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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Data Acquisition System Architecture and Capabilities At NASA GRC Plum Brook Station's Space Environment Test Facilities

Very large space environment test facilities present unique engineering challenges in the design of facility data systems. Data systems of this scale must be versatile enough to meet the wide range of data acquisition and measurement requirements from a diverse set of customers and test programs, but also must minimize design changes to maintain reliability and serviceability. This paper presents an overview of the common architecture and capabilities of the facility data acquisition systems available at two of the world?s largest space environment test facilities located at the NASA Glenn Research Center?s Plum Brook Station in Sandusky, Ohio; namely, the Space Propulsion Research Facility (commonly known as the B-2 facility) and the Space Power Facility (SPF). The common architecture of the data systems is presented along with details on system scalability and efficient measurement systems analysis and verification. The architecture highlights a modular design, which utilizes fully-remotely managed components, enabling the data systems to be highly configurable and support multiple test locations with a wide-range of measurement types and very large system channel counts.

Evans, Richard K.↗
NASA NTRS2014

Data Acquisition System Architecture and Capabilities at NASA GRC Plum Brook Station's Space Environment Test Facilities

Very large space environment test facilities present unique engineering challenges in the design of facility data systems. Data systems of this scale must be versatile enough to meet the wide range of data acquisition and measurement requirements from a diverse set of customers and test programs, but also must minimize design changes to maintain reliability and serviceability. This paper presents an overview of the common architecture and capabilities of the facility data acquisition systems available at two of the world's largest space environment test facilities located at the NASA Glenn Research Center's Plum Brook Station in Sandusky, Ohio; namely, the Space Propulsion Research Facility (commonly known as the B-2 facility) and the Space Power Facility (SPF). The common architecture of the data systems is presented along with details on system scalability and efficient measurement systems analysis and verification. The architecture highlights a modular design, which utilizes fully-remotely managed components, enabling the data systems to be highly configurable and support multiple test locations with a wide-range of measurement types and very large system channel counts.

Evans, Richard K.↗
NASA NTRSDate not supplied

Progressive Development of Fleet Management Capabilities for a High Density Vertiplex Environment

The High Density Vertiplex (HDV) Sub-Project, as part of NASA’s Advanced Air Mobility (AAM) Project, has been developing a reference automation architecture with a far-term view of scalable, high-density operations in and around vertiport terminal areas. One of the components of that architecture under development has been focused on fleet management capabilities to support the management of multiple AAM operations from a supervisory role of a fleet manager. This capability relies on connectivity and information exchanges with other services for airspace and vertiport management as well as with flight crews responsible for operation execution. This paper will present this capability with a focus on its user interface developments as well as its integration into the simulation and flight testing performed as part of the HDV research roadmap.

AAM↗
NASA NTRSDate not supplied

Progressive Development of Fleet Management Capabilities for a High Density Vertiplex Environment

The High Density Vertiplex (HDV) Sub-Project, as part of NASA’s Advanced Air Mobility (AAM) Project, has been developing a reference automation architecture with a far-term view of scalable, high-density operations in and around vertiport terminal areas. One of the components of that architecture under development has been focused on fleet management capabilities to support the management of multiple AAM operations from a supervisory role of a fleet manager. This capability relies on connectivity and information exchanges with other services for airspace and vertiport management as well as with flight crews responsible for operation execution. This paper will present this capability with a focus on its user interface developments as well as its integration into the simulation and flight testing performed as part of the HDV research roadmap.

high density vertiport↗
NASA NTRS1996

Unstructured Adaptive Grid Computations on an Array of SMPs

Dynamic load balancing is necessary for parallel adaptive methods to solve unsteady CFD problems on unstructured grids. We have presented such a dynamic load balancing framework called JOVE, in this paper. Results on a four-POWERnode POWER CHALLENGEarray demonstrated that load balancing gives significant performance improvements over no load balancing for such adaptive computations. The parallel speedup of JOVE, implemented using MPI on the POWER CHALLENCEarray, was significant, being as high as 31 for 32 processors. An implementation of JOVE that exploits 'an array of SMPS' architecture was also studied; this hybrid JOVE outperformed flat JOVE by up to 28% on the meshes and adaption models tested. With large, realistic meshes and actual flow-solver and adaption phases incorporated into JOVE, hybrid JOVE can be expected to yield significant advantage over flat JOVE, especially as the number of processors is increased, thus demonstrating the scalability of an array of SMPs architecture.

Biswas, Rupak↗
NASA NTRSDate not supplied

Attitude Control System for the Solar Cruiser and Future Solar Sail Missions

NASA’s Marshall Space Flight Center (MSFC) is developing the Solar Cruiser solar sail mission to continue to mature solar sail propulsion. Solar Cruiser is a 95kg spacecraft capable of deploying a 1654 square meter solar sail. Solar Cruiser will demonstrate science observation capabilities and navigation in sub Lagrange Point (L1) halo-orbits. Solar Cruiser sail technology can be scaled to larger solar sail missions, including attitude control system and sail membrane and boom technology. Larger solar sails could be used to image the solar poles, enabling Solar Polar Imager (SPI) missions currently prohibitively expensive for propellant-based propulsion methods. Similarly, solar sails can be used to create artificial equilibria and indefinite station-keeping at locations sunward of Lagrange Point one, L1, along the Sun-Earth line (SEL), which can provide space-weather monitor and prediction and reveal discoveries about our Sun and solar system. To achieve the high characteristic acceleration required for larger solar sail missions such as SPI, a solar sail area of approximately 7000 square-meters would be needed. In comparison, the largest solar sail flown to date is the JAXA’s IKAROS mission with a 196 square-meters sail. Attitude control an SPI-size sail poses challenges, including station keeping under solar sail induced torques and thrust vector pointing. In this study, the scalability of the Solar Cruiser sail attitude control architecture to larger sails is studied, such as pointing control performance, mass and power requirements. Solar Cruiser attitude control actuators include propellant-based, such as ion engines, and propellantless options which create differential solar pressures to generate spacecraft torques. Propellantless attitude control includes active translation of the center of mass, Reflectivity Control Devices (RCDs), and tip vanes. The study summarizes the scalability characteristics of the different attitude control architectures for larger solar sail missions.

Solar Sail Propulsion↗
NASA NTRS2008

Real-Time Wavefront Control for the PALM-3000 High Order Adaptive Optics System

We present a cost-effective scalable real-time wavefront control architecture based on off-the-shelf graphics processing units hosted in an ultra-low latency, high-bandwidth interconnect PC cluster environment composed of modules written in the component-oriented language of nesC. The architecture enables full-matrix reconstruction of the wavefront at up to 2 KHz with latency under 250 us for the PALM-3000 adaptive optics systems, a state-of-the-art upgrade on the 5.1 meter Hale Telescope that consists of a 64 x 64 subaperture Shack-Hartmann wavefront sensor and a 3368 active actuator high order deformable mirror in series with a 241 active actuator tweeter DM. The architecture can easily scale up to support much larger AO systems at higher rates and lower latency.

GPU↗
NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRSDate not supplied

Big-data Efficient and Automated Science Transfer (BEAST): An Open-Source Software Architecture for Arc Jet Data Management, Modeling, and Automation

Big-data Efficient and Automated Science Transfer (BEAST) is a facility data management application developed for the NASA Ames arc jet facilities. The current decentralized data management practices limit statistical tracking, synchronization between video/time series, search capability, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRSDate not supplied

Big-data Efficient Automated Science Transfer (BEAST): an open-source software architecture for arc jet data management, modeling, and automation

Big-data Efficient and Automated Science Transfer (BEAST) was conceived to address the existing ground testing data management of the NASA Ames arc jet facilities (e.g., manually entered Excel files and USB drive data transfers). These data management practices were seen as a choke point for future thermal protection system (TPS) development as they limit statistical tracking, resolution of diagnostics, coordination between video/time series, data throughput, and data processing speed/efficiency. Consequently, BEAST was developed to provide a new data infrastructure with streamlined data collection, processing, transfer, and analysis. This new framework also seeks to implement the FAIR principles of data stewardship: Findable, Accessible, Interoperable, and Reusable. The BEAST framework is based on a combination of the Python Django web framework and the Python data stack to provide a monolithic, open-source platform for data management, automation, and machine learning. This architecture was chosen for maintainability and scalability for a small, in-house development team. This paper will describe the application framework, deployment, and discuss the benefits and future plans for the system.

Data management↗
NASA NTRSDate not supplied

Segmented Hexagonal Antenna Reflector Concentrically Stacked Using Shape Memory Composite Tubular Hinges

A new architecture for solid surface reflector antennas scalable to sizes greater than 10 m is presented. The design combines the compact, lightness and simplicity characteristics of advanced deployable structures to create sub-reflectors that can be assembled in space into larger units using a robotic arm and operations under development by a related parallel project. The seven-panel hexagonal sub-reflector is divided into hexagonal panels of that efficiently stack concentrically and vertically. The central panel is connected to each side panel on the back side by a pair of tubular shape memory composite hinges that enable the required deployment kinematics. A secondary mechanism ultimately closes the interpanel gap. The focus of the paper is on the development of the sub-reflector elements, namely the tubular hinges that use embedded heaters and sensors for actuation, the actuation mechanisms, and the lightweight sandwich construction panels. A finite element analysis parametric study assessed how design features of the hinge affect its stowage and deployment dynamics. The component fabrication and testing plans for the two-panel assembly breadboard model are outlined.

deployable reflectors↗
NASA NTRSDate not supplied

Segmented Hexagonal Antenna Reflector Concentrically Stacked Using Shape Memory Composite Tubular Hinges

A new architecture for solid surface reflector antennas scalable to sizes greater than 10 m is presented. The design combines the compact, lightness and simplicity characteristics of advanced deployable structures to create sub-reflectors that can be assembled in space into larger units using a robotic arm and operations under development by a related parallel project. The seven-panel hexagonal sub-reflector is divided into hexagonal panels of that efficiently stack concentrically and vertically. The central panel is connected to each side panel on the back side by a pair of tubular shape memory composite hinges that enable the required deployment kinematics. A secondary mechanism ultimately closes the interpanel gap. The focus of the paper is on the development of the sub-reflector elements, namely the tubular hinges that use embedded heaters and sensors for actuation, the actuation mechanisms, and the lightweight sandwich construction panels. A finite element analysis parametric study assessed how design features of the hinge affect its stowage and deployment dynamics. The component fabrication and testing plans for the two-panel assembly breadboard model are outlined.

deployable reflectors↗
NASA NTRSDate not supplied

Segmented Solid Surface Reflector Concentrically Stacked With Tubular Shape Memory Composite Hinges

A new architecture for solid surface reflector antennas scalable to sizes greater than 10 m is presented. The design uses compact, light, and simple advanced deployable structures to create sub-reflectors that can be assembled in space into larger units using a robotic arm. The seven-panel hexagonal sub-reflector is divided into hexagonal panels that stack concentrically and vertically. The central panel is connected to each side panel on the back side by a pair of tubular shape memory composite hinges that enable the required deployment kinematics with controlled dynamics. A secondary mechanism closes the interpanel gap. The focus of the paper is on the development of the sub-reflector elements, namely the tubular hinges that use embedded heaters and sensors for triggering and control, the actuation mechanisms, and the lightweight sandwich construction reflector panels. A parametric study using finite element analyses was conducted to assess how design features of the hinge affect its stowage and deployment dynamics. The preliminary component fabrication and testing results for the two-panel assembly breadboard model are outlined. Finally, the results of the test campaign with the brassboard reflector model are presented. A comparison of deployed reflector surface deviation between the measured surface after the stowage and deployment process and the pre-test scans and the nominal surface revealed root mean square errors of less than 1 mm, as required by X-band radiofrequency transmission.

gravity offloading↗
NASA NTRSDate not supplied

Segmented Hexagonal Antenna Reflector Concentrically Stacked Using Shape Memory Composite Tubular Hinges

A new architecture for solid surface reflector antennas scalable to sizes greater than 10 m is presented. The design uses compact, light, and simple advanced deployable structures to create sub-reflectors that can be assembled in space into larger units using a robotic arm. The seven-panel hexagonal sub-reflector is divided into hexagonal panels that stack concentrically and vertically. The central panel is connected to each side panel on the back side by a pair of tubular shape memory composite hinges that enable the required deployment kinematics with controlled dynamics. A secondary mechanism closes the interpanel gap. The focus of the paper is on the development of the sub-reflector elements, namely the tubular hinges that use embedded heaters and sensors for triggering and control, the actuation mechanisms, and the lightweight sandwich construction reflector panels. A parametric study using finite element analyses was conducted to assess how design features of the hinge affect its stowage and deployment dynamics. The preliminary component fabrication and testing results for the two-panel assembly breadboard model are outlined. Finally, the results of the test campaign with the brassboard reflector model are presented. A comparison of deployed reflector surface deviation between the measured surface after the stowage and deployment process and the pre-test scans and the nominal surface revealed root mean square errors of less than 1 mm, as required by X-band radiofrequency transmission.

gravity offloading methods↗
NASA NTRS2022

A Framework for Dynamic Architecture and Functional Allocations for Increasing Airspace Autonomy

To enable scalability of air travel for use cases such as cargo delivery, it is anticipated that future air traffic operations will involve unmanned aircraft operated by remote pilots. Of particular interest are schemes where a small number of pilots operate a large number of vehicles, mitigating high cost and pilot shortage issues. Such architectures require increased levels of automation and supervisory control modes. They also require ensuring safe operations when the command and control link to the vehicle is degraded or lost completely, rendering the vehicle autonomous. To evaluate these variable and dynamic architectures, this paper will present a framework for decomposing the functions necessary to ensure safe, orderly, and expeditious air travel, assessing the agents in the system, and classifying the levels of autonomy. Then, an example allocation to agents of roles for the function of separation assurance is presented, highlighting the dependency of the allocation on three main factors: time criticality of a potential separation violation, the ratio of pilots to vehicles, and the loss of the command and control link.

air traffic management, function allocation, auton↗
NASA NTRS2022

A Framework for Dynamic Architecture and Functional Allocations for Increasing Airspace Autonomy

To enable scalability of air travel for use cases such as cargo delivery, it is anticipated that future air traffic operations will involve unmanned aircraft operated by remote pilots. Of particular interest are schemes where a small number of pilots operate a large number of vehicles, mitigating high cost and pilot shortage issues. Such architectures require increased levels of automation and supervisory control modes. They also require ensuring safe operations when the command and control link to the vehicle is degraded or lost completely, rendering the vehicle autonomous. To evaluate these variable and dynamic architectures, this paper will present a framework for decomposing the functions necessary to ensure safe, orderly, and expeditious air travel, assessing the agents in the system, and classifying the levels of autonomy. Then, an example allocation to agents of roles for the function of separation assurance is presented, highlighting the dependency of the allocation on three main factors; time criticality of a potential separation violation, the ratio of pilots to vehicles, and the loss of the command and control link.

air traffic management↗
NASA NTRS2011

Error Propagation Analysis in the SAE Architecture Analysis and Design Language (AADL) and the EDICT Tool Framework

This report documents the capabilities of the EDICT tools for error modeling and error propagation analysis when operating with models defined in the Architecture Analysis & Design Language (AADL). We discuss our experience using the EDICT error analysis capabilities on a model of the Scalable Processor-Independent Design for Enhanced Reliability (SPIDER) architecture that uses the Reliable Optical Bus (ROBUS). Based on these experiences we draw some initial conclusions about model based design techniques for error modeling and analysis of highly reliable computing architectures.

LaValley, Brian W.↗