physical parameters of the Earth's atmosphere necessary for climate studies or for pollution monitoring (for instance, temperature versus pressure or number densities of trace molecules) rely on the existence of accurate spectroscopic data and an understanding of the physical processes responsible for the absorption or emission of radiation. During the summer, research was either continued or begun on three distinct problems: (1) an improved theoretical framework for the calculation of the far-wing absorption of allowed spectral lines; (2) a refinement of the calculation of the collision-induced fundamental spectrum of N2; and (3) an investigation of possible line-mixing effects in the fundamental spectrum of CH4. Progress in these three areas is summarized below. During the past few years, we have developed a theoretical framework for the calculation of the absorption of radiation by the far wings of spectral lines. Such absorption due to water vapor plays a crucial role in the greenhouse effect as well as limiting the retrieval of temperature profiles from satellite data. Several improvements in the theory have been made and the results are being prepared for publication. Last year we published results for the theoretical calculation of the absorption of radiation due to the dipoles induced during binary collisions of N2 molecules using independently measured molecular parameters; the results were in reasonable agreement with experimental data. However, recent measurements have revealed new fine structure that has been attributed to line-mixing effects. We do not think that this is correct, rather that the structure results from short-range anisotropic dipoles. We are in the process of including this refinement in our theoretical calculation in order to compare with the new experimental data. Subtle changes in the spectra of CH4 measured by researchers at Langley have also been attributed to line-mixing effects. By analyzing the same spectral lines we have attempted to verify or rule out possible line-mixing mechanisms. Due to the complexity and richness of the spectrum of this highly symmetric molecule, as well as the small magnitude of the effects, a detailed first-principle calculation of the mixing is a difficult problem. Before such a program is undertaken it is important to glean as much information as possible concerning the possible mechanisms by a systematic analysis of the existing data.

Tipping, Richard H.↗

NASA NTRS1987

Physical processes and infrared emission from the Cassiopeia A supernova remnant

IRAS 12-100 micron data on the Cas A remnant are presented, and various physical mechanisms and astrophysical sites that may contribute to the observed infrared emission are analyzed. The contributions of various sources of infrared emission to the IRAS fluxes are found to be small. The residual infrared emission is attributed to thermal emission from dust which is swept up by the expanding supernova blast wave and collisionally heated by the postshock X-ray emitting gas. The calculations are consistent with a shock velocity of 1800 km/s and a preshock gas density of about 2/cu cm. The mass of the swept-up gas is about 0.6 solar mass. An excess of 12 micron thermal emission in the spectrum of Cas A suggests the presence of very small particles in the preshocked gas.

Dwek, E.↗

NASA NTRS2001

Predictability of the California Current System

The physical and biological oceanography of the Southern California Bight (SCB), a highly productive subregion of the California Current System (CCS) that extends from Point Conception, California, south to Ensenada, Mexico, continues to be extensively studied. For example, the California Cooperative Oceanic Fisheries Investigations (CalCOFI) program has sampled this region for over 50 years, providing an unparalleled time series of physical and biological data. However, our understanding of what physical processes control the large-scale and mesoscale variations in these properties is incomplete. In particular, the non-synoptic and relatively coarse spatial sampling (70km) of the hydrographic grid does not completely resolve the mesoscale eddy field (Figure 1a). Moreover, these unresolved physical variations exert a dominant influence on the evolution of the ecosystem. In recent years, additional datasets that partially sample the SCB have become available. Acoustic Doppler Current Profiler (ADCP) measurements, which now sample upper-ocean velocity between stations, and sea level observations along TOPEX tracks give a more complete picture of the mesoscale variability. However, both TOPEX and ADCP are well-sampled only along the cruise or orbit tracks and coarsely sampled in time and between tracks. Surface Lagrangian drifters also sample the region, although irregularly in time and space. SeaWiFS provides estimates of upper-ocean chlorophyll-a (chl-alpha), usually giving nearly complete coverage for week-long intervals, depending on cloud coverage. Historical ocean color data from the Coastal Zone Color Scanner (CZCS) has been used extensively to determine phytoplankton patterns and variability, characterize the primary production across the SCB coastal fronts, and describe the seasonal and interannual variability in pigment concentrations. As in CalCOFI, these studies described much of the observed structures and their variability over relatively large space and time scales.

Miller, Arthur J.↗

NASA NTRS2018

Seeing Double: ASASSN-18bt Exhibits a Two-component Rise in the Early-time K2 Light Curve

On 2018 February 4.41, the All-Sky Automated Survey for SuperNovae (ASAS-SN) discovered ASASSN-18bt in the K2 Campaign 16 field. With a redshift of z=0.01098 and a peak apparent magnitude of B(max)=14.31, ASASSN-18bt is the nearest and brightest SNe Ia yet observed by the Kepler spacecraft. Here we present the discovery of ASASSN-18bt, the K2 light curve, and prediscovery data from ASAS-SN and the Asteroid Terrestrial-impact Last Alert System. The K2 early-time light curve has an unprecedented 30-minute cadence and photometric precision for an SN Ia light curve, and it unambiguously shows a ∼4 day nearly linear phase followed by a steeper rise. Thus, ASASSN-18bt joins a growing list of SNe Ia whose early light curves are not well described by a single power law. We show that a double-power-law model fits the data reasonably well, hinting that two physical processes must be responsible for the observed rise. However, we find that current models of the interaction with a nondegenerate companion predict an abrupt rise and cannot adequately explain the initial, slower linear phase. Instead, we find that existing published models with shallow 56Ni are able to span the observed behavior and, with tuning, may be able to reproduce the ASASSN-18bt light curve. Regardless, more theoretical work is needed to satisfactorily model this and other early-time SNe Ia light curves. Finally, we use Swift X-ray nondetections to constrain the presence of circumstellar material (CSM) at much larger distances and lower densities than possible with the optical light curve. For a constant-density CSM, these nondetections constrain ρ<4.5×10(exp 5)per cu.cm at a radius of 4×10(exp 15) cm from the progenitor star. Assuming a wind-like environment, we place mass loss limits of M˙ < 8 x 10(exp -6) M(ʘ)per yr for v(w)=100 km/s, ruling out some symbiotic progenitor systems. This work highlights the power of well-sampled early-time data and the need for immediate multiband, high-cadence follow-up for progress in understanding SNe Ia.

B. J. Shappee↗

NASA NTRSDate not supplied

Using Machine Learning to Estimate Surface-Level SO2 Concentrations from Satellite-Based Measurements

Sulfur dioxide (SO2) is a criteria air pollutant due to its contributions to aerosol formation, rainfall acidification, and harm to human health. The placement of air quality monitoring sites is typically biased towards urban areas, leaving large areas with very limited monitoring data. The Ozone Monitoring Instrument (OMI) has been used to provide estimates of SO2 vertical column densities (VCDs) globally at spatial resolution of 10s of kms once per day. OMI SO2 VCDs have been previously used to estimate surface SO2 concentrations using chemical transport model (CTM) simulations. The CTMs use estimated emissions and assimilated meteorological data, and simulate the chemical and physical processes that determine the vertical profile of SO2, which can be used to derive a ratio between the surface concentrations and VCDs. These models are complex, computationally expensive, and have large uncertainties in the simulated surface-to-VCD ratio due to biases in emissions and relatively coarse resolution. Machine learning techniques are comparatively easier to use, much less computationally expensive to use after training, and can produce more accurate estimations of surface concentrations than the CTM-based method. The interpretation of machine learning models often poses challenges, and in some cases, non-physical variables unrelated to SO2 are used as predictors. In this work, we create an artificial neural network (ANN) to relate OMI retrievals and archived GEOS-FP boundary layer heights to surface SO2 concentrations from the ChinaHighAirPollutants ChinaHighSO2 dataset (CHAP; Wei et al., 2023) on a seasonal average timescale from 2013-2018. Our model only utilizes five variables that are directly relevant to the satellite retrieval, lifetime, and spatial distribution of SO2. The model was trained on 16 seasons (four of each) with independent validation (one of each season) and testing datasets (one of each season) to avoid overfitting. Our ANN generates surface SO2 concentrations that are sensitive (slope = 0.51) and consistent (r = 0.74) with the CHAP data, but are underpredicted by an average of 1.2 ppbv with a mean absolute error of 2.2 ppbv. These results are better than recent studies utilizing the CTM method. To our knowledge, this is the best performing machine learning model that only uses physical variables to predict surface SO2. Our work demonstrates that a carefully constructed, simple ML model can accurately estimate surface-based SO2 concentrations from satellite VCD measurements, and this technique has future promise to expend to newer, higher resolution satellites and other air pollutants.

SO2, air quality, OMI, machine learning↗

NASA NTRS1983

Observations of charging dynamics

Plasma data from the University of California, San Diego, Auroral Particles experiments on Applied Technology satellites 5 and 6 are used to investigate the dynamics of natural charging events. Both eclipse and daylight charging events are considered, and typical responses are illustrated by data from specific events. Two different physical processes are found to be involved in the charging process. One of these is straightforward: the spacecraft structure potential responds rapidly to changes in the environment, typically changing by hundreds of volts in a few seconds. The other process is more subtle: differential charging and potential barrier formation precede structural charging and determine the time scale; typically, tens of minutes are required for the potential to change by several hundreds of volts. The latter process is found to be predominately responsible for daylight charging on both spacecraft.

Olsen, R. C.↗

NASA NTRS1984

Magnetospheric topology of fields and currents

The interaction of the solar wind with the magnetosphere is examined from the standpoint of fundamental processes in physics and, whenever possible, observational data. A conceptual model is developed which incorporates several features of the magnetosphere that are still open to debate, such as open magnetic field lines in the polar cap and a boundary layer just inside the magnetopause. It is shown that the length of the magnetotail, the existence of the polar rain, the bursts of particles at the edge of the plasma sheet, and the poleward motion of auroral forms during the recovery phase of a magnetospheric substorm may all have a natural explanation with the proposed model. The one exception, however, is the topology of the electric field. The problems associated with reversal of the electric field within the magnetotail are examined.

Heikkila, W. J.↗

NASA NTRS1989

Stereo image visualization of numerically simulated turbulence

Stereo imaging techniques are used in the current study to analyze the results of a direct Navier-Stokes simulation of a turbulent boundary layer. Coherent structures embedded within the unsteady, highly three-dimensional turbulent flow fields are significantly enhanced through the use of stereo computer graphic renderings. In the paper, the geometrical foundations of two different methods for generating stereo images are reviewed. Sufficient detail is provided to enable readers to code stereo display algorithms from scratch. The effects of varying the geometric parameters of the stereo algorithm are demonstrated. Sample image pairs from animations of the turbulence numerical simulation are presented in color. Stereo imagery, especially when used with time-evolving data, provides new insight into the physical processes responsible for the generation an maintenance of turbulence within boundary layers.

Robinson, Stephen K.↗

NASA NTRS2001

Earth Remote Sensing: A Column Closure Approach

Entering the new millennium, there is no doubt that scientists expand greatly their scientific knowledge of the Earth system by utilizing unique capabilities from the vantage points of space. These global satellite observations include the NASA/NOAA Pathfinder and other ongoing data analysis projects, the Earth Observing System (EOS) program, the Earth System Science Pathfinders (ESSP) small research satellite missions, multi-agency planning for the National Polar-orbiting Operational Environmental Satellite (NPOESS), and other international satellite missions. However, using satellite remotely sensed data alone cannot explore fully the physical processes and energetic balance involved in our changing climate. To close the loop, the ground-based remote sensing and airborne in situ measurements are required. This talk provides an overview of the general strategy of Earth remote sensing for a column closure approach and discusses necessary instrumentation.

Tsay, Si-Chee↗

NASA NTRS2003

The Sun/Earth System and Space Weather

Solar variability and solar activity are now seen as significant drivers with respect to the Earth and human technology systems. Observations over the last 10 years have significantly advanced our understanding of causes and effects in the Sun/Earth system. On a practical level the interactions between the Sun and Earth dictate how we build our systems in space (communications satellites, GPS, etc), and some of our ground systems (power grids). This talk will be about the Sun/Earth system: how it changes with time, its magnetic interactions, flares, the solar wind, and how the Sun effects human systems. Data will be presented from some current spacecraft which show, for example, how we are able to currently give warnings to the scientific community, the Government and industry about space storms and how this data has improved our physical understanding of processes on the Sun and in the magnetosphere. The scientific advances provided by our current spacecraft has led to a new program in NASA to develop a 'Space Weather' system called 'Living With a Star'. The current plan for the 'Living With a Star' program will also be presented.

Poland, Arthur I.↗

NASA NTRSDate not supplied

A Review of IXPE Results for Blazars and Radio Loud AGN

The prime mission of the Imaging X-ray Polarimetry Explorer (IXPE) has provided the first measurements of the polarization for blazar jets and active galactic nuceli in the X-ray band, observing thirteen blazars during its first two years of observations. With these data, a clear picture of the physical processes driving the X-ray emission and particle acceleration in blazars is emerging. This talk will review the results obtained with IXPE thus far, how they inform the larger questions of particle acceleration in blazar jets, and which further questions will require further observations.

X-ray astronomy, polarimetery↗