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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Space Generic Open Avionics Architecture (SGOAA) standard specification

The purpose of this standard is to provide an umbrella set of requirements for applying the generic architecture interface model to the design of a specific avionics hardware/software system. This standard defines a generic set of system interface points to facilitate identification of critical interfaces and establishes the requirements for applying appropriate low level detailed implementation standards to those interface points. The generic core avionics system and processing architecture models provided herein are robustly tailorable to specific system applications and provide a platform upon which the interface model is to be applied.

Wray, Richard B.↗
NASA NTRS1999

Methods and Apparatus for Reducing Multipath Signal Error Using Deconvolution

A deconvolution approach to adaptive signal processing has been applied to the elimination of signal multipath errors as embodied in one preferred embodiment in a global positioning system receiver. The method and receiver of the present invention estimates then compensates for multipath effects in a comprehensive manner. Application of deconvolution, along with other adaptive identification and estimation techniques, results in completely novel GPS (Global Positioning System) receiver architecture.

Kumar, Rajendra↗
NASA NTRS2015

Robotic Reconnaissance Missions to Small Bodies and Their Potential Contributions to Human Exploration

Introduction: Robotic reconnaissance missions to small bodies will directly address aspects of NASA's Asteroid Initiative and will contribute to future human exploration. The NASA Asteroid Initiative is comprised of two major components: the Grand Challenge and the Asteroid Mission. The first component, the Grand Challenge, focuses on protecting Earth's population from asteroid impacts by detecting potentially hazardous objects with enough warning time to either prevent them from impacting the planet, or to implement civil defense procedures. The Asteroid Mission involves sending astronauts to study and sample a near- Earth asteroid (NEA) prior to conducting exploration missions of the Martian system, which includes Phobos and Deimos. The science and technical data obtained from robotic precursor missions that investigate the surface and interior physical characteristics of an object will help identify the pertinent physical properties that will maximize operational efficiency and reduce mission risk for both robotic assets and crew operating in close proximity to, or at the surface of, a small body. These data will help fill crucial strategic knowledge gaps (SKGs) concerning asteroid physical characteristics that are relevant for human exploration considerations at similar small body destinations. Small Body Strategic Knowledge Gaps: For the past several years NASA has been interested in identifying the key SKGs related to future human destinations. These SKGs highlight the various unknowns and/or data gaps of targets that the science and engineering communities would like to have filled in prior to committing crews to explore the Solar System. An action team from the Small Bodies Assessment Group (SBAG) was formed specifically to identify the small body SKGs under the direction of the Human Exploration and Operations Missions Directorate (HEOMD), given NASA's recent interest in NEAs and the Martian moons as potential human destinations [1]. The action team organized the SKGs into four broad themes: 1) Identify human mission targets; 2) Understand how to work on and interact with the small body surface; 3) Understand the small body environment and its potential risk/benefit to crew, systems, and operational assets; and 4) Understand the small body resource potential. Each of these themes were then further subdivided into categories to address specific SKG issues. Robotic Precursor Contributions to SKGs: Robotic reconnaissance missions should be able to address specific aspects related to SKG themes 1 through 4. Theme 1 deals with the identification of human mission targets within the NEA population. The current guideline indicates that human missions to fastspinning, tumbling, or binary asteroids may be too risky to conduct successfully from an operational perspective. However, no spacecraft mission has been to any of these types of NEAs before. Theme 2 addresses the concerns about interacting on the small body surface under microgravity conditions, and how the surface and/or sub-surface properties affect or restrict the interaction for human exploration. The combination of remote sensing instruments and in situ payloads will provide good insight into the asteroid's surface and subsurface properties. SKG theme 3 deals with the environment in and around the small body that may present a nuisance or hazard to any assets operating in close proximity. Impact and surface experiments will help address issues related to particle size, particle longevity, internal structure, and the near-surface mechanical stability of the asteroid. Understanding or constraining these physical characteristics are important for mission planning. Theme 4 addresses the resource potential of the small body. This is a particularly important aspect of human exploration since the identification and utilization of resources is a key aspect for deep space mission architectures to the Martian system (i.e., Phobos and Deimos). Conclusions: Robotic reconnaissance of small bodies can provide a wealth of information relevant to the science and planetary defense of NEAs. However, such missions to investigate NEAs can also provide key insights into small body strategic knowledge gaps and contribute to the overall success for human exploration missions to asteroids.

Abell, P. A.↗
NASA NTRS2020

Pattern Identification - A Foundation for Research in the Emphasis of Design Patterns in Systems Engineering and Knowledge Capture

Pattern Language describes the morphology and functionality of a system in the absence of design particulars. Harnessing this capability will provide the Systems Engineering discipline a means of managing the development of increasingly complex systems with increasingly distributed design teams while capturing and retaining knowledge for future generations. Pattern Language is a syntax for describing, and structurally relating, design patterns. Design patterns contextually describe the application of domain knowledge in the engineered solution to the force balance problem. The parallels between pattern recognition and application, as a fundamental stage of human learning, and pattern observation within a complex system, suggests pattern language may be a valuable tool in the capture and dissemination of knowledge. Pattern application has enjoyed considerable study over the last several decades, however much of this work has focused on the replication of design particulars. This work returns to the roots of Pattern Language and explores the utility of patterns as an architectural description and guide, and knowledge capture method, for complex system development beginning with the identification of a time proven design pattern.

Russell, Samuel P.↗
NASA NTRS1990

Cryogenic propellant management architectures to support the Space Exploration Initiative

The initial results of a current study to develop fuel system architectures to support the lunar requirements of the Space Exploration Initiative (SEI) are reported. The study includes the development and assessment of propellant management facility concepts, supporting infrastructure, operations analysis, and identification of impact on current programs, including Space Station Freedom, Earth-to-Orbit vehicles, and the Space Transfer Vehicle. The cryogenic propellant management architectures are evaluated using criteria that have been defined to provide for minimum subjective assessment and effective data reliability.

Cady, E. C.↗
NASA NTRS1984

SETI science working group report

This report covers the initial activities and deliberations of a continuing working group asked to assist the SETI Program Office at NASA. Seven chapters present the group's consensus on objectives, strategies, and plans for instrumental R&D and for a microwave search for extraterrestrial in intelligence (SETI) projected for the end of this decade. Thirteen appendixes reflect the views of their individual authors. Included are discussions of the 8-million-channel spectrum analyzer architecture and the proof-of-concept device under development; signal detection, recognition, and identification on-line in the presence of noise and radio interference; the 1-10 GHz sky survey and the 1-3 GHz targeted search envisaged; and the mutual interests of SETI and radio astronomy. The report ends with a selective, annotated SETI reading list of pro and contra SETI publications.

Drake, F.↗
NASA NTRS1993

Nuclear propulsion control and health monitoring

An integrated control and health monitoring architecture is being developed for the Pratt & Whitney XNR2000 nuclear rocket. Current work includes further development of the dynamic simulation modeling and the identification and configuration of low level controllers to give desirable performance for the various operating modes and faulted conditions. Artificial intelligence and knowledge processing technologies need to be investigated and applied in the development of an intelligent supervisory controller module for this control architecture.

Walter, P. B.↗
NASA NTRS1990

Rocket engine diagnostics using neural networks

Two problems in applying neural networks to fault detection and identification are (1) the complexity of the sensor data to fault mapping and (2) the lack of sufficient training data. Here, methods are derived and tested in an architecture which addresses these two problems. First, the sensor data to fault mapping is decomposed into three simpler mappings which perform sensor data compression, hypothesis generation, and sensor fusion. Efficient training is performed for each mapping separately. Second, the neural network which performs sensor fusion is structured to detect new unknown faults for which training examples were not presented. These methods were tested on a task of fault detection and identification in the Space Shuttle Main Engine (SSME). Results indicate that the decomposed neural network architecture can be trained efficiently, can identify faults for which it has been trained, and can detect the occurrence of faults for which it has not been trained.

Whitehead, Bruce A.↗
NASA NTRS1990

Pattern recognition of clouds and ice in polar regions

The study is based on AVHRR imagery and results from Landsat high-spatial-resolution scenes. Among the textual features investigated are the gray level difference vector (GLDV), and sum and difference histogram (SADH) approaches as well as gray level run length, spatial-coherence, and spectral-histogram measures. The traditional stepwise discriminant analysis and neural-network analysis are used for the identification of 20 Arctic surface and cloud classes. A principal-component analysis and hybrid architecture employing a modularized competitive learning layer are utilized. It is pointed out that the cloud-classification accuracy comparable to that of back-propagation could be achieved with a training time two orders of magnitude faster.

Welch, R. M.↗
NASA NTRS1994

Toward Real Time Neural Net Flight Controllers

NASA Ames Research Center has an ongoing program in neural network control technology targeted toward real time flight demonstrations using a modified F-15 which permits direct inner loop control of actuators, rapid switching between alternative control designs, and substitutable processors. An important part of this program is the ACTIVE flight project which is examining the feasibility of using neural networks in the design, control, and system identification of new aircraft prototypes. This paper discusses two research applications initiated with this objective in mind: utilization of neural networks for wind tunnel aircraft model identification and rapid learning algorithms for on line reconfiguration and control. The first application involves the identification of aerodynamic flight characteristics from analysis of wind tunnel test data. This identification is important in the early stages of aircraft design because complete specification of control architecture's may not be possible even though concept models at varying scales are available for aerodynamic wind tunnel testing. Testing of this type is often a long and expensive process involving measurement of aircraft lift, drag, and moment of inertia at varying angles of attack and control surface configurations. This information in turn can be used in the design of the flight control systems by applying the derived lookup tables to generate piece wise linearized controllers. Thus, reduced costs in tunnel test times and the rapid transfer of wind tunnel insights into prototype controllers becomes an important factor in more efficient generation and testing of new flight systems. NASA Ames Research Center is successfully applying modular neural networks as one way of anticipating small scale aircraft model performances prior to testing, thus reducing the number of in tunnel test hours and potentially, the number of intermediate scaled models required for estimation of surface flow effects.

Jorgensen, C. C.↗
NASA NTRSDate not supplied

Creating a Wastewater Technology ‘Toolbox’ for Space Exploration ECLSS

Life Support Systems (LSS) are essential for manned spaceflight; without them, humans would not survive. Upcoming long-duration missions demand robust environmental control LSS (ECLSS) due to their insolation and limited prospect for immediate resupply. As part of LSS, water purification systems will require high reliability, sustainability, and efficiency due to transport mass limitations, because routine water delivery will be very difficult and costly to resupply future habitats. This suggests a highly effective treatment method and reuse of every wastewater source. A variety of wastewater streams are generated by crew, and although not all are currently treated, habitat success will require each stream to be treated and utilized as a ‘resource’ rather than 'waste’. These wastewater streams include human wastewater (urine, feces), food waste (plate waste, inedible plant biomass), humidity condensate, hygiene water (shower, oral, handwash), and laundry. Proven technologies are often relied upon due to long-term operations. For future, longer-term missions, this paradigm must shift to include technologies based on meeting mission requirements rather than sacrificing productivity in lieu of proven existing technology capabilities. Many physical, chemical, and biological water-processing technologies are proven and established for terrestrial applications. Herein, these technologies were collected into a ‘toolbox’ to perform possible functions towards effective water purification steps in reduced gravity. Selection criteria are dependent on approach (physical, chemical, or biological), complexity/components, terrestrial performance, and potential applicability to space life support. Utilization of this ‘toolbox’ approach provides a streamline methodology for technology development and down-selection into future architecture in direct response to the dynamic space life support requirements. Establishing the ‘toolbox’ also provides organized and efficient identification of the most appropriate technologies. From there, the technologies with the largest potential to be configured for mission requirements can be further developed and appropriately assessed. This presentation seeks to provide a comprehensive review of space life support water purification requirements and challenges, as well as to present a ‘toolbox’ methodology of available technologies to aid in the difficult process of selecting appropriate LSS water purification for short and long-term NASA mission architectures.

Luke Roberson↗
NASA NTRS2009

Tele-Supervised Adaptive Ocean Sensor Fleet

The Tele-supervised Adaptive Ocean Sensor Fleet (TAOSF) is a multi-robot science exploration architecture and system that uses a group of robotic boats (the Ocean-Atmosphere Sensor Integration System, or OASIS) to enable in-situ study of ocean surface and subsurface characteristics and the dynamics of such ocean phenomena as coastal pollutants, oil spills, hurricanes, or harmful algal blooms (HABs). The OASIS boats are extended- deployment, autonomous ocean surface vehicles. The TAOSF architecture provides an integrated approach to multi-vehicle coordination and sliding human-vehicle autonomy. One feature of TAOSF is the adaptive re-planning of the activities of the OASIS vessels based on sensor input ( smart sensing) and sensorial coordination among multiple assets. The architecture also incorporates Web-based communications that permit control of the assets over long distances and the sharing of data with remote experts. Autonomous hazard and assistance detection allows the automatic identification of hazards that require human intervention to ensure the safety and integrity of the robotic vehicles, or of science data that require human interpretation and response. Also, the architecture is designed for science analysis of acquired data in order to perform an initial onboard assessment of the presence of specific science signatures of immediate interest. TAOSF integrates and extends five subsystems developed by the participating institutions: Emergent Space Tech - nol ogies, Wallops Flight Facility, NASA s Goddard Space Flight Center (GSFC), Carnegie Mellon University, and Jet Propulsion Laboratory (JPL). The OASIS Autonomous Surface Vehicle (ASV) system, which includes the vessels as well as the land-based control and communications infrastructure developed for them, controls the hardware of each platform (sensors, actuators, etc.), and also provides a low-level waypoint navigation capability. The Multi-Platform Simulation Environment from GSFC is a surrogate for the OASIS ASV system and allows for independent development and testing of higher-level software components. The Platform Communicator acts as a proxy for both actual and simulated platforms. It translates platform-independent messages from the higher control systems to the device-dependent communication protocols. This enables the higher-level control systems to interact identically with heterogeneous actual or simulated platforms.

Lefes, Alberto↗
NASA NTRS1990

Neuromorphic learning of continuous-valued mappings from noise-corrupted data. Application to real-time adaptive control

The ability of feed-forward neural network architectures to learn continuous valued mappings in the presence of noise was demonstrated in relation to parameter identification and real-time adaptive control applications. An error function was introduced to help optimize parameter values such as number of training iterations, observation time, sampling rate, and scaling of the control signal. The learning performance depended essentially on the degree of embodiment of the control law in the training data set and on the degree of uniformity of the probability distribution function of the data that are presented to the net during sequence. When a control law was corrupted by noise, the fluctuations of the training data biased the probability distribution function of the training data sequence. Only if the noise contamination is minimized and the degree of embodiment of the control law is maximized, can a neural net develop a good representation of the mapping and be used as a neurocontroller. A multilayer net was trained with back-error-propagation to control a cart-pole system for linear and nonlinear control laws in the presence of data processing noise and measurement noise. The neurocontroller exhibited noise-filtering properties and was found to operate more smoothly than the teacher in the presence of measurement noise.

Troudet, Terry↗
NASA NTRS2004

Selection of a Data Acquisition and Controls System Communications and Software Architecture for Johnson Space Center's Space Environment Simulation Laboratory Thermal and Vacuum Test Facilities

Upgrade of data acquisition and controls systems software at Johnson Space Center's Space Environment Simulation Laboratory (SESL) involved the definition, evaluation and selection of a system communication architecture and software components. A brief discussion of the background of the SESL and its data acquisition and controls systems provides a context for discussion of the requirements for each selection. Further framework is provided as upgrades to these systems accomplished in the 1990s and in 2003 are compared to demonstrate the role that technological advances have had in their improvement. Both of the selections were similar in their three phases; 1) definition of requirements, 2) identification of candidate products and their evaluation and testing and 3) selection by comparison of requirement fulfillment. The candidates for the communication architecture selection embraced several different methodologies which are explained and contrasted. Requirements for this selection are presented and the selection process is described. Several candidates for the software component of the data acquisition and controls system are identified, requirements for evaluation and selection are presented, and the evaluation process is described.

Jordan, Eric A.↗
NASA NTRS2016

Considerations for the Next Revision of NASA's Space Telecommunications Radio System Architecture

Development of NASA's Software Defined Radio architecture, the Space Telecommunication Radio System (STRS), was initiated in 2004 with a goal of reducing the cost, risk and schedule when implementing Software Defined Radios (SDR) for National Aeronautics and Space Administration (NASA) space missions. Since STRS was first flown in 2012 on three Software Defined Radios on the Space Communication and Navigation (SCaN) Testbed, only minor changes have been made to the architecture. Multiple entities have since implemented the architecture and provided significant feedback for consideration for the next revision of the standard. The focus for the first set of updates to the architecture is items that enhance application portability. Items that require modifications to existing applications before migrating to the updated architecture will only be considered if there is compelling reasons to make the change. The significant suggestions that were further evaluated for consideration include expanding and clarifying the timing Application Programming Interfaces (APIs), improving handle name and identification (ID) definitions and use, and multiple items related to implementation of STRS Devices. In addition to ideas suggested while implementing STRS, SDR technology has evolved significantly and this impact to the architecture needs to be considered. These include incorporating cognitive concepts - learning from past decisions and making new decisions that the radio can act upon. SDRs are also being developed that do not contain a General Purpose Module - which is currently required for the platform to be STRS compliant. The purpose of this paper is to discuss the comments received, provide a summary of the evaluation considerations, and examine planned dispositions.

transmitters receivers↗
NASA NTRS2016

Considerations for the Next Revision of STRS

Development of NASAs Software Defined Radio architecture, the Space Telecommunication Radio System (STRS), was initiated in 2004 with a goal of reducing the cost, risk and schedule when implementing Software Defined Radios (SDR) for NASA space missions. Since STRS was first flown in 2012 on three Software Defined Radios on the Space Communication and Navigation (SCaN) Testbed, only minor changes have been made to the architecture. Multiple entities have since implemented the architecture and have provided significant feedback for consideration for the next revision of the standard. The focus for the first set of updates to the architecture is items that enhance application portability. Items that require modifications to existing applications before migrating to the updated architecture will only be considered if there is compelling reasons to make the change. The significant suggestions that were further evaluated for consideration include expanding and clarifying the timing Application Programming Interfaces (APIs), improving handle name and identification (ID) definitions and use, and multiple items related to implementation of STRS Devices. In addition to ideas suggested while implementing STRS, SDR technology has evolved significantly and this impact to the architecture needs to be considered. These include incorporating cognitive concepts - learning from past decisions and making new decisions that the radio can act upon. SDRs are also being developed that do not contain a General Purpose Module which is currently required for the platform to be STRS compliant. The purpose of this paper is to discuss the comments received, provide a summary of the evaluation considerations, and examine planned dispositions

waveforms↗
NASA NTRS1989

Neuromorphic learning of continuous-valued mappings in the presence of noise: Application to real-time adaptive control

The ability of feed-forward neural net architectures to learn continuous-valued mappings in the presence of noise is demonstrated in relation to parameter identification and real-time adaptive control applications. Factors and parameters influencing the learning performance of such nets in the presence of noise are identified. Their effects are discussed through a computer simulation of the Back-Error-Propagation algorithm by taking the example of the cart-pole system controlled by a nonlinear control law. Adequate sampling of the state space is found to be essential for canceling the effect of the statistical fluctuations and allowing learning to take place.

Troudet, Terry↗
NASA NTRS2009

Development of a Relay Performance Web Tool for the Mars Network

Modern Mars surface missions rely upon orbiting spacecraft to relay communications to and from Earth systems. An important component of this multi-mission relay process is the collection of relay performance statistics supporting strategic trend analysis and tactical anomaly identification and tracking.

data accountability↗