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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Correlated Temporal and Spectral Variability

The variability of neutron star and black hole X-ray sources has several dimensions, because of the roles played by different important time-scales. The variations on time scales of hours, weeks, and months, ranging from 50% to orders of magnitude, arise out of changes in the flow in the disk. The most important driving forces for those changes are probably various possible instabilities in the disk, though there may be effects with other dominant causes. The changes in the rate of flow appear to be associated with changes in the flow's configuration, as the accreting material approaches the compact object, for there are generally correlated changes in both the Xray spectra and the character of the faster temporal variability. There has been a lot of progress in tracking these correlations, both for Z and Atoll neutron star low-mass X-ray binaries, and for black hole binaries. I will discuss these correlations and review briefly what they tell us about the physical states of the systems.

Swank, Jean H.↗
NASA NTRS2011

Surface and Atmospheric Contributions to Passive Microwave Brightness Temperatures

Physically-based passive microwave precipitation retrieval algorithms require a set of relationships between satellite observed brightness temperatures (TB) and the physical state of the underlying atmosphere and surface. These relationships are typically non-linear, such that inversions are ill-posed especially over variable land surfaces. In order to better understand these relationships, this work presents a theoretical analysis using brightness temperature weighting functions to quantify the percentage of the TB resulting from absorption/emission/reflection from the surface, absorption/emission/scattering by liquid and frozen hydrometeors in the cloud, the emission from atmospheric water vapor, and other contributors. The results are presented for frequencies from 10 to 874 GHz and for several individual precipitation profiles as well as for three cloud resolving model simulations of falling snow. As expected, low frequency channels (<89 GHz) respond to liquid hydrometeors and the surface, while the higher frequency channels become increasingly sensitive to ice hydrometeors and the water vapor sounding channels react to water vapor in the atmosphere. Low emissivity surfaces (water and snow-covered land) permit energy downwelling from clouds to be reflected at the surface thereby increasing the percentage of the TB resulting from the hydrometeors. The slant path at a 53deg viewing angle increases the hydrometeor contributions relative to nadir viewing channels and show sensitivity to surface polarization effects. The TB percentage information presented in this paper answers questions about the relative contributions to the brightness temperatures and provides a key piece of information required to develop and improve precipitation retrievals over land surfaces.

Gail Skofronick-Jackson↗
NASA NTRS2012

Feasibility and Definition of a Lunar Polar Volatiles Prospecting Mission

The recent Lunar Crater Observing and Sensing Satellite (LCROSS) mission has provided evidence for significant amounts of cold trapped volatiles in Cabeus crater near the Moon's south pole. Moreover, LRO/Diviner measurements of extremely cold lunar polar surface temperatures imply that volatiles can be stable outside or areas of strict permanent shadows. These discoveries suggest that orbital neutron spectrometer data point to extensive deposits at both lunar poles. The physical state, composition and distribution of these volatiles are key scientific issues that relate to source and emplacement mechanisms. These issues are also important for enabling lunar in situ resource utilization (ISRU). An assessment of the feasibility of cold-trapped volatile ISRU requires a priori information regarding the location, form, quantity, and potential for extraction of available resources. A robotic mission to a mostly shadowed but briefly .unlit location with suitable environmental conditions (e.g. short periods of oblique sunlight and subsurface cryogenic temperatures which permit volatile trapping) can help answer these scientific and exploration questions. Key parameters must be defined in order to identify suitable landing sites, plan surface operations, and achieve mission success. To address this need, we have conducted an initial study for a lunar polar volatile prospecting mission, assuming the use of a solar-powered robotic lander and rover. Here we present the mission concept, goals and objectives, and landing site selection analysis for a short-duration, landed, solar-powered mission to a potential hydrogen volatile-rich site.

Heldmann, Jennifer↗
NASA NTRS2011

Surface and Atmospheric Contributions to Passive Microwave Brightness Temperatures for Falling Snow Events

Physically based passive microwave precipitation retrieval algorithms require a set of relationships between satellite -observed brightness temperatures (TBs) and the physical state of the underlying atmosphere and surface. These relationships are nonlinear, such that inversions are ill ]posed especially over variable land surfaces. In order to elucidate these relationships, this work presents a theoretical analysis using TB weighting functions to quantify the percentage influence of the TB resulting from absorption, emission, and/or reflection from the surface, as well as from frozen hydrometeors in clouds, from atmospheric water vapor, and from other contributors. The percentage analysis was also compared to Jacobians. The results are presented for frequencies from 10 to 874 GHz, for individual snow profiles, and for averages over three cloud-resolving model simulations of falling snow. The bulk structure (e.g., ice water path and cloud depth) of the underlying cloud scene was found to affect the resultant TB and percentages, producing different values for blizzard, lake effect, and synoptic snow events. The slant path at a 53 viewing angle increases the hydrometeor contributions relative to nadir viewing channels. Jacobians provide the magnitude and direction of change in the TB values due to a change in the underlying scene; however, the percentage analysis provides detailed information on how that change affected contributions to the TB from the surface, hydrometeors, and water vapor. The TB percentage information presented in this paper provides information about the relative contributions to the TB and supplies key pieces of information required to develop and improve precipitation retrievals over land surfaces.

Skofronick-Jackson, Gail↗
NASA NTRS2014

Lunar Flashlight: Mapping Lunar Surface Volatiles Using a Cubesat

Water ice and other volatiles may be located in the Moon's polar regions, with sufficient quantities for in situ extraction and utilization by future human and robotic missions. Evidence from orbiting spacecraft and the LCROSS impactor suggests the presence of surface and/or nearsurface volatiles, including water ice. These deposits are of interest to human exploration to understand their potential for use by astronauts. Understanding the composition, quantity, distribution, and form of water/H species and other volatiles associated with lunar cold traps is identified as a NASA Strategic Knowledge Gap (SKG) for Human Exploration. These polar volatile deposits could also reveal important information about the delivery of water to the Earth- Moon system, so are of scientific interest. The scientific exploration of the lunar polar regions was one of the key recommendations of the Planetary Science Decadal Survey. In order to address NASA's SKGs, the Advanced Exploration Systems (AES) program selected three lowcost 6-U CubeSat missions for launch as secondary payloads on the first test flight (EM1) of the Space Launch System (SLS) scheduled for 2017. The Lunar Flashlight mission was selected as one of these missions, specifically to address the SKG associated with lunar volatiles. Development of the Lunar Flashlight CubeSat concept leverages JPL's Interplanetary Nano- Spacecraft Pathfinder In Relevant Environment (INSPIRE) mission, MSFC's intimate knowledge of the Space Launch System and EM-1 mission, small business development of solar sail and electric propulsion hardware, and JPL experience with specialized miniature sensors. The goal of Lunar Flashlight is to determine the presence or absence of exposed water ice and its physical state, and map its concentration at the kilometer scale within the permanently shadowed regions of the lunar south pole. After being ejected in cislunar space by SLS, Lunar Flashlight deploys its solar panels and solar sail and maneuvers into a low-energy transfer to lunar orbit. The solar sail and attitude control system work to bring the satellite into an elliptical polar orbit spiraling down to a perilune of 30-10 km above the south pole for data collection. Lunar Flashlight uses its solar sail to shine reflected sunlight into permanently shadowed regions, measuring surface albedo with a four-filter point spectrometer at 1.1, 1.5 1.9, and 2.0 microns. Water ice will be distinguished from dry regolith from these measurements in two ways: 1) spatial variations in absolute reflectance (water ice is much brighter in the continuum channels), and 2) reflectance ratios between absorption and continuum channels. Derived reflectance and reflectance ratios will be mapped onto the lunar surface in order to distinguish the composition of the PSRs from that of the sunlit terrain. Lunar Flashlight enables a low-cost path to in-situ resource utilization (ISRU) by identifying operationally useful deposits (if there are any), which is a game-changing capability for expanded human exploration.

Cohen, B. A.↗
NASA NTRS2013

ALMA Observations of the Molecular Gas in the Debris Disk of the 30 Myr Old Star HD 21997

The 30 Myr old A3-type star HD 21997 is one of the two known debris dust disks having a measurable amount of cold molecular gas. With the goal of understanding the physical state, origin, and evolution of the gas in young debris disks, we obtained CO line observations with the Atacama Large Millimeter/submillimeter Array (ALMA). Here, we report on the detection of (12)CO and (13)CO in the J = 2-1 and J = 3-2 transitions and C(18)O in the J = 2-1 line. The gas exhibits a Keplerian velocity curve, one of the few direct measurements of Keplerian rotation in young debris disks. The measured CO brightness distribution could be reproduced by a simple star+disk system, whose parameters are r(sub in) < 26 AU, r(sub out) = 138 +/- 20 AU, Stellar M = 1.8 +0.5/−0.2 Solar M, and i = 32. Deg. 6 +/- 3 deg..1. The total CO mass, as calculated from the optically thin C(18)O line, is about (4-8) ×10(exp −2 ) Solar M, while the CO line ratios suggest a radiation temperature on the order of 6-9 K. Comparing our results with those obtained for the dust component of the HD 21997 disk from ALMA continuum observations by Moor et al., we conclude that comparable amounts of CO gas and dust are present in the disk. Interestingly, the gas and dust in the HD 21997 system are not colocated, indicating a dust-free inner gas disk within 55 AU of the star. We explore two possible scenarios for the origin of the gas. A secondary origin, which involves gas production from colliding or active planetesimals, would require unreasonably high gas production rates and would not explain why the gas and dust are not colocated. We propose that HD 21997 is a hybrid system where secondary debris dust and primordial gas coexist. HD 21997, whose age exceeds both the model predictions for disk clearing and the ages of the oldest T Tauri-like or transitional gas disks in the literature, may be a key object linking the primordial and the debris phases of disk evolution.

molecular gas↗
NASA NTRS2014

Anomalous Expansion of Coronal Mass Ejections During Solar Cycle 24 and Its Space Weather Implications

The familiar correlation between the speed and angular width of coronal mass ejections (CMEs) is also found in solar cycle 24, but the regression line has a larger slope: for a given CME speed, cycle 24 CMEs are significantly wider than those in cycle 23. The slope change indicates a significant change in the physical state of the heliosphere, due to the weak solar activity. The total pressure in the heliosphere (magnetic + plasma) is reduced by approximately 40%, which leads to the anomalous expansion of CMEs explaining the increased slope. The excess CME expansion contributes to the diminished effectiveness of CMEs in producing magnetic storms during cycle 24, both because the magnetic content of the CMEs is diluted and also because of the weaker ambient fields. The reduced magnetic field in the heliosphere may contribute to the lack of solar energetic particles accelerated to very high energies during this cycle.

coronal mass ejections↗
NASA NTRS2015

Visualization of Thin Liquid Crystal Bubbles in Microgravity

The Observation and Analysis of Smectic Islands in Space (OASIS) experiment exploits the unique characteristics of freely suspended liquid crystals in a microgravity environment to advance the understanding of fluid state physics.

microgravity↗
NASA NTRS2017

Robust Exploration and Commercial Missions to the Moon Using LANTR Propulsion and Lunar Liquid Oxygen Derived from FeO-Rich Pyroclastic Deposits

Studies conducted by NASA and its contractors (early 1980s early 1990s) indicated a substantial benefit from using lunar-derived propellants specifically lunar-derived LO2 (LLO2) or LUNOX in a lunar space transportation system (LTS). With a LTS using LO2/LH2 chemical rockets, approx. 6 kilograms (kg) of mass in low Earth orbit (LEO) is required to place 1 kg of payload on the lunar surface (LS). Of this 6 kg, approx. 70% (4.2 kg) is propellant and 6/7th of this mass (3.6 kg) is oxygen assuming an O/H MR 6:1. Since the cost of placing a kilogram of mass on the LS is approx. 6X the cost of delivering it to LEO, the ability to produce and utilize LUNOX or lunar-derived LO2 and hydrogen (LLH2) from lunar polar ice (LPI) deposits can provide significant mission leverage. Providing LUNOX for use in fuel cells, life support systems and LO2/LH2 chemical rockets used on lunar landing vehicles (LLVs), can allow high value cargo (people, manufacturing and scientific equipment, etc.) to be transported to LEO, then to the Moon instead of bulk LO2 propellant. Oxygen is abundant in the lunar regolith (approx. 43% by mass) and can be extracted using a variety of techniques, such as hydrogen reduction of ilmenite (FeOTiO2) or FeO-rich volcanic glass (orange soil) discovered during the Apollo 17 mission to Taurus-Littrow. While considerable interest has been expressed about mining and processing LPI for rocket propellant, ground truth must first be established to quantify the physical state of the ice (e.g., its vertical thickness and areal extent, levels of soil contamination, etc.) and the deep, extremely cold (approx. 26-100 K) permanently shadowed craters where the ice resides.

Nuclear Thermal Propulsion↗
NASA NTRS2018

The Network for the Detection of Atmospheric Composition Change (NDACC): History, Status and Perspectives

The Network for the Detection of Atmospheric Composition Change (NDACC) is an international global network of more than 90 stations making high-quality measurements of atmospheric composition that began official operations in 1991 after 5 years of planning. Apart from sonde measurements, all measurements in the network are performed by ground-based remote-sensing techniques. Originally named the Network for the Detection of Stratospheric Change (NDSC), the name of the network was changed to NDACC in 2005 to better reflect the expanded scope of its measurements. The primary goal of NDACC is to establish long-term databases for detecting changes and trends in the chemical and physical state of the atmosphere (mesosphere, stratosphere, and troposphere) and to assess the coupling of such changes with climate and air quality. NDACC's origins, station locations, organizational structure, and data archiving are described. NDACC is structured around categories of ground-based observational techniques (sonde, lidar, microwave radiometers, Fourier-transform infrared, UV-visible DOAS (differential optical absorption spectroscopy)-type, and Dobson-Brewer spectrometers, as well as spectral UV radiometers), timely cross-cutting themes (ozone, water vapour, measurement strategies, cross-network data integration), satellite measurement systems, and theory and analyses. Participation in NDACC requires compliance with strict measurement and data protocols to ensure that the network data are of high and consistent quality. To widen its scope, NDACC has established formal collaborative agreements with eight other cooperating networks and Global Atmosphere Watch (GAW). A brief history is provided, major accomplishments of NDACC during its first 25 years of operation are reviewed, and a forward-looking perspective is presented.

lidar↗
NASA NTRS2018

Atmospheric Chemistry Modeling Using a Regression Forest Model

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM. We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for improving this approach.

Keller, Christoph A.↗
NASA NTRS2015

Simulation of Liquid Rocket Engine Failure Propagation Using Self-Evolving Scenarios

Traditional probabilistic risk assessment approaches often require failure scenarios to be explicitly defined through event sequences that are then quantified as part of the integrated analysis. This approach becomes difficult when failure propagation paths change as a function of the system operation. Additionally, if the propagation paths represent interactions among even a modest number of components, the scenario count becomes combinatorially intractable. This paper presents an alternate approach for quantifying the probability of failure propagation in such a case. Rather than explicitly defining scenario sequences, simple physical models are created for each of the components. In this way, only the physical states and rules of component interactions must be defined, rather than event sequences for each individual scenario. Initiating failures are introduced into the system, either randomly or as defined by relative likelihood, and the failures cascade through the system via the interaction rules. This process is repeated using Monte Carlo methods and, as a result, the most probable scenarios “self-evolve” in terms of both sequence path and frequency. This approach was applied to failures occurring in the engine compartment of a space launch vehicle with four liquid rocket engines and four high-pressure helium tanks. Each engine was modeled with key components, such as turbomachinery, combustion chamber, propellant lines, and additional support systems. Three test cases were conducted with different high-energy engine failures. End results of interest included an additional engine-out failure and tank burst, which represent the loss-of-mission (LOM) and loss-of-crew (LOC) failure environments, respectively. Observations show that almost every scenario outcome is unique and that many scenarios involve complex chain reactions that are difficult to predict. This validates the usefulness of the modeling approach in assessing the overall risks to the crew during a launch vehicle abort.

Rocket Engine↗
NASA NTRS2019

Atmospheric Chemistry Modeling Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Machine Learning Application to Atmospheric Chemistry Modeling

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O (sup 1) D (Deuterium) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model. We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry. This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRS2019

Atmospheric Chemistry Modeling and Air Quality Forecasting Using Machine Learning

Atmospheric chemistry models are a central tool to study the impact of chemical constituents on the environment, vegetation and human health. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (milli-seconds for O1D to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a chemistry model.We have investigated a machine learning approach to emulate the chemistry instead of solving the differential equations numerically. From a one-month simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, etc. We discuss speed and accuracy of our approach, and highlight some potential future directions for improving it.

Keller, Christoph A.↗
NASA NTRSDate not supplied

The Volatiles Investigating Polar Exploration Rover (VIPER) Near Infrared Volatile Spectrometer System (NIRVSS).

NASA’s VIPER mission is planned for delivery to the lunar surface in late 2023 [1]. VIPER’s science objectives are to: 1) characterize the distribution and physical state of lunar polar water and other volatiles in lunar cold traps and regolith to under-stand their origin and 2) provide the data necessary for NASA to evaluate the potential return of relying upon in-situ resource utilization from the lunar polar regions [1]. The NIRVSS instrument, matured through several R&D programs to TRL 6 [2,3,4], has been chosen as part of the VIPER science payload [5].

T. L. Roush↗
NASA NTRS2020

Goal-Oriented Architecture for Telescope Control Software

Modern telescope control systems control a multitude of sensors and actuators physically distributed across subsystems. Traditionally, control systems are specified to compute system outputs based on reference setpoints despite environmental or internal disturbances. The control strategies are typically tightly coupled and very prescriptive, defining how exactly the control is to be achieved. In a goal-oriented approach, the control system architecture emphasizes the operational aspect, specifying the behavior at a higher level in terms of operational goals, considering several aspects such as environmental conditions and required wavefront quality. The result is an architecture that can more autonomously handle different control strategies, taking into account systematically the physical states of the system which are relevant for control. This paper demonstrates the feasibility of a goal-oriented approach by analyzing four fundamental functions: pointing, tracking, offsetting, and dome vignetting. The resulting SysML analysis and design models are used to develop a software prototype for distributed telescope control. Typical operational scenarios are discussed and compared to the ESO Very Large Telescope control software architecture.

Karban, R.↗