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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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NASA NTRS2003

Application of Artificial Intelligence Techniques in Unmanned Aerial Vehicle Flight

This paper describes the development of an application of Artificial Intelligence for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in Artificial Intelligence (AI) at Nova southeastern University and as an adjunct to a project at NASA Goddard Space Flight Center's Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an AI method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed. A low cost approach was taken using freeware, gnu, software, and demo programs. The focus of this research has been to outline some of the AI techniques used for UAV flight control and discuss some of the tools used to apply AI techniques. The intent is to succeed with the implementation of applying AI techniques to actually control different aspects of the flight of an UAV.

Bauer, Frank H.↗
NASA NTRS2003

Intelligent Control Approaches for UAVs

This talk will present overviews of various intelligent control technologies currently being developed and studied at the NASA Ames Research Center as applicable to Unmanned Aerial Vehicles (UAVs), Mars flyers, and to the next generation of flight controllers for manned aircraft. The approaches being examined include: (a) direct adaptive dynamic inverse controller, (b) adaptive critic-based optimal trajectory generator; (c) optimal allocation technique based on linear programming; (4) immunized maneuvering using autopilot building blocks. These approaches can utilize, but do not require, fault detection and isolation information. Piloted and unmanned simulation studies are performed to examine if the intelligent flight control techniques adequately: 1) match flying qualities of modem fly-by-wire flight controllers under nominal conditions; 2) improve performance under failure conditions when sufficient control authority is available; and 3) achieve intelligent maneuvering capabilities for unmanned vehicles. Results obtained so far will be presented and discussed.

KrisnaKumar, Kalmanje↗
NASA NTRS2003

Application of Artificial Intelligence Techniques in Uninhabitated Aerial Vehicle Flight

This paper describes the development of an application of Artificial Intelligence (AI) for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in AI at NOVA southeastern University and a beginning project at NASA Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an Artificial Intelligence method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed.

Dufrene, Warren R., Jr.↗
NASA NTRS2004

An Architecture for Intelligent Systems Based on Smart Sensors

Based on requirements for a next-generation rocket test facility, elements of a prototype Intelligent Rocket Test Facility (IRTF) have been implemented. A key component is distributed smart sensor elements integrated using a knowledgeware environment. One of the specific goals is to imbue sensors with the intelligence needed to perform self diagnosis of health and to participate in a hierarchy of health determination at sensor, process, and system levels. The preliminary results provide the basis for future advanced development and validation using rocket test stand facilities at Stennis Space Center (SSC). We have identified issues important to further development of health-enabled networks, which should be of interest to others working with smart sensors and intelligent health management systems.

Schmalzel, John↗
NASA NTRS2004

Application of Artificial Intelligence Techniques in Uninhabited Aerial Vehicle Flight

This paper describes the development of an application of Artificial Intelligence (AI) for Unmanned Aerial Vehicle (UAV) control. The project was done as part of the requirements for a class in AI at NOVA Southeastearn University and a beginning project at NASA Wallops Flight Facility for a resilient, robust, and intelligent UAV flight control system. A method is outlined which allows a base level application for applying an Artificial Intelligence method, Fuzzy Logic, to aspects of Control Logic for UAV flight. One element of UAV flight, automated altitude hold, has been implemented and preliminary results displayed.

Dufrene, Warren R., Jr.↗
NASA NTRS2004

Intelligent Propulsion System Foundation Technology: Summary of Research

The purpose of this cooperative agreement was to develop a foundation of intelligent propulsion technologies for NASA and industry that will have an impact on safety, noise, emissions and cost. These intelligent engine technologies included sensors, electronics, communications, control logic, actuators, and smart materials and structures. Furthermore this cooperative agreement helped prepare future graduates to develop the revolutionary intelligent propulsion technologies that will be needed to ensure pre-eminence of the U.S. aerospace industry. The program consisted of three primary research areas (and associated work elements at Ohio universities): 1.0 Turbine Engine Prognostics, 2.0 Active Controls for Emissions and Noise Reduction, and 3.0 Active Structural Controls.

Williams, James C.↗
NASA NTRS2004

Experimental Test-Bed for Intelligent Passive Array Research

This document describes the test-bed designed for the investigation of passive direction finding, recognition, and classification of speech and sound sources using sensor arrays. The test-bed forms the experimental basis of the Intelligent Small-Scale Spatial Direction Finder (ISS-SDF) project, aimed at furthering digital signal processing and intelligent sensor capabilities of sensor array technology in applications such as rocket engine diagnostics, sensor health prognostics, and structural anomaly detection. This form of intelligent sensor technology has potential for significant impact on NASA exploration, earth science and propulsion test capabilities. The test-bed consists of microphone arrays, power and signal distribution modules, web-based data acquisition, wireless Ethernet, modeling, simulation and visualization software tools. The Acoustic Sensor Array Modeler I (ASAM I) is used for studying steering capabilities of acoustic arrays and testing DSP techniques. Spatial sound distribution visualization is modeled using the Acoustic Sphere Analysis and Visualization (ASAV-I) tool.

Solano, Wanda M.↗
NASA NTRS2005

IVHM Framework for Intelligent Integration for Vehicle Health Management

Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, is the process of assessing, preserving, and restoring system functionality across flight and techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of Integrated Intelligent Vehicle Management (IIVM). These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, this framework integrates technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear that IIVM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives. These systems include the following: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle Mission Planning, Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations.

Paris, Deidre↗
NASA NTRS2005

A Framework for Integration of IVHM Technologies for Intelligent Integration for Vehicle Management

As a part of the overall goal of developing Integrated Vehicle Health Management (IVHM) systems for aerospace vehicles, the NASA Faculty Fellowship Program (NFFP) at Marshall Space Flight Center has performed a pilot study on IVHM principals which integrates researched IVHM technologies in support of Integrated Intelligent Vehicle Management (IIVM). IVHM is the process of assessing, preserving, and restoring system functionality across flight and ground systems (NASA NGLT 2004). The framework presented in this paper integrates advanced computational techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of IIVM. These real-time responses allow the IIVM to modify the effected vehicle subsystem(s) prior to a catastrophic event. Furthermore, the objective of this pilot program is to develop and integrate technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear the IIVM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition, to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle mission Planning; Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations. The representative IVHM technologies for IIVH includes: 1) robust controllers for use in re-usable launch vehicles, 2) scaleable/flexible computer platform using heterogeneous communication, 3) coupled electromagnetic oscillators for enhanced communications, 4) Linux-based real-time systems, 5) genetic algorithms, 6) Bayesian Networks, 7) evolutionary algorithms, 8) dynamic systems control modeling, and 9) advanced sensing capabilities. This paper presents IVHM technologies developed under NASA's NFFP pilot project. The integration of these IVHM technologies forms the framework for IIVM.

Paris, Deidre E.↗
NASA NTRS2005

Business Intelligence Modeling in Launch Operations

This technology project is to advance an integrated Planning and Management Simulation Model for evaluation of risks, costs, and reliability of launch systems from Earth to Orbit for Space Exploration. The approach builds on research done in the NASA ARC/KSC developed Virtual Test Bed (VTB) to integrate architectural, operations process, and mission simulations for the purpose of evaluating enterprise level strategies to reduce cost, improve systems operability, and reduce mission risks. The objectives are to understand the interdependency of architecture and process on recurring launch cost of operations, provide management a tool for assessing systems safety and dependability versus cost, and leverage lessons learned and empirical models from Shuttle and International Space Station to validate models applied to Exploration. The systems-of-systems concept is built to balance the conflicting objectives of safety, reliability, and process strategy in order to achieve long term sustainability. A planning and analysis test bed is needed for evaluation of enterprise level options and strategies for transit and launch systems as well as surface and orbital systems. This environment can also support agency simulation .based acquisition process objectives. The technology development approach is based on the collaborative effort set forth in the VTB's integrating operations. process models, systems and environment models, and cost models as a comprehensive disciplined enterprise analysis environment. Significant emphasis is being placed on adapting root cause from existing Shuttle operations to exploration. Technical challenges include cost model validation, integration of parametric models with discrete event process and systems simulations. and large-scale simulation integration. The enterprise architecture is required for coherent integration of systems models. It will also require a plan for evolution over the life of the program. The proposed technology will produce long-term benefits in support of the NASA objectives for simulation based acquisition, will improve the ability to assess architectural options verses safety/risk for future exploration systems, and will facilitate incorporation of operability as a systems design consideration, reducing overall life cycle cost for future systems. The future of business intelligence of space exploration will focus on the intelligent system-of-systems real-time enterprise. In present business intelligence, a number of technologies that are most relevant to space exploration are experiencing the greatest change. Emerging patterns of set of processes rather than organizational units leading to end-to-end automation is becoming a major objective of enterprise information technology. The cost element is a leading factor of future exploration systems.

Bardina, Jorge E.↗
NASA NTRS2005

Intelligent Vehicle Health Management

As a part of the overall goal of developing Integrated Vehicle Health Management systems for aerospace vehicles, the NASA Faculty Fellowship Program (NFFP) at Marshall Space Flight Center has performed a pilot study on IVHM principals which integrates researched IVHM technologies in support of Integrated Intelligent Vehicle Management (IIVM). IVHM is the process of assessing, preserving, and restoring system functionality across flight and ground systems (NASA NGLT 2004). The framework presented in this paper integrates advanced computational techniques with sensor and communication technologies for spacecraft that can generate responses through detection, diagnosis, reasoning, and adapt to system faults in support of INM. These real-time responses allow the IIVM to modify the affected vehicle subsystem(s) prior to a catastrophic event. Furthermore, the objective of this pilot program is to develop and integrate technologies which can provide a continuous, intelligent, and adaptive health state of a vehicle and use this information to improve safety and reduce costs of operations. Recent investments in avionics, health management, and controls have been directed towards IIVM. As this concept has matured, it has become clear the INM requires the same sensors and processing capabilities as the real-time avionics functions to support diagnosis of subsystem problems. New sensors have been proposed, in addition, to augment the avionics sensors to support better system monitoring and diagnostics. As the designs have been considered, a synergy has been realized where the real-time avionics can utilize sensors proposed for diagnostics and prognostics to make better real-time decisions in response to detected failures. IIVM provides for a single system allowing modularity of functions and hardware across the vehicle. The framework that supports IIVM consists of 11 major on-board functions necessary to fully manage a space vehicle maintaining crew safety and mission objectives: Guidance and Navigation; Communications and Tracking; Vehicle Monitoring; Information Transport and Integration; Vehicle Diagnostics; Vehicle Prognostics; Vehicle mission Planning; Automated Repair and Replacement; Vehicle Control; Human Computer Interface; and Onboard Verification and Validation. Furthermore, the presented framework provides complete vehicle management which not only allows for increased crew safety and mission success through new intelligence capabilities, but also yields a mechanism for more efficient vehicle operations. The representative IVHM technologies for computer platform using heterogeneous communication, 3) coupled electromagnetic oscillators for enhanced communications, 4) Linux-based real-time systems, 5) genetic algorithms, 6) Bayesian Networks, 7) evolutionary algorithms, 8) dynamic systems control modeling, and 9) advanced sensing capabilities. This paper presents IVHM technologies developed under NASA's NFFP pilot project and the integration of these technologies forms the framework for IIVM.

Paris, Deidre E.↗
NASA NTRS2004

Compact Microscope Imaging System With Intelligent Controls Improved

The Compact Microscope Imaging System (CMIS) with intelligent controls is a diagnostic microscope analysis tool with intelligent controls for use in space, industrial, medical, and security applications. This compact miniature microscope, which can perform tasks usually reserved for conventional microscopes, has unique advantages in the fields of microscopy, biomedical research, inline process inspection, and space science. Its unique approach integrates a machine vision technique with an instrumentation and control technique that provides intelligence via the use of adaptive neural networks. The CMIS system was developed at the NASA Glenn Research Center specifically for interface detection used for colloid hard spheres experiments; biological cell detection for patch clamping, cell movement, and tracking; and detection of anode and cathode defects for laboratory samples using microscope technology.

McDowell, Mark↗
NASA NTRS2005

A Demonstration of a Retrofit Architecture for Intelligent Control and Diagnostics of a Turbofan Engine

A retrofit architecture for intelligent turbofan engine control and diagnostics that changes the fan speed command to maintain thrust is proposed and its demonstration in a piloted flight simulator is described. The objective of the implementation is to increase the level of autonomy of the propulsion system, thereby reducing pilot workload in the presence of anomalies and engine degradation due to wear. The main functions of the architecture are to diagnose the cause of changes in the engine s operation, warning the pilot if necessary, and to adjust the outer loop control reference signal in response to the changes. This requires that the retrofit control architecture contain the capability to determine the changed relationship between fan speed and thrust, and the intelligence to recognize the cause of the change in order to correct it or warn the pilot. The proposed retrofit architecture is able to determine the fan speed setting through recognition of the degradation level of the engine, and it is able to identify specific faults and warn the pilot. In the flight simulator it was demonstrated that when degradation is introduced into an engine with standard fan speed control, the pilot needs to take corrective action to maintain heading. Utilizing the intelligent retrofit control architecture, the engine thrust is automatically adjusted to its expected value, eliminating yaw without pilot intervention.

Litt, Jonathan S.↗
NASA NTRS2005

Decision Facilitator for Launch Operations using Intelligent Agents

Launch operations require millions of micro-decisions which contribute to the macro decision of 'Go/No-Go' for a launch. Knowledge workers"(such as managers and technical professionals) need information in a timely precise manner as it can greatly affect mission success. The intelligent agent (web search agent) uses the words of a hypertext markup language document which is connected through the internet. The intelligent agent's actions are to determine if its goal of seeking a website containing a specified target (e.g., keyword or phrase), has been met. There are few parameters that should be defined for the keyword search like "Go" and "No-Go". Instead of visiting launch and range decision making servers individually, the decision facilitator constantly connects to all servers, accumulating decisions so the final decision can be decided in a timely manner. The facilitator agent uses the singleton design pattern, which ensures that only a single instance of the facilitator agent exists at one time. Negotiations could proceed between many agents resulting in a final decision. This paper describes details of intelligent agents and their interaction to derive an unified decision support system.

Thirumalainambi, Rajkumar↗
NASA NTRS2005

NASA Intelligent Systems Project: Results, Accomplishments and Impact on Science Missions

The Intelligent Systems Project was responsible for much of NASA's programmatic investment in artificial intelligence and advanced information technologies. IS has completed three major project milestones which demonstrated increased capabilities in autonomy, human centered computing, and intelligent data understanding. Autonomy involves the ability of a robot to place an instrument on a remote surface with a single command cycle. Human centered computing supported a collaborative, mission centric data and planning system for the Mars Exploration Rovers and data understanding has produced key components of a terrestrial satellite observation system with automated modeling and data analysis capabilities. This paper summarizes the technology demonstrations and metrics which quantify and summarize these new technologies which are now available for future Nasa missions.

Coughlan, Joseph C.↗
NASA NTRS2006

Intelligent Sensors and Components for On-Board ISHM

A viewgraph presentation on the development of intelligent sensors and components for on-board Integrated Systems Health Health Management (ISHM) is shown. The topics include: 1) Motivation; 2) Integrated Systems Health Management (ISHM); 3) Intelligent Components; 4) IEEE 1451; 5)Intelligent Sensors; 6) Application; and 7) Future Directions

Figueroa, Jorge↗
NASA NTRS2007

A Probabilistic System Analysis of Intelligent Propulsion System Technologies

NASA s Intelligent Propulsion System Technology (Propulsion 21) project focuses on developing adaptive technologies that will enable commercial gas turbine engines to produce fewer emissions and less noise while increasing reliability. It features adaptive technologies that have included active tip-clearance control for turbine and compressor, active combustion control, turbine aero-thermal and flow control, and enabling technologies such as sensors which are reliable at high operating temperatures and are minimally intrusive. A probabilistic system analysis is performed to evaluate the impact of these technologies on aircraft CO2 (directly proportional to fuel burn) and LTO (landing and takeoff) NO(x) reductions. A 300-passenger aircraft, with two 396-kN thrust (85,000-pound) engines is chosen for the study. The results show that NASA s Intelligent Propulsion System technologies have the potential to significantly reduce the CO2 and NO(x) emissions. The results are used to support informed decisionmaking on the development of the intelligent propulsion system technology portfolio for CO2 and NO(x) reductions.

Tong, Michael T.↗
NASA NTRS2008

Intelligent Propulsion System Foundation Technology: Summary of Research

The purpose of this cooperative agreement was to develop a foundation of intelligent propulsion technologies for NASA and industry that will have an impact on safety, noise, emissions, and cost. These intelligent engine technologies included sensors, electronics, communications, control logic, actuators, smart materials and structures, and system studies. Furthermore, this cooperative agreement helped prepare future graduates to develop the revolutionary intelligent propulsion technologies that will be needed to ensure pre-eminence of the U.S. aerospace industry. This Propulsion 21 - Phase 11 program consisted of four primary research areas and associated work elements at Ohio universities: 1.0 Turbine Engine Prognostics, 2.0 Active Controls for Emissions and Noise Reduction, 3.0 Active Structural Controls and Performance, and 4.0 System Studies and Integration. Phase l, which was conducted during the period August 1, 2003, through September 30, 2004, has been reported separately.

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