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Solenoid-driven device tests the integrity of a microweld joint between an electronic component lead wire and a wire ribbon by applying tension stress to the joint. Variable measured force is provided when either destructive or nondestructive testing is performed.
The objective of the Energy Efficient Engine Component Development and Integration program is to develop, evaluate, and demonstrate the technology for achieving lower installed fuel consumption and lower operating costs in future commercial turbofan engines. Minimum goals have been set for a 12 percent reduction in thrust specific fuel consumption (TSFC), 5 percent reduction in direct operating cost (DOC), and 50 percent reduction in performance degradation for the Energy Efficient Engine (flight propulsion system) relative to the JT9D-7A reference engine. The Energy Efficienct Engine features a twin spool, direct drive, mixed flow exhaust configuration, utilizing an integrated engine nacelle structure. A short, stiff, high rotor and a single stage high pressure turbine are among the major enhancements in providing for both performance retention and major reductions in maintenance and direct operating costs. Improved clearance control in the high pressure compressor and turbines, and advanced single crystal materials in turbine blades and vanes are among the major features providing performance improvement. Highlights of work accomplished and programs modifications and deletions are presented.
A Direct Drive Gas-Cooled (DDG) reactor core simulator has been coupled to a Brayton Power Conversion Unit (BPCU) for integrated system testing at NASA Glenn Research Center (GRC) in Cleveland, Ohio. This is a closed-cycle system that incorporates an electrically heated reactor core module, turboalternator, recuperator, and gas cooler. Nuclear fuel elements in the gas-cooled reactor design are replaced with electric resistance heaters to simulate the heat from nuclear fuel in the corresponding fast spectrum nuclear reactor. The thermodynamic transient behavior of the integrated system was the focus of this test series. In order to better mimic the integrated response of the nuclear-fueled system, a simulated reactivity feedback control loop was implemented. Core power was controlled by a point kinetics model in which the reactivity feedback was based on core temperature measurements; the neutron generation time and the temperature feedback coefficient are provided as model inputs. These dynamic system response tests demonstrate the overall capability of a non-nuclear test facility in assessing system integration issues and characterizing integrated system response times and response characteristics.
A Direct Drive Gas-Cooled (DDG) reactor core simulator has been coupled to a Brayton Power Conversion Unit (BPCU) for integrated system testing at NASA Glenn Research Center (GRC) in Cleveland, OH. This is a closed-cycle system that incorporates an electrically heated reactor core module, turbo alternator, recuperator, and gas cooler. Nuclear fuel elements in the gas-cooled reactor design are replaced with electric resistance heaters to simulate the heat from nuclear fuel in the corresponding fast spectrum nuclear reactor. The thermodynamic transient behavior of the integrated system was the focus of this test series. In order to better mimic the integrated response of the nuclear-fueled system, a simulated reactivity feedback control loop was implemented. Core power was controlled by a point kinetics model in which the reactivity feedback was based on core temperature measurements; the neutron generation time and the temperature feedback coefficient are provided as model inputs. These dynamic system response tests demonstrate the overall capability of a non-nuclear test facility in assessing system integration issues and characterizing integrated system response times and response characteristics.
The technology and configurational requirements of an all new 1990's energy efficient turbofan engine having a twin spool arrangement with a directly coupled fan and low-pressure turbine, a mixed exhaust nacelle, and a high 38.6:1 overall pressure ratio were studied. Major advanced technology design features required to provide the overall benefits were a high pressure ratio compression system, a thermally actuated advanced clearance control system, lightweight shroudless fan blades, a low maintenance cost one-stage high pressure turbine, a short efficient mixer and structurally integrated engine and nacelle. A conceptual design analysis was followed by integration and performance analyses of geared and direct-drive fan engines with separate or mixed exhaust nacelles to refine previously designed engine cycles. Preliminary design and more detailed engine-aircraft integration analysis were then conducted on the more promising configurations. Engine and aircraft sizing, fuel burned, and airframe noise studies on projected 1990's domestic and international aircraft produced sufficient definition of configurational and advanced technology requirements to allow immediate initiation of component technology development.
Thermally-driven, nonmechanical compressor uses container filled with compressed praseodymium cerium oxide powder (PrCeOx) to provide high-pressure flow of oxygen gas for driving closed-cycle Joule-Thomson-expansion refrigeration unit. Integrated heat switch/oxide sorption compressor has no moving parts except check valves, which control flow of oxygen gas between compressor and closed-cycle Joule-Thomson refrigeration system. Oxygen expelled from sorbent at high pressure by evacuating heat-switch gap and turning on heater.
A study is made of the input impedance Z of a small strip-loop antenna with arbitrary orientation in a cold collisionless uniform multicomponent magnetoplasma. Assuming a uniform current distribution, an integral expression for Z is derived which is valid for arbitrary values of driving frequency, plasma composition and density, loop orientation angle, and static magnetic field strength. The integral expression is evaluated numerically for the VLF/ELF range in a plasma modeled upon the inner magnetosphere. Approximate closed-form expressions for Z are also developed. It is found that the loop VLF/ELF input reactance is essentially identical to its free space self inductance. Also the loop radiation resistance is found to be a strong function of the loop orientation angle for frequencies near the lower-hybrid-resonance frequency or below the proton gyrofrequency.
The Resource Prospector (RP) is an In-‐Situ Resource Utilization (ISRU) lunar rover mission under study by NASA. RP is planned to launch in 2020 to prospect for subsurface volatiles and to extract oxygen from lunar regolith. The mission will address several of NASA's "Strategic Knowledge Gaps" for lunar exploration. The mission will also address the Global Exploration Roadmap's strategic goal of using local resources for human exploration. The distribution of lunar subsurface volatiles drives the mission requirement for mobility. The spatial distribution is hypothesized to be governed by impact cratering with the top 0.5 m being patchy at scales of 100 m. The mixing time scale increases with depth (less frequent larger impacts). Consequently, increased mobility reduces the depth requirement for sampling. The target RP traverse will extend 1 km radially from the landing site to sample craters of varying sizes. Sampling craters with different ages will reveal possible volatile emplacement history. In 1 Ga, approximately 60-70 craters of 10 m diameter form per km2. Thus, the rover will need to sample at least ten of these craters, which may require a total traverse path length of 2-‐3 km. During 2014-2015, we developed an initial prototype rover for RP. The current design is a solar powered, four-wheeled vehicle, with hub motor drive, offset four wheel steering, and active suspension. Active suspension provides capabilities including changing vehicle ride height, traversing comparatively large obstacles, and controlling load on the wheels. All-wheel steering enables the vehicle to point arbitrarily while roving, e.g., to keep the solar array pointed at the sun while in motion. The offset steering combined with active suspension improves driving in soft soil. The rover's on-board software utilizes NASA's Core Flight Software, which is a reusable flight software environment. During 2015, we completed the initial rover software build, which provides low-level hardware interfaces, basic mobility control, waypoint driving, odometry, basic error checking, and camera services. Development of the prototype rover has enabled maturation of many of the subsystems to TRL 5. During the next year, we will conduct integrated testing of concepts of operation, navigation, and remote driving tools. In addition, we will perform environmental tests including radiation (avionics), thermal and thermal/vacuum (mechanisms), and gravity offload (mobility).
Proportional controller for motor speed provides full 0- to 100-percent linear control of durations of motor-driving pulses. Controller uses commercially available LM3900 integrated circuit, which requires only single supply voltage of 4 to 30 V. Control voltage provided by adjustable potentiometer or by external source of feedback information such as motor-speed sensing circuit.
The elastic-viscoplastic constitutive equations of Bodner-Partom were applied to modeling creep damage in a high temperature Ni-alloy, B1900 + Hf. Both tertiary creep in bulk materials and creep crack growth in flawed materials were considered. In the latter case, the energy rate line integral was used for characterizing the crack driving force, and the rate of crack extension was computed using a local damage formulation that assumed fracture was controlled by cavitation occurring within the crack-tip process zone. The results of this investigation were used to assess the evolution equation for isotropic damage utilized in the Bodner-Partom constitutive equations.
An architecture for integrating large numbers of data storage units (drives) to form a distributed mass storage system is proposed. The network of interconnected units consists of nodes and links. At each node there resides a controller board, a data storage unit and, possibly, a local/remote user-terminal. The links (twisted-pair wires, coax cables, or fiber-optic channels) provide the communications backbone of the network. There is no central controller for the system as a whole; all decisions regarding allocation of resources, routing of messages and data-blocks, creation and distribution of redundant data-blocks throughout the system (for protection against possible failures), frequency of backup operations, etc., are made locally at individual nodes. The system can handle as many user-terminals as there are nodes in the network. Various users compete for resources by sending their requests to the local controller-board and receiving allocations of time and storage space. In principle, each user can have access to the entire system, and all drives can be running in parallel to service the requests for one or more users. The system is expandable up to a maximum number of nodes, determined by the number of routing-buffers built into the controller boards. Additional drives, controller-boards, user-terminals, and links can be simply plugged into an existing system in order to expand its capacity.
One of the key areas of the National Advanced Robotics Centre's enabling technologies research program is that of the human system interface, phase 1 of which started in July 1989 and is currently addressing the potential of virtual environments to permit intuitive and natural interactions between a human operator and a remote robotic vehicle. The aim of the first 12 months of this program (to September, 1990) is to develop a virtual human-interface demonstrator for use later as a test bed for human factors experimentation. This presentation will describe the current state of development of the test bed, and will outline some human factors issues and problems for more general discussion. In brief, the virtual telepresence system for remote driving has been designed to take the following form. The human operator will be provided with a helmet-mounted stereo display assembly, facilities for speech recognition and synthesis (using the Marconi Macrospeak system), and a VPL DataGlove Model 2 unit. The vehicle to be used for the purposes of remote driving is a Cybermotion Navmaster K2A system, which will be equipped with a stereo camera and microphone pair, mounted on a motorized high-speed pan-and-tilt head incorporating a closed-loop laser ranging sensor for camera convergence control (currently under contractual development). It will be possible to relay information to and from the vehicle and sensory system via an umbilical or RF link. The aim is to develop an interactive audio-visual display system capable of presenting combined stereo TV pictures and virtual graphics windows, the latter featuring control representations appropriate for vehicle driving and interaction using a graphical 'hand,' slaved to the flex and tracking sensors of the DataGlove and an additional helmet-mounted Polhemus IsoTrack sensor. Developments planned for the virtual environment test bed include transfer of operator control between remote driving and remote manipulation, dexterous end effector integration, virtual force and tactile sensing (also the focus of a current ARRL contract, initially employing a 14-pneumatic bladder glove attachment), and sensor-driven world modeling for total virtual environment generation and operator-assistance in remote scene interrogation.
The Microwave Anisotropy Probe (MAP) mission is the second mission in NASA's Office of Space Science low-cost, Medium-class Explorers (MIDEX) program. The Explorers Program is designed to accomplish frequent, low cost, high quality space science investigations utilizing innovative, streamlined, efficient management, design and operations approaches. The MAP spacecraft will produce an accurate full-sky map of the cosmic microwave background temperature fluctuations with high sensitivity and angular resolution. The MAP spacecraft is planned for launch in early 2001, and will be staffed by only single-shift operations. During the rest of the time the spacecraft must be operated autonomously, with personnel available only on an on-call basis. Four (4) innovations will work cooperatively to enable a significant reduction in operations costs for the MAP spacecraft. First, the use of a common ground system for Spacecraft Integration and Test (I&T) as well as Operations. Second, the use of Finite State Modeling for intelligent autonomy. Third, the integration of a graphical planning engine to drive the autonomous systems without an intermediate manual step. And fourth, the ability for distributed operations via Web and pager access.
Miniature high-performance low-mass space avionics systems are desired for planned future outer planetary exploration missions (i.e. Europa Orbiter/Lander, Pluto-Kuiper Express). The spacecraft fuel and mass requirements enabling orbit insertion is the driving requirement. The Micro Navigator is an integrated autonomous Guidance, Navigation & Control (GN&C)micro-system that would provide the critical avionics function for navigation, pointing, and precision landing. The Micro Navigator hardware and software allow fusion of data from multiple sensors to provide a single integrated vehicle state vector necessary for six degrees of freedom GN&C. The benefits of this MicroNavigator include: 1) The Micro Navigator employs MEMS devices that promise orders of magnitude reductions in mass power and volume of inertial sensors (accelerometers and gyroscopes), celestial sensing devices (startracker, sun sensor), and computing element; 2) The highly integrated nature of the unit will reduce the cost of flight missions. a) The advanced miniaturization technologies employed by the Micro Navigator lend themselves to mass production, and therefore will reduce production cost of spacecraft. b) The integral approach simplifies interface issues associated with discrete components and reduces cost associated with integration and test of multiple components; and 3) The integration of sensors and processing elements into a single unit will allow the Micro Navigator to encapsulate attitude information and determination functions into a single object. This is particularly beneficial for object-oriented software architectures that are used in advanced spacecraft. Additional information is contained in the original extended abstract.
There is a significant interest in the Earth Science research and user remote sensing community to substantially increase the number of useful observations relative to the current frequency of collection. The obvious reason for such a push is to improve the temporal, spectral, and spatial coverage of the area(s) under investigation. However, there is little analysis available in terms of the benefits, costs and the optimal set of sensors needed to make the necessary observations. Classic observing system solutions may no longer be applicable because of their point design philosophy. Instead, a new intelligent data collection system paradigm employing both reactive and proactive measurement strategies with adaptability to the dynamics of the phenomena should be developed. This is a complex problem that should be carefully studied and balanced across various boundaries including: science, modeling, applications, and technology. Modeling plays a crucial role in making useful predictions about naturally occurring or human-induced phenomena In particular, modeling can serve to mitigate the potentially deleterious impacts a phenomenon may have on human life, property, and the economy. This is especially significant when one is interested in learning about the dynamics of, for example, the spread of forest fires, regional to large-scale air quality issues, the spread of the harmful invasive species, or the atmospheric transport of volcanic plumes and ash. This paper identifies and examines these challenging issues and presents architectural alternatives for an integrated sensor web to provide observing scenarios driving the requisite dynamic spatial, spectral, and temporal characteristics to address these key application areas. A special emphasis is placed on the observing systems and its operational aspects in serving the multiple users and stakeholders in providing societal benefits. We also address how such systems will take advantage of technological advancement in small spacecraft and emerging information technologies, and how sensor web options may be realized and made affordable. Specialized detector subsystems and precision flying techniques may still require substantial innovation, development time and cost: we have presented the considerations for these issues. Finally, data and information gathering and compression techniques are also briefly described.
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.
A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.