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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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This paper describes dynamic trade-off evalation (DTE), a new technique that has been developed to improve the performance of real-time problem solving systems. The DTE technique is most suitable for environments in which the requirement for meeting time constraints is of equal or greater importance to that of providing optimally intelligent solutions.
The ground processing and launch of Shuttle vehicles and their payloads is the primary task of Kennedy Space Center. It is a process which is largely manual and contains little inherent automation. Business is conducted today much as it was during previous NASA programs such as Apollo. In light of new programs and decreasing budgets, NASA must find more cost effective ways in which to do business while retaining the quality and safety of activities. Advanced technologies including artificial intelligence could cut manpower and processing time. This paper is an overview of the research and development in Al technology at KSC with descriptions of the systems which have been implemented, as well as a few under development which are promising additions to ground processing software. Projects discussed cover many facets of ground processing activities, including computer sustaining engineering, subsystem monitor and diagnosis tools and launch team assistants. The deployed Al applications have proven an effectiveness which has helped to demonstrate the benefits of utilizing intelligent software in the ground processing task.
Recent advances in nonlinear dynamics demonstrate a remarkable complexity of patterns outside of equilibrium, which are derived from simple basic laws of physics.
In this Phase II proposal, Applied Research LLC (ARLLC), Thomas Jefferson National Accelerator Facility (Jefferson Lab), and Old Dominion University (ODU) propose the combination of domain knowledge (beam characteristics, fixed structural shielding, earthen burden (the soil and foliage added to the dome of the experimental halls as additional shielding), etc.), machine learning (ML) and/or artificial intelligence (AI) to correlate a variety of multi-modal onsite signals and the radiation fields seen in accessible areas of the accelerator site and the site boundary. The ML/AI will consider the complex influence of environmental parameters affecting the radon contribution of the measurements, focusing on actual data obtained from Jefferson Lab. In Phase I, the coded beam and location data were fed into a deep learning model to predict doses at several designated locations in Jefferson Lab’s facility. Moreover, a dense radiation map was generated using only a sparse collection of the samples in a facility. In Phase II, we will develop a software prototype containing a radiation prediction algorithm, dense radiation map algorithms, and background noise prediction algorithms, with actual data used to evaluate the prototype. This work will provide a framework for evaluation of radiation measurement results around the site based on learned responses. In addition, the proposed approach allows more granular mapping of radiation levels. Better understanding and communication of these levels is related to the overall approach in keeping doses to personnel ALARA.
VISIONARY is an AI system that accelerates energy materials discovery by automatically generating hypotheses about structure-property relationships. It analyzes patterns in materials data, identifies promising correlations, and proposes testable scientific hypotheses without human intervention. By streamlining this reasoning process, VISIONARY helps researchers efficiently identify candidate materials with desired properties, significantly speeding up the materials development pipeline for energy applications. During the project, we developed a standalone application. The application uses a combination of papers provided by the user and data collected from FutureHouse’s dataset to build an understanding of the background that the user wants to explore for the hypothesis.
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Gas tungsten arc welding experiments for improving properties of 2219 aluminum by adaptive control of workpiece temperature near molten puddle
The field of telepresence is defined, and overviews of those capabilities that are now available, and those that will be required to support a NASA telepresence effort are provided. Investigation of NASA's plans and goals with regard to telepresence, extensive literature search for materials relating to relevant technologies, a description of these technologies and their state of the art, and projections for advances in these technologies are included. Several space projects are examined in detail to determine what capabilities are required of a telepresence system in order to accomplish various tasks, such as servicing and assembly. The key operational and technological areas are identified, conclusions and recommendations are made for further research, and an example developmental program leading to an operational telepresence servicer is presented.
The field of telepresence is defined, and overviews of those capabilities that are now available, and those that will be required to support a NASA telepresence effort are provided. Investigation of NASA's plans and goals with regard to telepresence, extensive literature search for materials relating to relevant technologies, a description of these technologies and their state of the art, and projections for advances in these technologies over the next decade are included. Several space projects are examined in detail to determine what capabilities are required of a telepresence system in order to accomplish various tasks, such as servicing and assembly. The key operational and technological areas are identified, conclusions and recommendations are made for further research, and an example developmental program is presented, leading to an operational telepresence servicer.
The field of telepresence is defined and overviews of those capabilities that are now available, and those that will be required to support a NASA telepresence effort are provided. Investigation of NASA' plans and goals with regard to telepresence, extensive literature search for materials relating to relevant technologies, a description of these technologies and their state of the art, and projections for advances in these technologies over the next decade are included.
Techniques for classifying objects into groups or clases go under many different names including, most commonly, cluster analysis. Mathematically, the general problem is to find a best mapping of objects into an index set consisting of class identifiers. When an a priori grouping of objects exists, the process of deriving the classification rules from samples of classified objects is known as discrimination. When such rules are applied to objects of unknown class, the process is denoted classification. The specific problem addressed involves the group classification of a set of objects that are each associated with a series of measurements (ratio, interval, ordinal, or nominal levels of measurement). Each measurement produces one variable in a multidimensional variable space. Cluster analysis techniques are reviewed and methods for incuding geographic location, distance measures, and spatial pattern (distribution) as parameters in clustering are examined. For the case of patterning, measures of spatial autocorrelation are discussed in terms of the kind of data (nominal, ordinal, or interval scaled) to which they may be applied.
NASA's Office of Aeronautics and Space Technology (OAST) has instituted a 'Systems Autonomy' technology development program aimed at the creation of increasingly sophisticated AI systems. Expert system production will in due course be followed by fully autonomous AI systems capable of the complex mission objectives of the Space Station, as well as of lunar and Martian exploration. AI systems must demonstrate the degree of their autonomy in a real-time operational environment, and then undergo mating to robotics technologies. OAST's Systems Autonomy Program and Robotics Program are discussed with a view to anticipated difficulties in their integration.
Significant research products which have emerged from the core program of NASA's Office of Aeronautics and Space Technology (OAST) are discussed. The Space Station Thermal Control System, the Space Shuttle Integrated Communications Officer Station, the Launch Processing System, the Expert Scheduling System for Pioneer Venus Spacecraft, a Bayesian classification system, and a spaceborne multiprocessor system are included. The technology trends which led to these results are discussed and future developments in technology are forecasted.
The concepts and preliminary results for the OASIS testbed which will permit an evolutionary growth with minimum impact to the existing software and hardware environment are presented. Space Station Freedom's data management system is utilized as the application focus for the evaluation of the OASIS concepts. It is concluded that guidelines for the validation and definition of standards for open architectures must be aggressively pursued with attention paid to the integration of these standards with the hardware platforms, graphics interface, and operating system software infrastructure.
In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.
An approach to rendering robotic systems as 'personlike' as possible to achieve needed capabilities is outlined. Human characteristics such as knowledge, motivation, know-how, performance, achievement and individual differences corresponding to propensities and abilities can be supplied, within limits, with computing software and hardware to robotic systems provided with sufficiently rich sensory configurations. Pushing these limits is the developmental path for more and more personlike robotic systems. The portions of the Person Concept that appear to be most directly relevant to this effort are described in the following topics: reality concepts (the state-of-affairs system and descriptive formats, behavior as intentional action, individual persons (person characteristics), social patterns of behavior (social practices), and boundary conditions (status maxims). Personlike robotic themes and considerations for a technical development plan are also discussed.
The Lyndon B. Johnson Space Center Automation and Robotics Division has developed a simulation tool for use in evaluating the feasibility and effectiveness of proposed candidate mission concepts, such as the First Lunar Outpost design reference missions. The tool helps in defining requirements for such missions at the beginning of the definition activities and can be applied iteratively thereafter on a short cycle basis as changes are made to the mission definition. The simulation is applicable to the amount and capabilities of automation and robotics; number and skills of crew; amount of engineering work, science, and in-space materials utilization to be accomplished; impact of provision for maintenance and repair; and flight schedules and manifests. The simulation accounts for supply and demand of resources to accomplish tasks and accounts for elapsed time in carrying out mission processes and tasks.
The primary motivation for this research emanates from providing a decision support system to the electric bus operators in the municipal and urban localities which will guide the operators to maintain an optimal compromise among the noise level, pollution level, fuel usage etc. This study is backed up by our previous studies on study of battery characteristics, permanent magnet DC motor studies and electric traction motor size studies completed in the first year. The operator of the Hybrid Electric Car must determine optimal power management schedule to meet a given load demand for different weather and road conditions. The decision support system for the bus operator comprises three sub-tasks viz. forecast of the electrical load for the route to be traversed divided into specified time periods (few minutes); deriving an optimal 'plan' or 'preschedule' based on the load forecast for the entire time-horizon (i.e., for all time periods) ahead of time; and finally employing corrective control action to monitor and modify the optimal plan in real-time. A fully connected artificial neural network (ANN) model is developed for forecasting the kW requirement for hybrid electric bus based on inputs like climatic conditions, passenger load, road inclination, etc. The ANN model is trained using back-propagation algorithm employing improved optimization techniques like projected Lagrangian technique. The pre-scheduler is based on a Goal-Programming (GP) optimization model with noise, pollution and fuel usage as the three objectives. GP has the capability of analyzing the trade-off among the conflicting objectives and arriving at the optimal activity levels, e.g., throttle settings. The corrective control action or the third sub-task is formulated as an optimal control model with inputs from the real-time data base as well as the GP model to minimize the error (or deviation) from the optimal plan. These three activities linked with the ANN forecaster proving the output to the GP model which in turn produces the pre-schedule of the optimal control model. Some preliminary results based on a hypothetical test case will be presented for the load forecasting module. The computer codes for the three modules will be made available fe adoption by bus operating agencies. Sample results will be provided using these models. The software will be a useful tool for supporting the control systems for the Electric Bus project of NASA.