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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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Iron/air batteries recharged chemically by solution of strong base in alcohol or by basic alcohol solution of reducing agent. Although method still experimental, it has potential for batteries in electric automobiles or as energy system in remote applications. Also used in quiet operations where noise or infrared signature of diesel engine is not desired.
A status review is presented for a generic blackboard-based distributed problem-solving environment in which multiple-agent cooperation can be effected. This environment is organized into a shared information panel, a chairman control panel, and a metaplanning panel. Each panel contains a number of embedded AI techniques that facilitate its operation and that provide heuristics for solving the underlying team-agent decision problem. The status of these panels and heuristics is described along with a number of robustness considerations. The techniques for each of the three panels and for four sets of paradigm-related advances are described, along with selected results from classroom teaching experiments and from three applications.
A biologically-enhanced, physical/chemical terminal water treatment testbed for the Space Station Freedom is proposed. Recycled water requirements for human, animal, plant and/or combined crews for long duration space missions are discussed. An effective terminal treatment method for recycled water reclamation systems that is based on using granular activated carbon as the principal active agent and the controls of microbial contamination and growth within recycled water systems are examined. The roles of plants in water recycling within CELSS is studied.
Surface acoustic wave (SAW) piezoelectric devices are finding widespread applications in many arenas, particularly in the area of chemical sensing. We have developed an oxygen pressure sensor based on coating a SAW device with an oxygen binding agent which can be tailored to provide variable sensitivity. The coating is prepared by dissolving an oxygen binding agent in a toluene solution of a copolymer which is then sprayed onto the surface of the SAW device. Experimental data shows the feasibility of tailoring sensors to measure the partial pressure of oxygen from 2.6 to 67 KPa (20 to 500 torr). Potential applications of this technology are discussed.
Vapor transport of ZnO by PVT and CVT using carbon, carbon monoxide, and hydrogen as the transport agents was studied. Theoretical calculations of the mass flux were based on equilibrium thermodynamics and 1-D diffusional mass transport. Experimental results were found to be consistent with theoretical predictions. NO apparent kinetic limitations to sublimation were observed. Slow reaction of carbon with ZnO source was found.
In compliance with the Committee on Space Research (COSPAR) planetary protection policy, National Aeronautics and Space Administration (NASA) monitors the total microbial burden of spacecraft as a means for minimizing the inadvertent transfer of viable contaminant microorganisms to extraterrestrial environments (forward contamination). NASA standard assay-based counts are used both as a proxy for relative surface cleanliness and to estimate overall microbial burden as well as to assess whether forward planetary protection risk criteria are met for a given mission, which vary by the planetary body to be explored and whether or not life detection missions are present. Despite efforts to reduce presence of microorganisms from spacecraft prior to launch, microbes have been isolated from spacecraft and associated surfaces within the extreme conditions of clean room facilities using state of the art molecular technologies. Development of a more sensitive method that will better enumerate all viable microorganisms from spacecraft and associated surfaces could support future life detection missions. Current culture-based (NASA standard spore assay) and nucleic-acid-based polymerase chain reaction (PCR) methods have significant shortcomings in this type of analysis. The overall goal of this project is to evaluate and validate a new molecular method based on the use of a deoxyribonucleic acid (DNA) intercalating agent propidium monoazide (PMA). This is used in combination with DNA microarray (PhyloChip) which has been shown to identify very low levels of organisms on spacecraft associated surfaces. PMA can only penetrate the membrane of dead cells. Once penetrated, it intercalates the DNA and, upon photolysis using visible light it produces stable DNA monoadducts. This allows DNA to be unavailable for further PCR analysis. The specific aim of this study is to standardize the spore inactivation method for PMA-PhyloChip analysis. We have used the bacterial spores Bacillus subtilis 168 (standard laboratory isolate) as a test organism.
The on-going transformation from the current US Air Traffic System (ATS) to the Next Generation Air Traffic System (NextGen) will force the introduction of new automated systems and most likely will cause automation to migrate from ground to air. This will yield new function allocations between humans and automation and therefore change the roles and responsibilities in the ATS. Yet, safety in NextGen is required to be at least as good as in the current system. We therefore need techniques to evaluate the safety of the interactions between humans and automation. We think that current human factor studies and simulation-based techniques will fall short in front of the ATS complexity, and that we need to add more automated techniques to simulations, such as model checking, which offers exhaustive coverage of the non-deterministic behaviors in nominal and off-nominal scenarios. In this work, we present a verification approach based both on simulations and on model checking for evaluating the roles and responsibilities of humans and automation. Models are created using Brahms (a multi-agent framework) and we show that the traditional Brahms simulations can be integrated with automated exploration techniques based on model checking, thus offering a complete exploration of the behavioral space of the scenario. Our formal analysis supports the notion of beliefs and probabilities to reason about human behavior. We demonstrate the technique with the Ueberligen accident since it exemplifies authority problems when receiving conflicting advices from human and automated systems.
Research and development of cost saving technologies is vital to the success of NASA’s strategic goal to extend human presence deeper into space and to the moon for sustainable long-term exploration and utilization. Reduction of mass has long been a method of reducing space mission costs. In this submission, Inertial Transfer, a new approach to mass transfer in space, is presented. A Multi-Agent System solution to the problem space is discussed. A scaled base-line simulator is constructed and demonstrated which will enable the development of AI capabilities necessary to perform Inertial Transfer. Finally, future work and key challenges are discussed.
Carbon dioxide (CO2) is well known as the primary forcing agent of global warming. Although the climate forcing due to CO2 is well known, the sources and sinks of CO2 are not well understood. Currently the lack of global atmospheric CO2 observations limits our ability to diagnose the global carbon budget (e.g., finding the so-called "missing sink") and thus limits our ability to understand past climate change and predict future climate response. Space-based techniques are being developed to make high-resolution and high-precision global column CO2 measurements. One of the proposed techniques utilizes the passive remote sensing of Earth's reflected solar radiation at the weaker vibration-rotation band of CO2 in the near infrared (approx. 1.57 micron). We use a line-by-line radiative transfer model to explore the potential of this method. Results of sensitivity studies for CO2 concentration variation and geophysical conditions (i.e., atmospheric temperature, surface reflectivity, solar zenith angle, aerosol, and cirrus cloud) will be presented. We will also present sensitivity results for an O2 A-band (approx. 0.76 micron) sensor that will be needed along with CO2 to make surface pressure and cloud height measurements.
We present an approach to the problem of verification of epistemic properties in multi-agent systems by means of symbolic model checking. In particular, it is shown how to extend the technique of unbounded model checking from a purely temporal setting to a temporal-epistemic one. In order to achieve this, we base our discussion on interpreted systems semantics, a popular semantics used in multi-agent systems literature. We give details of the technique and show how it can be applied to the well known train, gate and controller problem. Keywords: model checking, unbounded model checking, multi-agent systems
The AI Bus architecture is layered, distributed object oriented framework developed to support the requirements of advanced technology programs for an order of magnitude improvement in software costs. The consequent need for highly autonomous computer systems, adaptable to new technology advances over a long lifespan, led to the design of an open architecture and toolbox for building large scale, robust, production quality systems. The AI Bus accommodates a mix of knowledge based and conventional components, running on heterogeneous, distributed real world and testbed environment. The concepts and design is described of the AI Bus architecture and its current implementation status as a Unix C++ library or reusable objects. Each high level semiautonomous agent process consists of a number of knowledge sources together with interagent communication mechanisms based on shared blackboards and message passing acquaintances. Standard interfaces and protocols are followed for combining and validating subsystems. Dynamic probes or demons provide an event driven means for providing active objects with shared access to resources, and each other, while not violating their security.
Managers of systems of shared resources typically have many separate goals. Examples are efficient utilization of the resources among its users and ensuring no user's satisfaction in the system falls below a preset minimal level. Since such goals will usually conflict with one another, either implicitly or explicitly the manager must determine the relative importance of the goals, encapsulating that into an overall utility function rating the possible behaviors of the entire system. Here we demonstrate a distributed, robust, and adaptive way to optimize that overall function. Our approach is to interpose adaptive agents between each user and the system, where each such agent is working to maximize its own private utility function. In turn, each such agent's function should be both relatively easy for the agent to learn to optimize, and 'aligned' with the overall utility function of the system manager - an overall function that is based on but in general different from the satisfaction functions of the individual users. To ensure this we enhance the COllective INtelligence (COIN) framework to incorporate user satisfaction functions in the overall utility function of the system manager and accordingly in the associated private utility functions assigned to the users agents. We present experimental evaluations of different COIN-based private utility functions and demonstrate that those COIN-based functions outperform some natural alternatives.
Managers of systems of shared resources typically have many separate goals. Examples are efficient utilization of the resources among its users and ensuring no user s satisfaction in the system falls below a preset minimal level. Since such goals will usually conflict with one another, either implicitly or explicitly the manager must determine the relative importance of the goals, encapsulating that into an overall utility function rating the possible behaviors of the entire system. Here we demonstrate a distributed, robust, and adaptive way to optimize that overall function. Our approach is to interpose adaptive agents between each user and the system, where each such agent is working to maximize its own private utility function. In turn, each such agent's function should be both relatively easy for the agent to learn to optimize, and "aligned" with the overall utility function of the system manager - an overall function that is based on but in general different from the satisfaction functions of the individual users. To ensure this we enhance the Collective INtelligence (COIN) framework to incorporate user satisfaction functions in the overall utility function of the system manager and accordingly in the associated private utility functions assigned to the users agents. We present experimental evaluations of different COIN-based private utility functions and demonstrate that those COIN-based functions outperform some natural alternatives.
The objective is to simulate and optimize distributed cooperation among a network of robots tasked with cooperative excavation on an extra-terrestrial surface. Additionally to examine the concept of directed Emergence among a group of limited artificially intelligent agents. Emergence is the concept of achieving complex results from very simple rules or interactions. For example, in a termite mound each individual termite does not carry a blueprint of how to make their home in a global sense, but their interactions based strictly on local desires create a complex superstructure. Leveraging this Emergence concept applied to a simulation of cooperative agents (robots) will allow an examination of the success of non-directed group strategy achieving specific results. Specifically the simulation will be a testbed to evaluate population based robotic exploration and cooperative strategies while leveraging the evolutionary teamwork approach in the face of uncertainty about the environment and partial loss of sensors. Checking against a cost function and 'social' constraints will optimize cooperation when excavating a simulated tunnel. Agents will act locally with non-local results. The rules by which the simulated robots interact will be optimized to the simplest possible for the desired result, leveraging Emergence. Sensor malfunction and line of sight issues will be incorporated into the simulation. This approach falls under Swarm Robotics, a subset of robot control concerned with finding ways to control large groups of robots. Swarm Robotics often contains biologically inspired approaches, research comes from social insect observation but also data from among groups of herding, schooling, and flocking animals. Biomimetic algorithms applied to manned space exploration is the method under consideration for further study.
We are at the apex of an aviation revolution where autonomy will play a central role in enabling complex, multi-agent systems to communicate, interact, and collaborate on a myriad of applications spanning autonomous swarms to wild-fire management. Autonomy is not an absolute but rather a spectrum ranging from a system requiring significant human intervention to one requiring little to none [1]. For example, the extreme, in the case of an autonomous aircraft, is one that operates independently in the airspace interacting with all other elements (air traffic controllers, other pilots) as if it were a human pilot. Critical to this vision is an architecture that enables autonomous agents to interact with minimal latency. Edge computing is an emerging architecture where compute and storage is pushed to the ‘edge’ of the network in order to minimize the round-trip time from agent to resource thereby mitigating the latency associated with cloud-only based approaches. Additionally, services can generate massive amounts of data (e.g., video feeds), which may require analysis in near real-time. Moving this data to the cloud for further processing may not be feasible due to latency, bandwidth, and cost. Privacy, security, and reliability can also be improved by edge computing architectures. However, this geo-distributed and dynamic* architecture complicates the establishment of unambiguous network security boundaries and can lead to vulnerabilities including man in the middle attacks, replay attacks, physical security breaches of edge nodes, signal interception, etc. This motivates the need for zero-trust architectures [2–4] which de-emphasize the notion of static network perimeters and, as the name implies, do not instill any innate trust in any particular agent. It is required that all agents must be authorized and approved in every transaction. In this paper, we present a zero-trust architecture suitable for edge-computing applications that demand significant low-latency, security, privacy, and reliability.
The present conference on airborne and spaceborne remote sensing laser applications discusses topics in atmospheric and geophysical sciences-related sensors, lidar and DIAL component and subsystem technologies, and coherent laser experiments and semiconductor laser technologies. Attention is given to airborne lidar measurement of aerosols, a ground-based injection-locked pulsed TEA laser for wind measurements, chemical/biological agent standoff detection methods, lidars for wind shear erosion, laser tuning to selected gas absorption lines in the atmosphere, the NASA lidar-in-space technology experiment, and the Laser Atmospheric Wind Sounder.
Future applications of artificial intelligence to Mission Control Centers are presented in the form of the viewgraphs. The following subject areas are covered: basic objectives of the NASA-wide AI program; inhouse research program; constraint-based scheduling; learning and performance improvement for scheduling; GEMPLAN multi-agent planner; planning, scheduling, and control; Bayesian learning; efficient learning algorithms; ICARUS (an integrated architecture for learning); design knowledge acquisition and retention; computer-integrated documentation; and some speculation on future applications.
The process of the present invention includes first treating a polyamide acid (such as LARC-TPI polyamide acid) in an amide-containing solvent (such as N-methyl pyrrolidone) with an aprotic organic base (such as triethylamine), followed by dehydrating with an organic dehydrating agent (such as acetic anhydride). The level of crystallinity in the linear aromatic polyimide so produced is maximized without any degradation in the molecular weight thereof.