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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

System Design and Locomotion of Superball, an Untethered Tensegrity Robot

The Spherical Underactuated Planetary Exploration Robot ball (SUPERball) is an ongoing project within NASA Ames Research Center's Intelligent Robotics Group and the Dynamic Tensegrity Robotics Lab (DTRL). The current SUPERball is the first full prototype of this tensegrity robot platform, eventually destined for space exploration missions. This work, building on prior published discussions of individual components, presents the fully-constructed robot. Various design improvements are discussed, as well as testing results of the sensors and actuators that illustrate system performance. Basic low-level motor position controls are implemented and validated against sensor data, which show SUPERball to be uniquely suited for highly dynamic state trajectory tracking. Finally, SUPERball is shown in a simple example of locomotion. This implementation of a basic motion primitive shows SUPERball in untethered control.

Tensegrity↗
NASA NTRS2003

ASRC Aerospace Corporation Selects Dynamically Reconfigurable Anadigm(Registered Trademark) FPAA For Advanced Data Acquisition System

Anadigm(registered trademark) today announced that ASRC Aerospace Corporation has designed Anadigm's dynamically reconfigurable Field Programmable Analog Array (FPAA) technology into an advanced data acquisition system developed under contract for NASA. ASRC Aerospace designed in the Anadigm(registered trademark) FPAA to provide complex analog signal conditioning in its intelligent, self-calibrating, and self-healing advanced data acquisition system (ADAS). The ADAS has potential applications in industrial, manufacturing, and aerospace markets. This system offers highly reliable operation while reducing the need for user interaction. Anadigm(registered trademark)'s dynamically reconfigurable FPAAs can be reconfigured in-system by the designer or on the fly by a microprocessor. A single device can thus be programmed to implement multiple analog functions and/or to adapt on-the-fly to maintain precision operation despite system degradation and aging. In the case of the ASRC advanced data acquisition system, the FPAA helps ensure that the system will continue to operating at 100% functionality despite changes in the environment, component degradation, and/or component failures.

Mata, Carlos T.↗
NASA NTRS1997

Develop Advanced Nonlinear Signal Analysis Topographical Mapping System

During the development of the SSME, a hierarchy of advanced signal analysis techniques for mechanical signature analysis has been developed by NASA and AI Signal Research Inc. (ASRI) to improve the safety and reliability for Space Shuttle operations. These techniques can process and identify intelligent information hidden in a measured signal which is often unidentifiable using conventional signal analysis methods. Currently, due to the highly interactive processing requirements and the volume of dynamic data involved, detailed diagnostic analysis is being performed manually which requires immense man-hours with extensive human interface. To overcome this manual process, NASA implemented this program to develop an Advanced nonlinear signal Analysis Topographical Mapping System (ATMS) to provide automatic/unsupervised engine diagnostic capabilities. The ATMS will utilize a rule-based Clips expert system to supervise a hierarchy of diagnostic signature analysis techniques in the Advanced Signal Analysis Library (ASAL). ASAL will perform automatic signal processing, archiving, and anomaly detection/identification tasks in order to provide an intelligent and fully automated engine diagnostic capability. The ATMS has been successfully developed under this contract. In summary, the program objectives to design, develop, test and conduct performance evaluation for an automated engine diagnostic system have been successfully achieved. Software implementation of the entire ATMS system on MSFC's OISPS computer has been completed. The significance of the ATMS developed under this program is attributed to the fully automated coherence analysis capability for anomaly detection and identification which can greatly enhance the power and reliability of engine diagnostic evaluation. The results have demonstrated that ATMS can significantly save time and man-hours in performing engine test/flight data analysis and performance evaluation of large volumes of dynamic test data.

Jong, Jen-Yi↗
DOE OSTI2020

A Model-Free Voltage Control Approach to Mitigate Motor Stalling and FIDVR for Smart Grids

Electric power networks are large and highly nonlinear dynamical systems that present unique challenges to control design. Though there is a large number of dynamic models for power system stability and control, many models are only useful with right assumptions and wrong for other tasks. Moreover, the dynamic behavior of the grid is increasingly complex under the banner of smart grids. These lead to the difficulty of developing appropriate dynamic modeling, and thus an efficient control strategy. To avoid such modeling challenges, here we present a novel dynamic voltage control strategy based on a model-free control (MFC) approach, requiring no modeling procedure. In particular, it focuses on fault-induced delayed voltage recovery (FIDVR) events, which require complex and accurate dynamic load models to replicate such events. This work utilizes MFC as an online controller to achieve the desired voltage stability under the FIDVR event. The proposed MFC strategy allows simple implementation and low computational cost for efficient mitigation of FIDVR. For benchmarking, a reasonably accurate dynamic performance model is explored. Simulation results with the IEEE 57 bus test network demonstrate the enhanced dynamic voltage profile for load buses having induction motors with the support of reactive power resources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1994

DYNACLIPS (DYNAmic CLIPS): A dynamic knowledge exchange tool for intelligent agents

In a dynamic environment, intelligent agents must be responsive to unanticipated conditions. When such conditions occur, an intelligent agent may have to stop a previously planned and scheduled course of actions and replan, reschedule, start new activities and initiate a new problem solving process to successfully respond to the new conditions. Problems occur when an intelligent agent does not have enough knowledge to properly respond to the new situation. DYNACLIPS is an implementation of a framework for dynamic knowledge exchange among intelligent agents. Each intelligent agent is a CLIPS shell and runs a separate process under SunOS operating system. Intelligent agents can exchange facts, rules, and CLIPS commands at run time. Knowledge exchange among intelligent agents at run times does not effect execution of either sender and receiver intelligent agent. Intelligent agents can keep the knowledge temporarily or permanently. In other words, knowledge exchange among intelligent agents would allow for a form of learning to be accomplished.

Cengeloglu, Yilmaz↗
NASA NTRS2002

The Design of Collectives of Agents to Control Non-Markovian Systems

The 'Collective Intelligence' (COIN) framework concerns the design of collectives of reinforcement-learning agents such that their interaction causes a provided 'world' utility function concerning the entire collective to be maximized. Previously, we applied that framework to scenarios involving Markovian dynamics where no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. This approach sets the individual utility function of each agent to be both aligned with the world utility, and at the same time, easy for the associated agents to optimize. Here we extend that approach to systems involving non-Markovian dynamics. In computer simulations, we compare our techniques with each other and with conventional-'team games'. We show whereas in team games performance often degrades badly with time, it steadily improves when our techniques are used. We also investigate situations where the system's dimensionality is effectively reduced. We show that this leads to difficulties in the agents' ability to learn. The implication is that 'learning' is a property only of high-enough dimensional systems.

Lawson, John W.↗
NASA NTRS2004

The Design of Collectives of Agents to Control Non-Markovian Systems

The Collective Intelligence (COIN) framework concerns the design of collectives of reinforcement-learning agents such that their interaction causes a provided "world" utility function concerning the entire collective to be maximized. Previously, we applied that framework to scenarios involving Markovian dynamics where no re-evolution of the system from counter-factual initial conditions (an often expensive calculation) is permitted. This approach sets the individual utility function of each agent to be both aligned with the world utility, and at the same time, easy for the associated agents to optimize. Here we extend that approach to systems involving non-Markovian dynamics. In computer simulations, we compare our techniques with each other and with conventional "team games". We show whereas in team games performance often degrades badly with time, it steadily improves when our techniques are used. We also investigate situations where the system's dimensionality is effectively reduced. We show that this leads to difficulties in the agents ability to learn. The implication is that learning is a property only of high-enough dimensional systems.

Lawson, John W.↗
NASA NTRS1988

An intelligent training system for space shuttle flight controllers

An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The system was designed to train Mission Control Center (MCC) Flight Dynamics Officers (FDOs) to deploy a certain type of satellite from the Space Shuttle. The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system consists of five components: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The interface provides the trainee with information of the characteristics of the current training session and with on-line help. The domain expert (DeplEx for Deploy Expert) contains the rules and procedural knowledge needed by the FDO to carry out the satellite deploy. The DeplEx also contains mal-rules which permit the identification and diagnosis of common errors made by the trainee. The training session manager (TSM) examines the actions of the trainee and compares them with the actions of DeplEx in order to determine appropriate responses. A trainee model is developed for each individual using the system. The model includes a history of the trainee's interactions with the training system and provides evaluative data on the trainee's current skill level. A training scenario generator (TSG) designs appropriate training exercises for each trainee based on the trainee model and the training goals. All of the expert system components of PD/ICAT communicate via a common blackboard. The PD/ICAT is currently being tested. Ultimately, this project will serve as a vehicle for developing a general architecture for intelligent training systems together with a software environment for creating such systems.

Loftin, R. Bowen↗
NASA NTRS1988

An intelligent training system for space shuttle flight controllers

An autonomous intelligent training system which integrates expert system technology with training/teaching methodologies is described. The system was designed to train Mission Control Center (MCC) Flight Dynamics Officers (FDOs) to deploy a certain type of satellite from the Space Shuttle. The Payload-assist module Deploys/Intelligent Computer-Aided Training (PD/ICAT) system consists of five components: a user interface, a domain expert, a training session manager, a trainee model, and a training scenario generator. The interface provides the trainee with information of the characteristics of the current training session and with on-line help. The domain expert (Dep1Ex for Deploy Expert) contains the rules and procedural knowledge needed by the FDO to carry out the satellite deploy. The Dep1Ex also contains mal-rules which permit the identification and diagnosis of common errors made by the trainee. The training session manager (TSM) examines the actions of the trainee and compares them with the actions of Dep1Ex in order to determine appropriate responses. A trainee model is developed for each individual using the system. The model includes a history of the trainee's interactions with the training system and provides evaluative data on the trainee's current skill level. A training scenario generator (TSG) designs appropriate training exercises for each trainee based on the trainee model and the training goals. All of the expert system components of PD/ICAT communicate via a common blackboard. The PD/ICAT is currently being tested. Ultimately, this project will serve as a vehicle for developing a general architecture for intelligent training systems together with a software environment for creating such systems.

Loftin, R. Bowen↗
NASA NTRS2004

NASA Tech Briefs, June 2004

Topics covered include: COTS MEMS Flow-Measurement Probes; Measurement of an Evaporating Drop on a Reflective Substrate; Airplane Ice Detector Based on a Microwave Transmission Line; Microwave/Sonic Apparatus Measures Flow and Density in Pipe; Reducing Errors by Use of Redundancy in Gravity Measurements; Membrane-Based Water Evaporator for a Space Suit; Compact Microscope Imaging System with Intelligent Controls; Chirped-Superlattice, Blocked-Intersubband QWIP; Charge-Dissipative Electrical Cables; Deep-Sea Video Cameras Without Pressure Housings; RFID and Memory Devices Fabricated Integrally on Substrates; Analyzing Dynamics of Cooperating Spacecraft; Spacecraft Attitude Maneuver Planning Using Genetic Algorithms; Forensic Analysis of Compromised Computers; Document Concurrence System; Managing an Archive of Images; MPT Prediction of Aircraft-Engine Fan Noise; Improving Control of Two Motor Controllers; Electro-deionization Using Micro-separated Bipolar Membranes; Safer Electrolytes for Lithium-Ion Cells; Rotating Reverse-Osmosis for Water Purification; Making Precise Resonators for Mesoscale Vibratory Gyroscopes; Robotic End Effectors for Hard-Rock Climbing; Improved Nutation Damper for a Spin-Stabilized Spacecraft; Exhaust Nozzle for a Multitube Detonative Combustion Engine; Arc-Second Pointer for Balloon-Borne Astronomical Instrument; Compact, Automated Centrifugal Slide-Staining System; Two-Armed, Mobile, Sensate Research Robot; Compensating for Effects of Humidity on Electronic Noses; Brush/Fin Thermal Interfaces; Multispectral Scanner for Monitoring Plants; Coding for Communication Channels with Dead-Time Constraints; System for Better Spacing of Airplanes En Route; Algorithm for Training a Recurrent Multilayer Perceptron; Orbiter Interface Unit and Early Communication System; White-Light Nulling Interferometers for Detecting Planets; and Development of Methodology for Programming Autonomous Agents.

Source record↗
DOE OSTI2025

Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower (DIVERS-H)

U.S. hydropower plants face potential threats from shrinking water supply, rising demands, and warmer stream temperatures from various causes. Power plant owners, operators, and regulators require new tools to take advantage of and interpret the diverse range of scientific data being produced by both observational methods (for example, satellite, radar, stream gauges) and computer modeling methods that evaluate and predict how earth's dynamic systems (atmosphere, oceans, land surface, and sea ice) are changing and interacting. Combining datasets such as these with AI-based analyses introduces a novel decision support system to help users anticipate and address potential impacts on power generation stations. This new technology has been named DIVERS-H for "Data Integration and Visualization for Enhanced Resilience and Sustainability in Hydropower." In Phase I, technical feasibility was established with the development and demonstration of all the new technologies that are required. Most notably, DIVERS-H will use new artificial intelligence (AI) methods to capture the complex dynamics of water availability, demand, and environmental changes. In addition, new data management software was developed, and a prototype user interface was implemented as the precursor to a full scale decision support system. With technical research complete, the project focus now shifts to development of a commercial software product to provide users with actionable insight into water availability and the risk/resilience of critical systems at their locations of interest. Although DIVER-H was originally conceived as a tool for hydroelectric power applications, the same underlying technology can be readily applied to other water-consuming systems including coal, natural gas, oil, and nuclear power plants.

Chaudhary, Aashish [Kitware, Inc., Clifton Park, N↗
DOE OSTI2023

A Mobile Edge Computing Framework for Traffic Optimization At Urban Intersections Through Cyber-Physical Integration

The stop-and-go traffic pattern on urban roads often results in excessive energy consumption because of unnecessary vehicle braking, idling, and accelerations. With the widespread and increased use of automobiles, this traffic pattern creates many negative impacts (e.g., delayed travel time, air pollution, and additional carbon emission) on the sustainability of our cities. Taking advantage of the recent emerging Internet of Things (IoT) and edge computing paradigms, we propose a mobile edge computing framework that integrates the capability of real-time vehicle-to-infrastructure communication and intelligent speed optimization algorithms into a mobile app to optimize individual vehicles' driving speed at signalized intersections. The optimization aims to mitigate the stop-and-go traffic pattern and its undesirable consequences in urban transportation systems. The framework consists of (1) a cyberinfrastructure-enabled dynamic messaging system for retrieving and delivering real-time traffic and signal phase and timing information from IoT-connected signal controllers and sensors, (2) a real-time speed optimization algorithm for generating intelligent speed advisory using vehicle's information (e.g., GPS and driving directions from mobile sensing) and corresponding signal and traffic information, and (3) an ad-hoc mobile computing environment that converts drivers' smartphones into edge devices to host the speed optimization algorithms for enabling intelligent advisory on the vehicle's driving speed within signalized corridors. The paper presents the design and implementation of the proposed framework. Finally, we demonstrate the feasibility, usefulness, and energy-saving benefits of our proposed framework and its prototyping mobile app on urban transportation systems through traffic simulation, real-vehicle laboratory experiments, an evaluative survey, and field communication tests. The simulation-based energy evaluation results show that the 100% usage of the mobile app can achieve 24% energy savings in the transportation system.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2005

Low-Speed Active Flow Control Laboratory Developed

The future of aviation propulsion systems is increasingly focused on the application of control technologies to significantly enhance the performance of a new generation of air vehicles. Active flow control refers to a set of technologies that manipulate the flow of air and combustion gases deep within the confines of an engine to dynamically alter its performance during flight. By employing active flow control, designers can create engines that are significantly lighter, are more fuel efficient, and produce lower emissions. In addition, the operating range of an engine can be extended, yielding safer transportation systems. The realization of these future propulsion systems requires the collaborative development of many base technologies to achieve intelligent, embedded control at the engine locations where it will be most effective. NASA Glenn Research Center s Controls and Dynamics Technology Branch has developed a state-of-the-art low-speed Active Flow Control Laboratory in which emerging technologies can be integrated and explored in a flexible, low-cost environment. The facility allows the most promising developments to be prescreened and optimized before being tested on higher fidelity platforms, thereby reducing the cost of experimentation and improving research effectiveness.

Culley, Dennis E.↗
NASA NTRS1995

Uses And Characteristics Of Dynamic Tradeoff Evaluation

Report discusses basic concepts, some applications, and performance characteristics of dynamic tradeoff evaluation (DTE). Basic concepts of DTE also described in "Dynamic Restructuring of Problems in Artificial Intelligence" (NPO-18488). DTE is method of enhancing real-time performance of artificial-intelligence system such as might be used to monitor data from multiple sensors in factory, aircraft, spacecraft, or other complex system of equipment. Report presents evaluation of DTE as applied to spacecraft-monitoring problems.

Schwuttke, Ursula M.↗
NASA NTRS1986

Solar array flight dynamic experiment

The purpose of the Solar Array Flight Dynamic Experiment (SAFDE) is to demonstrate the feasibility of on-orbit measurement and ground processing of large space structures dynamic characteristics. Test definition or verification provides the dynamic characteristic accuracy required for control systems use. An illumination/measurement system was developed to fly on space shuttle flight STS-31D. The system was designed to dynamically evaluate a large solar array called the Solar Array Flight Experiment (SAFE) that had been scheduled for this flight. The SAFDE system consisted of a set of laser diode illuminators, retroreflective targets, an intelligent star tracker receiver and the associated equipment to power, condition, and record the results. In six tests on STS-41D, data was successfully acquired from 18 retroreflector targets and ground processed, post flight, to define the solar array's dynamic characteristic. The flight experiment proved the viability of on-orbit test definition of large space structures dynamic characteristics. Future large space structures controllability should be greatly enhanced by this capability.

Schock, R. W.↗
NASA NTRS1987

Solar array flight dynamic experiment

The purpose of the Solar Array Flight Dynamic Experiment (SAFDE) is to demonstrate the feasibility of on-orbit measurement and ground processing of large space structures' dynamic characteristics. Test definition or verification provides the dynamic characteristic accuracy required for control systems use. An illumination/measurement system was developed to fly on space shuttle flight STS-41D. The system was designed to dynamically evaluate a large solar array called the Solar Array Flight Experiment (SAFE) that had been scheduled for this flight. The SAFDE system consisted of a set of laser diode illuminators, retroreflective targets, an intelligent star tracker receiver and the associated equipment to power, condition, and record the results. In six tests on STS-41D, data was successfully acquired from 18 retroreflector targets and ground processed, post flight, to define the solar array's dynamic characteristic. The flight experiment proved the viability of on-orbit test definition of large space structures dynamic characteristics. Future large space structures controllability should be greatly enhanced by this capability.

Schock, Richard W.↗
NASA NTRS1986

Solar array flight dynamic experiment

The purpose of the Solar Array Flight Dynamic Experiment (SAFDE) is to demonstrate the feasibility of on-orbit measurement and ground processing of large space structures dynamic characteristics. Test definition or verification provides the dynamic characteristic accuracy required for control systems use. An illumination/measurement system was developed to fly on Space Shuttle flight STS-31D. The system was designed to dynamically evaluate a large solar array called the Solar Array Flight Experiment (SAFE) that had been scheduled for this flight. The SAFDE system consisted of a set of laser diode illuminators, retroreflective targets, an intelligent star tracker receiver and the associated equipment to power, condition, and record the results. In six tests on STS-41D, data was successfully acquired from 18 retroreflector targets and ground processed, post flight, to define the solar array's dynamic characteristic. The flight experiment proved the viability of on-orbit test definition of large space structures dynamic characteristics. Future large space structures controllability should be greatly enhanced by this capability.

Schock, Richard W.↗
NASA NTRS1994

Coordinated control of a dual-arm dexterous robot using full immersion telepresence and virtual reality

Telepresence is an approach to teleoperation that provides egocentric, intuitive interactions between an operator and a remote environment. This approach takes advantage of the natural cognitive and sensory motor skills of an on-board crew and effectively transfers them to a slave robot. A dual alarm dexterous robot operating under telepresence control has been developed and initial evaluations of the system performing candidate EVA, IVA and planetary geological tasks were conducted. The results of our evaluation showed that telepresence control is very effective in transferring the operator's skills to the slave robot. However, the results also showed that, due to the kinematic and dynamics inconsistencies between the operator and the robot, a limited amount of intelligent automation is also required to carry out some to the tasks. Therefore, several enhancements have been made to the original system to increase the automated capabilities of the control system without losing the benefits of telepresence.

Li, Larry↗