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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

New Geologic Map of the Argyre Region of Mars

The new generation of Mars orbital topographic and imaging data justifies a new mapping effort of the Argyre impact basin and surroundings (-30.0deg to -65.0deg lat., -20.0deg to -70.0deg long; Fig.1). Our primary objective is to produce a geologic map of the Argyre region at 1:5,000,000 scale in both digital and print formats. The map will detail the stratigraphic and crosscutting relations among rock materials and landforms. These include Argyre basin infill, impact crater rim materials and adjoining highland materials of Noachis Terra, valleys and elongated basins that are radial and concentric about the primary Argyre basin, faults, enigmatic ridges, lobate debris aprons, and valley networks. Such information will be useful to the planetary science community for constraining the regional geology, paleohydrology, and paleoclimate. This includes the assessment of: (a) whether the Argyre basin contained lakes [1], (b) the extent of reported flooding and glaciation, which includes ancient flows of volatiles into the impact basin [2-4], (c) existing interpretations of the origin of the narrow ridges located in the southeast part of the basin floor [2,5], and (d) the extent of Argyre-related tectonism and its influence on the surrounding regions. Whereas the geologic mapping investigation of Timothy Parker focuses on the Argyre floor materials at 1:1,000,000 (MTMs -50036, -50043, -55036, -55043; see Fig. 1 for approximate corners of the area), our regional geologic mapping investigation includes the Argyre basin floor and rim materials, the transition zone that straddles the Thaumasia plateau, which includes Argyre impact-related modification [6], and the southeast margin of the Thaumasia plateau using important new data sets (Fig. 1). Our mapping effort will incorporate the map information of Parker if it is made available during the project.

Dohm, J. M.↗
NASA NTRS2011

NEXUS Scalable and Distributed Next-Generation Avionics Bus for Space Missions

A paper discusses NEXUS, a common, next-generation avionics interconnect that is transparently compatible with wired, fiber-optic, and RF physical layers; provides a flexible, scalable, packet switched topology; is fault-tolerant with sub-microsecond detection/recovery latency; has scalable bandwidth from 1 Kbps to 10 Gbps; has guaranteed real-time determinism with sub-microsecond latency/jitter; has built-in testability; features low power consumption (< 100 mW per Gbps); is lightweight with about a 5,000-logic-gate footprint; and is implemented in a small Bus Interface Unit (BIU) with reconfigurable back-end providing interface to legacy subsystems. NEXUS enhances a commercial interconnect standard, Serial RapidIO, to meet avionics interconnect requirements without breaking the standard. This unified interconnect technology can be used to meet performance, power, size, and reliability requirements of all ranges of equipment, sensors, and actuators at chip-to-chip, board-to-board, or box-to-box boundary. Early results from in-house modeling activity of Serial RapidIO using VisualSim indicate that the use of a switched, high-performance avionics network will provide a quantum leap in spacecraft onboard science and autonomy capability for science and exploration missions.

He, Yutao↗
NASA NTRS2012

Antenna Deployment for a Pathfinder Lunar Radio Observatory

A first step in the development of a large radio observatory on the moon for cosmological or other astrophysical and planetary goals is to deploy a few antennas as a pathfinder mission. In this presentation, we describe a mechanism being developed to deploy such antennas from a small craft, such as a Google Lunar X-prize lander. The antenna concept is to deposit antennas and leads on a polyimide film, such as Kapton, and to unroll the film on the lunar surface. The deployment technique utilized is to launch an anchor which pulls a double line from a reel at the spacecraft. Subsequently, the anchor is set by catching on the surface or collecting sufficient regolith. A motor then pulls in one end of the line, pulling the film off of its roller onto the lunar surface. Detection of a low frequency cutoff of the galactic radio background or of solar radio bursts by such a system would determine the maximum lunar ionospheric density at the time of measurement. The current design and testing, including videos of the deployment, will be presented. These activities are funded in part by the NASA Lunar Science Institute as an activity of the Lunar University Network for Astrophysical Research (LUNAR) consortium. Part of this research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration.

MacDowall, Robert J.↗
DOE OSTI2023

An Ensemble of Neural Networks for Moist Physics Processes, Its Generalizability and Stable Integration

Abstract With the recent advances in data science, machine learning has been increasingly applied to convection and cloud parameterizations in global climate models (GCMs). This study extends the work of Han et al. (2020, https://doi.org/10.1029/2020MS002076 ) and uses an ensemble of 32‐layer deep convolutional residual neural networks, referred to as ResCu‐en, to emulate convection and cloud processes simulated by a superparameterized GCM, SPCAM. ResCu‐en predicts GCM grid‐scale temperature and moisture tendencies, and cloud liquid and ice water contents from moist physics processes. The surface rainfall is derived from the column‐integrated moisture tendency. The prediction uncertainty inherent in deep learning algorithms in emulating the moist physics is reduced by ensemble averaging. Results in 1‐year independent offline validation show that ResCu‐en has high prediction accuracy for all output variables, both in the current climate and in a warmer climate with +4K sea surface temperature. The analysis of different neural net configurations shows that the success to generalize in a warmer climate is attributed to convective memory and the 1‐dimensional convolution layers incorporated into ResCu‐en. We further implement a member of ResCu‐en into CAM5 with real world geography and run the neural‐network‐enabled CAM5 (NCAM) for 5 years without encountering any numerical integration instability. The simulation generally captures the global distribution of the mean precipitation, with a better simulation of precipitation intensity and diurnal cycle. However, there are large biases in temperature and moisture in high latitudes. These results highlight the importance of convective memory and demonstrate the potential for machine learning to enhance climate modeling.

Meteorology & Atmospheric Sciences↗
DOE OSTI2024

Pinus ponderosa physiology data from the 2022 shade manipulation field experiment at Game Ridge, Missoula County, Montana, USA

This dataset contains physiology data on Pinus ponderosa saplings, including: leaf water potential (MPa, megapascals), leaf osmotic potential (MPa, megapascals), leaf pressure potential (MPa, megapascals), leaf relative water content (% saturated mass), as well as soluble sugars (% dry mass),starch (% dry mass), and total nonstructural carbohydrates (% dry mass) found in branch phloem and xylem. Data files and data dictionary(ies) are uploaded as .csv files and .xlsx files. The Users Guide is a .pdf file. Location data can be found in the Google Earth file GameRidge_SitePlotCoordinates.kmz.kml included here. These datasets were collected for Plant Carbohydrate Depletion, Mycorrhizal Networks, and Vulnerability to Drought: An Experimental Test in the Field. This experiment examined the interdependency between plant hydraulics and carbohydrate availability and sought to develop ways to incorporate interactions with below ground symbiotic organisms to better model and quantify forest response to drought.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Pinus ponderosa physiology data from the 2023 girdling manipulation field experiment at Game Ridge, Missoula County, Montana, USA

This dataset contains physiology data on Pinus ponderosa saplings, including: leaf water potential (MPa, megapascals), leaf osmotic potential (MPa, megapascals), leaf pressure potential (MPa, megapascals), leaf relative water content (% saturated mass), as well as soluble sugars (% dry mass),starch (% dry mass), and total nonstructural carbohydrates (% dry mass) found in branch phloem and xylem. Data files and data dictionary(ies) are uploaded as .csv files and .xlsx files. The Users Guide is a .pdf file. Location data can be found in the Google Earth file GameRidge_SitePlotCoordinates.kmz.kml included here. These datasets were collected for Plant Carbohydrate Depletion, Mycorrhizal Networks, and Vulnerability to Drought: An Experimental Test in the Field. This experiment examined the interdependency between plant hydraulics and carbohydrate availability and sought to develop ways to incorporate interactions with below ground symbiotic organisms to better model and quantify forest response to drought.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Future Lunar Geophysical Mission Opportunities Including the Lunar Geophysical Network and CLPS

In the next few years, several opportunities are underway to take new geophysical observations of the Moon including geodetic and seismic. NASA’s novel Commercial Lunar Payload Services (CLPS) program seeks to acquire delivery services from 14 US companies. Nine funded task orders have been selected with payloads from multiple disciplines. Here we review the upcoming geophysical CLPS payloads and their measurement objectives then we provide a review of the Lunar Geophysical Network mission in development for New Frontiers 5. The Lunar Geophysical Network (LGN) mission is proposed to land on the Moon in the early 2030’s and deploy packages at four locations to enable continuous geophysical measurements for a minimum of 6 and a goal of 10 years. Returning to the lunar surface with a long-lived geophysical network is a key next step to advance lunar and planetary science. LGN will greatly expand our primarily Apollo-based knowledge of the deep lunar interior by identifying and characterizing mantle melt layers, as well as core size and state. To meet the mission objectives, the instrument suite provides complementary seismic, geodetic, heat flow, and electromagnetic (EM) observations. We discuss the network landing site requirements and provide example sites that meet these requirements. Landing sites include the P-5 region within the Procellarum KREEP Terrane (PKT; (lat:15˚; lon:-35˚), Schickard basin (lat:-44.3˚; lon:-55.1˚), Crisium basin (lat:18.5˚; lon:61.8˚), and the farside Korolev basin (lat:-2.4˚; lon:-159.3˚) (Figure 1). Network optimization considers the best locations to observe seismic core phases, e.g., ScS and PKP. Ray path density and proximity to young fault scarps are also analyzed to provide increased opportunities for seismic observations. Geodetic constraints from laser ranging require the LGN to have at least three nearside stations at maximum limb distances. Heat flow and EM measurements should be obtained away from terrane boundaries and from magnetic anomalies at locations representative of global trends. In our recent paper, an in-depth case study is provided for Mare Crisium. We also discuss the consequences for scientific return of less-than-optimal locations or number of stations.

Moons↗
NASA NTRSDate not supplied

Future Lunar Geophysical Mission Opportunities Including the Lunar Geophysical Network, Artemis and CLPS

In the next few years, several opportunities are underway to take new geophysical observations of the Moon including geodetic and seismic. NASA’s novel Commercial Lunar Payload Services (CLPS) program seeks to acquire delivery services from 14 US companies. Nine funded task orders have been selected with payloads from multiple disciplines. Here we review the upcoming geophysical CLPS payloads and their measurement objectives then we provide a review of the Lunar Geophysical Network mission in development for New Frontiers 5. The Lunar Geophysical Network (LGN) mission is proposed to land on the Moon in the early 2030’s and deploy packages at four locations to enable continuous geophysical measurements for a minimum of 6 and a goal of 10 years. Returning to the lunar surface with a long-lived geophysical network is a key next step to advance lunar and planetary science. LGN will greatly expand our primarily Apollo-based knowledge of the deep lunar interior by identifying and characterizing mantle melt layers, as well as core size and state. To meet the mission objectives, the instrument suite provides complementary seismic, geodetic, heat flow, and electromagnetic (EM) observations. We discuss the network landing site requirements and provide example sites that meet these requirements. Landing sites include the P-5 region within the Procellarum KREEP Terrane (PKT; (lat:15˚; lon:-35˚), Schickard basin (lat:-44.3˚; lon:-55.1˚), Crisium basin (lat:18.5˚; lon:61.8˚), and the farside Korolev basin (lat:-2.4˚; lon:-159.3˚) (Figure 1). Network optimization considers the best locations to observe seismic core phases, e.g., ScS and PKP. Ray path density and proximity to young fault scarps are also analyzed to provide increased opportunities for seismic observations. Geodetic constraints from laser ranging require the LGN to have at least three nearside stations at maximum limb distances. Heat flow and EM measurements should be obtained away from terrane boundaries and from magnetic anomalies at locations representative of global trends. In our recent paper, an in-depth case study is provided for Mare Crisium. We also discuss the consequences for scientific return of less-than-optimal locations or number of stations.

Moons↗
DOE OSTI2023

Constructing Neural Network Based Models for Simulating Dynamical Systems

Dynamical systems see widespread use in natural sciences like physics, biology, and chemistry, as well as engineering disciplines such as circuit analysis, computational fluid dynamics, and control. For simple systems, the differential equations governing the dynamics can be derived by applying fundamental physical laws. However, for more complex systems, this approach becomes exceedingly difficult. Data-driven modeling is an alternative paradigm that seeks to learn an approximation of the dynamics of a system using observations of the true system. In recent years, there has been an increased interest in applying data-driven modeling techniques to solve a wide range of problems in physics and engineering. Here this article provides a survey of the different ways to construct models of dynamical systems using neural networks. In addition to the basic overview, we review the related literature and outline the most significant challenges from numerical simulations that this modeling paradigm must overcome. Based on the reviewed literature and identified challenges, we provide a discussion on promising research areas.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1977

Viking extended mission support

The period 1 March 1977 through 30 April 1977 is reported and includes Deep Space Network (DSN) Mark III data system testing. Status of the Viking DSN discrepancy reporting system as well as the status of Viking related tracking and command support were also considered. Operations support of Viking events and radio science activities were examined. Current progress on the major reconfiguration of the Network is presented in the context of support for the Viking extended mission.

Howe, T. W.↗
DOE OSTI2020

PyDDA: A Pythonic Direct Data Assimilation Framework for Wind Retrievals

This software assimilates data from an arbitrary number of weather radars together with other spatial wind fields (eg numerical weather forecasting model data) in order to retrieve high resolution three dimensional wind fields. PyDDA uses NumPy and SciPy’s optimization techniques combined with the Python Atmospheric Radiation Measurement (ARM) Radar Toolkit (Py-ART) in order to create wind fields using the 3D variational technique (3DVAR). PyDDA is hosted and distributed on GitHub at https://github.com/openradar/PyDDA. PyDDA has the potential to be used by the atmospheric science community to develop high resolution wind retrievals from radar networks. These retrievals can be used for the evaluation of numerical weather forecasting models and plume modelling. This paper shows how wind fields from 2 NEXt generation RADar (NEXRAD) WSR-88D radars and the High Resolution Rapid Refresh can be assimilated together using PyDDA to create a high resolution wind field inside Hurricane Florence.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

The INSU and DMN network of ST radars

Due to their capabilities of measuring wind profiles with good time and height resolution, Stratosphere-Troposphere (ST) are well adapted to carry out atmospheric research. In France, a Very High Frequency (VHF) and an Ultrahigh Frequency (UHF) ST radar are working for research purposes. The INSU (Institut National des Sciences de l'Univers) and the DMN (Direction de la Meteorologie Nationale) networks are discussed.

Petitdidier, M.↗
NASA NTRS1999

Development of Formation Deployment and Intialization Concepts

NASA's Cross-Cutting Technology Development Program identified formation flying as a key enabler for the next generation Earth and Sciences campaign. It is hoped that this technology will allow a distributed network of autonomous satellites to act collaboratively as a single collective unit paving the way for extensive co-observing campaigns, coordinated multi-point observing programs, improved space-based interferometry, and entirely new approaches to conducting science. APL as a team member with GSFC, funded by the Earth Sciences and Technology Organization (ESTO), investigated formation deployment and initialization concepts which is central to the formation flying concept. This paper presents the analytical approach and preliminary results of the study. The study investigated a simple mission involving the deployment of six micro-satellites, one at a time, from a bus. At the initialization state, the satellites fly in an along-track trajectory separated by nominal spacing. The study entailed the development of a two-body (bus and satellite) relative motion propagator based on Clohessy-Wiltshire (C-W) equations with drag from which the relative motion of the micro-satellites is deduced. This code was used to investigate cluster development characteristics subject to "tip-off' (ejection) conditions. Results indicate that cluster development is very sensitive to the ballistic coefficients of the bus and satellites, and to relative ejection velocity. This information can be used to identify optimum deployment parameters, along with accuracy bounds for a particular mission, and to develop a cluster control strategy minimizing global fuel and cost. A suitable control strategy concept has been identified, however, it needs to be developed further.

Badesha, Surjit S.↗
DOE OSTI2024

Variational data augmentation for a learning-based granular predictive model of power outages

As the trend in climate change continues, extreme weather events are expected to occur with increasing frequency and severity and pose a significant threat to the electric power infrastructure. Regardless of the efforts a utility puts towards hardening the grid, storm-induced damage to the utility assets such as cables and distributed energy resources (DERs) that are particularly vulnerable to such events is unavoidable. Access to a highly granular, in space and time, outage forecasting tool with long lead times (i.e., days ahead) will enhance the efficiency of service restoration efforts. Here, in this study, we propose to develop and implement a multi-model framework as an operational tool based on a granular and multi-day outage forecasting model using operational numerical weather prediction model forecasts and detailed component outage information. An innovative two-layered recurrent neural network, i.e., a long-short-term-memory (LSTM)-based variational autoencoder (VAE) framework and a sliding window are used to address the uneven distribution of different types of weather events and make better use of the time-series data. Case studies are performed to demonstrate the performance of the new framework.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Identifying strengths and weaknesses of methods for computational network inference from single-cell RNA-seq data

Single-cell RNA-sequencing (scRNA-seq) offers unparalleled insight into the transcriptional programs of different cellular states by measuring the transcriptome of thousands of individual cells. An emerging problem in the analysis of scRNA-seq is the inference of transcriptional gene regulatory networks and a number of methods with different learning frameworks have been developed to address this problem. Here, we present an expanded benchmarking study of eleven recent network inference methods on seven published scRNA-seq datasets in human, mouse, and yeast considering different types of gold standard networks and evaluation metrics. We evaluate methods based on their computing requirements as well as on their ability to recover the network structure. We find that, while most methods have a modest recovery of experimentally derived interactions based on global metrics such as Area Under the Precision Recall curve, methods are able to capture targets of regulators that are relevant to the system under study. Among the top performing methods that use only expression were SCENIC, PIDC, MERLIN or Correlation. Addition of prior biological knowledge and the estimation of transcription factor activities resulted in the best overall performance with the Inferelator and MERLIN methods that use prior knowledge outperforming methods that use expression alone. We found that imputation for network inference did not improve network inference accuracy and could be detrimental. Comparisons of inferred networks for comparable bulk conditions showed that the networks inferred from scRNA-seq datasets are often better or at par with the networks inferred from bulk datasets. Our analysis should be beneficial in selecting methods for network inference. At the same time, this highlights the need for improved methods and better gold standards for regulatory network inference from scRNAseq datasets.

59 BASIC BIOLOGICAL SCIENCES↗