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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 NTRS2018

Development of High Power Hall Thruster Systems to Enable the NASA Exploration Vision

The next phase of space exploration missions requires high power Solar Electric Propulsion (SEP) systems for large-scale science missions and cargo transportation. Development is underway at Aerojet Rocketdyne on Hall thruster systems that are intended to bracket the needs of future NASA SEP missions in support of space exploration. The Advanced Electric Propulsion System (AEPS) program is developing and qualifying a 13.3kW Hall thruster system to be demonstrated on the Power and Propulsion Element (PPE), which is intended to be the first element of a Lunar Outpost Platform - Gateway (LOP-G). The NextSTEP program is integrating a nested Hall thruster into a 100 kW system and testing it for 100 hours. These two programs will provide a path to efficient in-space propulsion that will allow NASA to transfer the large amounts of cargo that is needed to support human missions - first to the moon and then on to Mars. The Advanced Electric Propulsion System (AEPS) program is completing development, qualification and delivery of five flight 13.3kW EP systems to NASA. The flight AEPS system includes a magnetically shielded long-life Hall thruster, Power Processing Unit (PPU) and a Xenon Flow Controller (XFC). The Hall thruster, developed and demonstrated by NASA, operates at input powers up to 12.5 kW while providing a specific impulse over an estimated 2800s at an input voltage of 600V. The power processor is designed to accommodate an input voltage range of 95-140V, consistent with operation beyond the orbit of Mars. The integrated system input power is continuously throttleable between 3 and 13.3kW. Component level testing of the EP String has begun with prototype hardware. The NextSTEP program is developing a 100kW Electric Propulsion (EP) system using a nested Hall thruster designed for powers up to 250kW, a modular power processor and a modular mass flow controller. While the program objective is to operate the integrated EP system continuously at 100kW for 100 hours to demonstrate thermal stability and support the development of system life time models, it builds on decades of experience with long-life Hall thrusters and the design is evolvable to a capability of 250kW. Design upgrades that demonstrate the 100kW EP system have been completed and tested. Aerojet Rocketdyne is excited to support NASA as it extends human reach into deep space and believes that these programs will provide the propulsion to make such missions affordable and sustainable. These systems provide NASA with a range of options to power its deep space transport vehicles. This paper presents the mission requirements for supporting the NASA exploration vision, as well as the status for the high power Hall thruster systems in development.

Jackson, Jerry↗
NASA NTRS2019

13kW Advanced Electric Propulsion Flight System Development and Qualification

The next phase of robotic and human deep space exploration missions requires high performance, high power solar electric propulsion systems for large-scale science missions and cargo transportation. Aerojet Rocketdyne's Advanced Electric Propulsion System (AEPS) program is completing development and qualification of a 13kW flight EP system to support NASA exploration. The first use of the AEPS is planned for the NASA Power & Propulsion Element, which is the first element of NASA's cis-lunar Gateway. The flight AEPS system includes a magnetically shielded long-life Hall thruster, power processing unit (PPU), and xenon flow controller (XFC). The Hall thruster, originally developed and demonstrated by NASA's Glenn Research Center and the Jet Propulsion Laboratory, operates at input powers up to 13.3kW while providing a specific impulse over 2600s at an input voltage of 600V. The power processor is designed to accommodate an input voltage range of 95 to 140V, consistent with operation beyond the orbit of Mars. The integrated system is continuously throttleable between 3 and 13.3kW. The program has completed testing of the Technology Development Units and is progressing into the Engineering Development Unit test phase and the final design phase to Critical Design Review (CDR). This paper will present the high power AEPS system capabilities, overall program and design status and the latest test results for the 13kW flight system development as well as the plans for the development and qualification effort of the EP string.

in-space propulsion↗
NASA NTRS2019

13kW Advanced Electric Propulsion Flight System Development and Qualification

The next phase of robotic and human deep space exploration missions requires high performance, high power solar electric propulsion systems for large-scale science missions and cargo transportation. Aerojet Rocketdyne's Advanced Electric Propulsion System (AEPS) program is completing development and qualification of a 13kW flight EP system to support NASA exploration. The first use of the AEPS is planned for the NASA Power & Propulsion Element, which is the first element of NASA's cis-lunar Gateway. The flight AEPS system includes a magnetically shielded long-life Hall thruster, power processing unit (PPU), and xenon flow controller (XFC). The Hall thruster, originally developed and demonstrated by NASA's Glenn Research Center and the Jet Propulsion Laboratory, operates at input powers up to 13.3kW while providing a specific impulse over 2600s at an input voltage of 600V. The power processor is designed to accommodate an input voltage range of 95 to 140V, consistent with operation beyond the orbit of Mars. The integrated system is continuously throttleable between 3 and 13.3kW. The program has completed testing of the Technology Development Units and is progressing into the Engineering Development Unit test phase and the final design phase to Critical Design Review (CDR). This paper will present the high power AEPS system capabilities, overall program and design status and the latest test results for the 13kW flight system development as well as the plans for the development and qualification effort of the EP string.

in-space propulsion↗
NASA NTRS2005

Large-Scale Experimental Planetary Science Meets Planetary Defense: Deorbiting an Asteroidal Satellite

Other than remote-sensing and spacecraft-derived data, the only information that exists regarding the physical and chemical properties of asteroids is that inferred through calculations, numerical simulations, extrapolation of experiments, and meteorite studies. Our understanding of the dynamics of accretion of planetesimals, collisional disruption of asteroids, and the macroscopic, shock-induced modification of the surfaces of such small objects is also, for the most part, founded on similar inferences. While considerable strides have been made in improving the state of asteroid science, too many unknowns remain to assert that we understand the parameters necessary for the more practical problem of deflecting an asteroid or asteroid pair on an Earth-intersecting trajectory. Many of these deficiencies could be reduced or eliminated by intentionally deorbiting an asteroidal satellite and monitoring the resulting collision between it and the primary asteroid, a capability that is well within the limitations of current technology.

Cintala, M. J.↗
NASA NTRS1993

The scaling of impact processes in planetary sciences

Current approaches to scaling laws for planetary impact processes are reviewed. Only the simplest concepts and first-order theories that are the most determined are presented. Experimental, analytical, and calculative approaches to determining scaling laws are examined, including the advantages and deficiencies of each.

Holsapple, K. A.↗
NASA NTRS2013

Trajectory Reconstruction and Uncertainty Analysis Using Mars Science Laboratory Pre-Flight Scale Model Aeroballistic Testing

As part of the Mars Science Laboratory (MSL) trajectory reconstruction effort at NASA Langley Research Center, free-flight aeroballistic experiments of instrumented MSL scale models was conducted at Aberdeen Proving Ground in Maryland. The models carried an inertial measurement unit (IMU) and a flush air data system (FADS) similar to the MSL Entry Atmospheric Data System (MEADS) that provided data types similar to those from the MSL entry. Multiple sources of redundant data were available, including tracking radar and on-board magnetometers. These experimental data enabled the testing and validation of the various tools and methodologies that will be used for MSL trajectory reconstruction. The aerodynamic parameters Mach number, angle of attack, and sideslip angle were estimated using minimum variance with a priori to combine the pressure data and pre-flight computational fluid dynamics (CFD) data. Both linear and non-linear pressure model terms were also estimated for each pressure transducer as a measure of the errors introduced by CFD and transducer calibration. Parameter uncertainties were estimated using a "consider parameters" approach.

Lugo, Rafael A.↗
NASA NTRS2000

Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering

The term "Grid" refers to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. The vision for NASN's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks that will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: The scientist / design engineer whose primary interest is problem solving (e.g., determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user if the tool designer: The computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. This paper describes the current state of IPG (the operational testbed), the set of capabilities being put into place for the operational prototype IPG, as well as some of the longer term R&D tasks.

Johnston, William E.↗
NASA NTRS2000

Information Power Grid: Distributed High-Performance Computing and Large-Scale Data Management for Science and Engineering

We use the term "Grid" to refer to distributed, high performance computing and data handling infrastructure that incorporates geographically and organizationally dispersed, heterogeneous resources that are persistent and supported. This infrastructure includes: (1) Tools for constructing collaborative, application oriented Problem Solving Environments / Frameworks (the primary user interfaces for Grids); (2) Programming environments, tools, and services providing various approaches for building applications that use aggregated computing and storage resources, and federated data sources; (3) Comprehensive and consistent set of location independent tools and services for accessing and managing dynamic collections of widely distributed resources: heterogeneous computing systems, storage systems, real-time data sources and instruments, human collaborators, and communications systems; (4) Operational infrastructure including management tools for distributed systems and distributed resources, user services, accounting and auditing, strong and location independent user authentication and authorization, and overall system security services The vision for NASA's Information Power Grid - a computing and data Grid - is that it will provide significant new capabilities to scientists and engineers by facilitating routine construction of information based problem solving environments / frameworks. Such Grids will knit together widely distributed computing, data, instrument, and human resources into just-in-time systems that can address complex and large-scale computing and data analysis problems. Examples of these problems include: (1) Coupled, multidisciplinary simulations too large for single systems (e.g., multi-component NPSS turbomachine simulation); (2) Use of widely distributed, federated data archives (e.g., simultaneous access to metrological, topological, aircraft performance, and flight path scheduling databases supporting a National Air Space Simulation systems}; (3) Coupling large-scale computing and data systems to scientific and engineering instruments (e.g., realtime interaction with experiments through real-time data analysis and interpretation presented to the experimentalist in ways that allow direct interaction with the experiment (instead of just with instrument control); (5) Highly interactive, augmented reality and virtual reality remote collaborations (e.g., Ames / Boeing Remote Help Desk providing field maintenance use of coupled video and NDI to a remote, on-line airframe structures expert who uses this data to index into detailed design databases, and returns 3D internal aircraft geometry to the field); (5) Single computational problems too large for any single system (e.g. the rotocraft reference calculation). Grids also have the potential to provide pools of resources that could be called on in extraordinary / rapid response situations (such as disaster response) because they can provide common interfaces and access mechanisms, standardized management, and uniform user authentication and authorization, for large collections of distributed resources (whether or not they normally function in concert). IPG development and deployment is addressing requirements obtained by analyzing a number of different application areas, in particular from the NASA Aero-Space Technology Enterprise. This analysis has focussed primarily on two types of users: the scientist / design engineer whose primary interest is problem solving (e.g. determining wing aerodynamic characteristics in many different operating environments), and whose primary interface to IPG will be through various sorts of problem solving frameworks. The second type of user is the tool designer: the computational scientists who convert physics and mathematics into code that can simulate the physical world. These are the two primary users of IPG, and they have rather different requirements. The results of the analysis of the needs of these two types of users provides a broad set of requirements that gives rise to a general set of required capabilities. The IPG project is intended to address all of these requirements. In some cases the required computing technology exists, and in some cases it must be researched and developed. The project is using available technology to provide a prototype set of capabilities in a persistent distributed computing testbed. Beyond this, there are required capabilities that are not immediately available, and whose development spans the range from near-term engineering development (one to two years) to much longer term R&D (three to six years). Additional information is contained in the original.

Johnston, William E.↗
NASA NTRS1995

Visualization: A pathway to enhanced scientific productivity in the expanding missions of Space and Earth Sciences

The movement toward the solution of problems involving large-scale system science, the ever-increasing capabilities of three-dimensional, time-dependent numerical models, and the enhanced capabilities of 'in situ' and remote sensing instruments bring a new era of scientific endeavor that requires an important change in our approach to mission planning and the task of data reduction and analysis. Visualization is at the heart of the requirements for a much-needed enhancement in scientific productivity as we face these new challenges. This article draws a perspective on the problem as it crosses discipline boundaries from solar physics to atmospheric and ocean sciences. It also attempts to introduce visualization as a new approach to scientific discovery and a tool which expedites and improves our insight into physically complex problems. A set of simple illustrations demonstrates a number of visualization techniques and the discussion emphasizes the trial-and-error and search-and-discover modes that are necessary for the techniques to reach their full potential. Further discussions also point to the importance of integrating data access, management, mathematical operations, and visualization into a single system. Some of the more recent developments in this area are reviewed.

Szuszczewicz, E. P.↗
NASA NTRS2016

IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

visualization↗
NASA NTRS2002

The Computing and Data Grid Approach: Infrastructure for Distributed Science Applications

With the advent of Grids - infrastructure for using and managing widely distributed computing and data resources in the science environment - there is now an opportunity to provide a standard, large-scale, computing, data, instrument, and collaboration environment for science that spans many different projects and provides the required infrastructure and services in a relatively uniform and supportable way. Grid technology has evolved over the past several years to provide the services and infrastructure needed for building 'virtual' systems and organizations. We argue that Grid technology provides an excellent basis for the creation of the integrated environments that can combine the resources needed to support the large- scale science projects located at multiple laboratories and universities. We present some science case studies that indicate that a paradigm shift in the process of science will come about as a result of Grids providing transparent and secure access to advanced and integrated information and technologies infrastructure: powerful computing systems, large-scale data archives, scientific instruments, and collaboration tools. These changes will be in the form of services that can be integrated with the user's work environment, and that enable uniform and highly capable access to these computers, data, and instruments, regardless of the location or exact nature of these resources. These services will integrate transient-use resources like computing systems, scientific instruments, and data caches (e.g., as they are needed to perform a simulation or analyze data from a single experiment); persistent-use resources. such as databases, data catalogues, and archives, and; collaborators, whose involvement will continue for the lifetime of a project or longer. While we largely address large-scale science in this paper, Grids, particularly when combined with Web Services, will address a broad spectrum of science scenarios. both large and small scale.

Johnston, William E.↗
NASA NTRS1998

Fractal Characterization of Multitemporal Scaled Remote Sensing Data

Scale is an "innate" concept in geographic information systems. It is recognized as something that is intrinsic to the ingestion, storage, manipulation, analysis, modeling, and output of space and time data within a GIS purview, yet the relative meaning and ramifications of scaling spatial and temporal data from this perspective remain enigmatic. As GISs become more sophisticated as a product of more robust software and more powerful computer systems, there is an urgent need to examine the issue of scale, and its relationship to the whole body of spatiotemporal data, as imparted in GISS. Scale is fundamental to the characterization of geo-spatial data as represented in GISS, but we have relatively little insight on the effects of, or how to measure the effects of, scale in representing multiscaled data; i.e., data that are acquired in different formats (e.g., map, digital) and exist in varying spatial, temporal, and in the case of remote sensing data, radiometric, configurations. This is particularly true in the emerging era of Integrated GISs (IGIS), wherein spatial data in a variety of formats (e.g., raster, vector) are combined with multiscaled remote sensing data, capable of performing highly sophisticated space-time data analyses and modeling. Moreover, the complexities associated with the integration of multiscaled data sets in a multitude of formats are exacerbated by the confusion of what the term "scale" is from a multidisciplinary perspective; i.e., "scale" takes on significantly different meanings depending upon one's disciplinary background and spatial perspective which can lead to substantive confusion in the input, manipulation, analyses, and output of IGISs (Quattrochi, 1993). Hence, we must begin to look at the universality of scale and begin to develop the theory, methods, and techniques necessary to advance knowledge on the "Science of Scale" across a wide number of spatial disciplines that use GISs.

Quattrochi, Dale A.↗
NASA NTRS2000

Fractal Characterization of Multitemporal Remote Sensing Data

Scale is an "innate" concept in geographic information systems. It is recognized as something that is intrinsic to the capture, storage, manipulation, analysis, modeling, and output of space and time data within a GIS purview, yet the relative meaning and ramifications of scaling spatial and temporal data from this perspective remain enigmatic. As GISs become more sophisticated as a product of more robust software and more powerful computer systems, there is an urgent need to examine the issue of scale, and its relationship to the whole body of spatiotemporal data, as imparted in GISs. Scale is fundamental to the characterization of geo-spatial data as represented in GISs, but we have relatively little insight on how to measure the effects of scale in representing data that are acquired in different formats and exist in varying spatial, temporal and radiometric configurations. Moreover, the complexities associated with the integration of multiscaled data sets in a multitude of formats are exacerbated by the confusion of what the term "scale" is from a multidisciplinary perspective. "Scale" takes on significantly different meanings depending upon one's disciplinary background and spatial perspective which lead to substantial confusion in the input, manipulation, analysis, and output operations. Hence, we must begin to look at the universality of scale and begin to develop the theory, methods, and techniques necessary to advance knowledge on the "Science of Scale' across all disciplines that use GISs.

Quattrochi, Dale A.↗
NASA NTRS2001

Fractal Characterization of Multitemporal Remote Sensing Data

Scale is an 'innate' concept in geographical information systems (GIS). It is recognized as something that is intrinsic to the capture, storage, manipulation, analysis, modelling, and output of space and time data within a GIS purview, yet the relative meaning and ramifications of scaling spatial and temporal data from this perspective remain enigmatic. As GIS becomes more sophisticated as a product of more robust software and more powerful computer systems, there is an urgent need to examine the issue of scale, and its relationship to the whole body of spatiotemporal data, as imparted in GIS. Scale is fundamental to the characterization of geo-spatial data as represented in GIS, but we have relatively little insight on how to measure the effects of scale in representing data that are acquired in different formats and exist in varying spatial, temporal and radiometric configurations. Moreover, the complexities associated with the integration of multi-scaled data sets in a multitude of formats are exacerbated by the confusion of what the term 'scale' means from a multidisciplinary perspective. 'Scale' takes on significantly different meanings depending upon one's disciplinary background and spatial perspective, which can lead to substantial confusion in the input, manipulation, analysis, and output operations. Hence, we must begin to look at the universality of scale and begin to develop the theory, methods and techniques necessary to advance knowledge on the 'Science of scale' across all disciplines that use GIS.

Quattrochi, Dale A.↗
NASA NTRSDate not supplied

Foundation AI Models for Science

Foundation Models (FM) are AI models that are designed to replace a task or an application specific model. These FM can be applied to many different downstream applications. These FM are trained using self supervised techniques and can be built on any type of sequence data. The use of self supervised learning removes the hurdle for developing a large labeled dataset for training. Most FM use transformer architecture utilizes the notion of self attention which allows the network to model the influence of distant data points to each other both in space and time. The FM models exhibit emergent properties that are induced from the data. FM can be an important tool for science. The scale of these models results in better performance for different downstream applications and these applications show better accuracy over models built from scratch. FM drastically reduces the cost of entry to build different downstream applications both in time and effort. FM for selected science datasets such as optical satellite data, can accelerate applications ranging from data quality monitoring, feature detection and prediction. FM can make it easier to infuse AI into scientific research by removing the training data bottleneck and increasing the use of science data.

Manil Maskey↗