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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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Requirements to Design to Code: Towards a Fully Formal Approach to Automatic Code Generation

A general-purpose method to mechanically transform system requirements into a provably equivalent model has yet to appear. Such a method represents a necessary step toward high-dependability system engineering for numerous possible application domains, including distributed software systems, sensor networks, robot operation, complex scripts for spacecraft integration and testing, and autonomous systems. Currently available tools and methods that start with a formal model of a system and mechanically produce a provably equivalent implementation are valuable but not sufficient. The gap that current tools and methods leave unfilled is that their formal models cannot be proven to be equivalent to the system requirements as originated by the customer. For the classes of systems whose behavior can be described as a finite (but significant) set of scenarios, we offer a method for mechanically transforming requirements (expressed in restricted natural language, or in other appropriate graphical notations) into a provably equivalent formal model that can be used as the basis for code generation and other transformations.

Hinchey, Michael G.↗
NASA NTRS2005

Requirements to Design to Code: Towards a Fully Formal Approach to Automatic Code Generation

A general-purpose method to mechanically transform system requirements into a provably equivalent model has yet to appear. Such a method represents a necessary step toward high-dependability system engineering for numerous possible application domains, including distributed software systems, sensor networks, robot operation, complex scripts for spacecraft integration and testing, and autonomous systems. Currently available tools and methods that start with a formal model of a: system and mechanically produce a provably equivalent implementation are valuable but not sufficient. The "gap" that current tools and methods leave unfilled is that their formal models cannot be proven to be equivalent to the system requirements as originated by the customer. For the ciasses of systems whose behavior can be described as a finite (but significant) set of scenarios, we offer a method for mechanically transforming requirements (expressed in restricted natural language, or in other appropriate graphical notations) into a provably equivalent formal model that can be used as the basis for code generation and other transformations.

Hinchey, Michael G.↗
NASA NTRS1995

Optical fiber sensors for damage analysis in aerospace materials

Under this grant, fiber optic sensors were investigated for use in the nondestructive evaluation of aging aircraft. Specifically, optical fiber sensors for detection and location of impacts on a surface, and for detection of corrosion in metals were developed. The use of neural networks was investigated for determining impact location by processing the output of a network of fiberoptic strain sensors distributed on a surface. This approach employs triangulation to determine location by comparing the arrival times at several sensors, of the acoustic signal generated by the impact. For this study, a neural network simulator running on a personal computer was used to train a network using a back-propagation algorithm. Fiber optic extrinsic Fabry-Perot interferometer (EFPI) strain sensors are attached to or embedded in the surface, so that stress waves emanating from an impact can be detected. The ability of the network to determine impact location by time-or-arrival of acoustic signals was assessed by comparing network outputs with actual experimental results using impacts on a panel instrumented with optical fiber sensors. Using the neural network to process the sensor outputs, the impact location can be inferred to centimeter range accuracy directly from the arrival time data. In addition, the network can be trained to determine impact location, regardless of material anisotropy. Results demonstrate that a back-propagation network identifies impact location for an anisotropic graphite/bismaleimide plate with the same accuracy as that for an isotropic aluminum plate. Two different approaches were investigated for the development of fiber optic sensors for corrosion detection in metals, both utilizing optical fiber sensors with metal coatings. In the first approach, an extrinsic Fabry-Perot interferometric fiber optic strain sensor was placed under tensile stress, and while in the resulting strained position, a thick coating of metal was applied. Due to an increase in the quantity of material, the sensor does not return to its original position upon removal of the applied stress, and some residual strain is maintained within the sensor element. As the metal thickness decreases due to corrosion, this strain is released, providing the sensing mechanism for corrosion detection. In the second approach, photosensitive optical fibers with long period Bragg gratings in the core were coated with metal. The Bragg gratings serve to couple core modes at discrete wavelengths to cladding modes. Since cladding modes interact with the metal coating surrounding the fiber cladding, the specific wavelengths coupled from core to cladding depend on the refractive index of the metal coating. Therefore, as the metal corrodes, the resulting change in index of the coating may be measured by measuring the change in wavelength of the coupled mode. Results demonstrate that both approaches can be successfully used to track the loss in metal coating on the optical fiber sensors due to corrosion.

Schindler, Paul↗
NASA NTRS1994

Micro-Sensor Networks

The concept of an all-fiber, IC-based sensor network is presented. Fundamental issues including topology tradeoffs, power budget, and power distribution subsystem are analyzed. Some potential applications of such sensor networks are discussed.

networks↗
NASA NTRS1995

Microelectromechanical Systems

Micro-electromechanical systems (MEMS) is an enabling technology that merges computation and communication with sensing and actuation to change the way people and machines interact with the physical world. MEMS is a manufacturing technology that will impact widespread applications including: miniature inertial measurement measurement units for competent munitions and personal navigation; distributed unattended sensors; mass data storage devices; miniature analytical instruments; embedded pressure sensors; non-invasive biomedical sensors; fiber-optics components and networks; distributed aerodynamic control; and on-demand structural strength. The long term goal of ARPA's MEMS program is to merge information processing with sensing and actuation to realize new systems and strategies for both perceiving and controlling systems, processes, and the environment. The MEMS program has three major thrusts: advanced devices and processes, system design, and infrastructure.

Gabriel, Kaigham J.↗
NASA NTRS2010

Distributed Prognostic Health Management with Gaussian Process Regression

Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper. we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.

Saha, Sankalita↗
NASA NTRS2007

Switched Band-Pass Filters for Adaptive Transceivers

Switched band-pass filters are key components of proposed adaptive, software- defined radio transceivers that would be parts of envisioned digital-data-communication networks that would enable real-time acquisition and monitoring of data from geographically distributed sensors. Examples of sensors to be connected to such networks include security cameras, radio-frequency identification units, and geolocation units based on the Global Positioning System. Through suitable software configuration and without changing hardware, these transceivers could be made to operate according to any of a number of complex wireless-communication standards that could be characterized by diverse modulation schemes, bandwidths, and data-handling protocols. The adaptive transceivers would include field-programmable gate arrays (FPGAs) and digital signal-processing hardware. In the receiving path of a transceiver, the incoming signal would be amplified by a low-noise amplifier (LNA). The output spectrum of the LNA would be processed by a band-pass filter operating in the frequency range between 900 MHz and 2.4 GHz. Then a down-converter would translate the signal to a lower frequency range to facilitate analog-to-digital conversion, which would be followed by baseband processing by one or more FPGAs. In the transmitting path, a digital stream would first be converted to an analog signal, which would then be up-converted to a selected frequency band before being applied to a transmitting power amplifier. The aforementioned band-pass filter in the receiving path would be a combination of resonant inductor-and-capacitor filters and switched band-pass filters. The overall combination would implement a switch function designed mathematically to exhibit desired frequency responses and to switch the signal in each frequency band to an analog-to-digital converter appropriate for that band to produce a digital intermediate-frequency signal for digital signal processing.

Wang, Ray↗
NASA NTRS1992

Sensor fusion V; Proceedings of the Meeting, Boston, MA, Nov. 15-17, 1992

Topics addressed include 3D object perception, human-machine interface in multisensor systems, sensor fusion architecture, fusion of multiple and distributed sensors, interface and decision models for sensor fusion, computational networks, simple sensing for complex action, multisensor-based control, and metrology and calibration of multisensor systems. Particular attention is given to controlling 3D objects by sketching 2D views, the graphical simulation and animation environment for flexible structure robots, designing robotic systems from sensorimotor modules, cylindrical object reconstruction from a sequence of images, an accurate estimation of surface properties by integrating information using Bayesian networks, an adaptive fusion model for a distributed detection system, multiple concurrent object descriptions in support of autonomous navigation, robot control with multiple sensors and heuristic knowledge, and optical array detectors for image sensors calibration. (No individual items are abstracted in this volume)

Schenker, Paul S.↗
NASA NTRS2000

Sensor Fusion for a Network of Processes/Systems with Highly Autonomous Sensors

This paper describes a distributed sensor-data-fusion paradigm and theory based on a previously developed theory to model sensors as highly autonomous units. Generic procedures are defined to reason and make decisions at the qualitative level. This facilitates distribution of intelligence ( code and hardware) to the sensor level and peer-to-peer communication among sensors, controllers, and other devices in the system.

Figueroa, Fernando↗
NASA NTRS2011

Maturation of Structural Health Management Systems for Solid Rocket Motors

Concepts of an autonomous and automated space-compliant diagnostic system were developed for conditioned-based maintenance (CBM) of rocket motors for space exploration vehicles. The diagnostic system will provide real-time information on the integrity of critical structures on launch vehicles, improve their performance, and greatly increase crew safety while decreasing inspection costs. Using the SMART Layer technology as a basis, detailed procedures and calibration techniques for implementation of the diagnostic system were developed. The diagnostic system is a distributed system, which consists of a sensor network, local data loggers, and a host central processor. The system detects external impact to the structure. The major functions of the system include an estimate of impact location, estimate of impact force at impacted location, and estimate of the structure damage at impacted location. This system consists of a large-area sensor network, dedicated multiple local data loggers with signal processing and data analysis software to allow for real-time, in situ monitoring, and longterm tracking of structural integrity of solid rocket motors. Specifically, the system could provide easy installation of large sensor networks, onboard operation under harsh environments and loading, inspection of inaccessible areas without disassembly, detection of impact events and impact damage in real-time, and monitoring of a large area with local data processing to reduce wiring.

Quing, Xinlin↗
NASA NTRSDate not supplied

Development and Field Test Results of Distributed Ground Sensor Fusion Based Object Tracking

Autonomous operations are a crucial aspect in the context of Advanced Air Mobility and other emerging aviation markets. In order to enable this autonomy, an accurate and detailed understanding of the positions of the various vehicles in the air is necessary. Full localization independent of on-board sensors makes the system suitable for noncooperative vehicles. This paper focuses on the object tracking part that relies on distributed ground-based RF and other sensor fusion, considering specific properties and limitations of different sensor types. Results show satisfactory performance in nominal scenarios with full coverage for some sensor types, but RF signals are challenging because of their nature. This paper includes the results from simulations as well as field tests to support the observations and conclusions.

sensor fusion↗
NASA NTRS2013

Distributed Prognostics and Health Management with a Wireless Network Architecture

A heterogeneous set of system components monitored by a varied suite of sensors and a particle-filtering (PF) framework, with the power and the flexibility to adapt to the different diagnostic and prognostic needs, has been developed. Both the diagnostic and prognostic tasks are formulated as a particle-filtering problem in order to explicitly represent and manage uncertainties in state estimation and remaining life estimation. Current state-of-the-art prognostic health management (PHM) systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to a loss in functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become for a number of reasons somewhat ungainly for successful deployment, and efficient distributed architectures can be more beneficial. The distributed health management architecture is comprised of a network of smart sensor devices. These devices monitor the health of various subsystems or modules. They perform diagnostics operations and trigger prognostics operations based on user-defined thresholds and rules. The sensor devices, called computing elements (CEs), consist of a sensor, or set of sensors, and a communication device (i.e., a wireless transceiver beside an embedded processing element). The CE runs in either a diagnostic or prognostic operating mode. The diagnostic mode is the default mode where a CE monitors a given subsystem or component through a low-weight diagnostic algorithm. If a CE detects a critical condition during monitoring, it raises a flag. Depending on availability of resources, a networked local cluster of CEs is formed that then carries out prognostics and fault mitigation by efficient distribution of the tasks. It should be noted that the CEs are expected not to suspend their previous tasks in the prognostic mode. When the prognostics task is over, and after appropriate actions have been taken, all CEs return to their original default configuration. Wireless technology-based implementation would ensure more flexibility in terms of sensor placement. It would also allow more sensors to be deployed because the overhead related to weights of wired systems is not present. Distributed architectures are furthermore generally robust with regard to recovery from node failures.

Goebel, Kai↗
NASA NTRS2013

Distributed Engine Control Empirical/Analytical Verification Tools

NASA's vision for an intelligent engine will be realized with the development of a truly distributed control system featuring highly reliable, modular, and dependable components capable of both surviving the harsh engine operating environment and decentralized functionality. A set of control system verification tools was developed and applied to a C-MAPSS40K engine model, and metrics were established to assess the stability and performance of these control systems on the same platform. A software tool was developed that allows designers to assemble easily a distributed control system in software and immediately assess the overall impacts of the system on the target (simulated) platform, allowing control system designers to converge rapidly on acceptable architectures with consideration to all required hardware elements. The software developed in this program will be installed on a distributed hardware-in-the-loop (DHIL) simulation tool to assist NASA and the Distributed Engine Control Working Group (DECWG) in integrating DCS (distributed engine control systems) components onto existing and next-generation engines.The distributed engine control simulator blockset for MATLAB/Simulink and hardware simulator provides the capability to simulate virtual subcomponents, as well as swap actual subcomponents for hardware-in-the-loop (HIL) analysis. Subcomponents can be the communication network, smart sensor or actuator nodes, or a centralized control system. The distributed engine control blockset for MATLAB/Simulink is a software development tool. The software includes an engine simulation, a communication network simulation, control algorithms, and analysis algorithms set up in a modular environment for rapid simulation of different network architectures; the hardware consists of an embedded device running parts of the CMAPSS engine simulator and controlled through Simulink. The distributed engine control simulation, evaluation, and analysis technology provides unique capabilities to study the effects of a given change to the control system in the context of the distributed paradigm. The simulation tool can support treatment of all components within the control system, both virtual and real; these include communication data network, smart sensor and actuator nodes, centralized control system (FADEC full authority digital engine control), and the aircraft engine itself. The DECsim tool can allow simulation-based prototyping of control laws, control architectures, and decentralization strategies before hardware is integrated into the system. With the configuration specified, the simulator allows a variety of key factors to be systematically assessed. Such factors include control system performance, reliability, weight, and bandwidth utilization.

DeCastro, Jonathan↗
NASA NTRS2005

Biomimetic Models for An Ecological Approach to Massively-Deployed Sensor Networks

Promises of ubiquitous control of the physical environment by massively-deployed wireless sensor networks open avenues for new applications that will redefine the way we live and work. Due to small size and low cost of sensor devices, visionaries promise systems enabled by deployment of massive numbers of sensors ubiquitous throughout our environment working in concert. Recent research has concentrated on developing techniques for performing relatively simple tasks with minimal energy expense, assuming some form of centralized control. Unfortunately, centralized control is not conducive to parallel activities and does not scale to massive size networks. Execution of simple tasks in sparse networks will not lead to the sophisticated applications predicted. We propose a new way of looking at massively-deployed sensor networks, motivated by lessons learned from the way biological ecosystems are organized. We demonstrate that in such a model, fully distributed data aggregation can be performed in a scalable fashion in massively deployed sensor networks, where motes operate on local information, making local decisions that are aggregated across the network to achieve globally-meaningful effects. We show that such architectures may be used to facilitate communication and synchronization in a fault-tolerant manner, while balancing workload and required energy expenditure throughout the network.

Jones, Kennie H.↗
NASA NTRS2005

Distributing Data from Desktop to Hand-Held Computers

A system of server and client software formats and redistributes data from commercially available desktop to commercially available hand-held computers via both wired and wireless networks. This software is an inexpensive means of enabling engineers and technicians to gain access to current sensor data while working in locations in which such data would otherwise be inaccessible. The sensor data are first gathered by a data-acquisition server computer, then transmitted via a wired network to a data-distribution computer that executes the server portion of the present software. Data in all sensor channels -- both raw sensor outputs in millivolt units and results of conversion to engineering units -- are made available for distribution. Selected subsets of the data are transmitted to each hand-held computer via the wired and then a wireless network. The selection of the subsets and the choice of the sequences and formats for displaying the data is made by means of a user interface generated by the client portion of the software. The data displayed on the screens of hand-held units can be updated at rates from 1 to

Elmore, Jason L.↗
NASA NTRS2011

Virtual Sensor Test Instrumentation

Virtual Sensor Test Instrumentation is based on the concept of smart sensor technology for testing with intelligence needed to perform sell-diagnosis of health, and to participate in a hierarchy of health determination at sensor, process, and system levels. A virtual sensor test instrumentation consists of five elements: (1) a common sensor interface, (2) microprocessor, (3) wireless interface, (4) signal conditioning and ADC/DAC (analog-to-digital conversion/ digital-to-analog conversion), and (5) onboard EEPROM (electrically erasable programmable read-only memory) for metadata storage and executable software to create powerful, scalable, reconfigurable, and reliable embedded and distributed test instruments. In order to maximize the efficient data conversion through the smart sensor node, plug-and-play functionality is required to interface with traditional sensors to enhance their identity and capabilities for data processing and communications. Virtual sensor test instrumentation can be accessible wirelessly via a Network Capable Application Processor (NCAP) or a Smart Transducer Interlace Module (STIM) that may be managed under real-time rule engines for mission-critical applications. The transducer senses the physical quantity being measured and converts it into an electrical signal. The signal is fed to an A/D converter, and is ready for use by the processor to execute functional transformation based on the sensor characteristics stored in a Transducer Electronic Data Sheet (TEDS). Virtual sensor test instrumentation is built upon an open-system architecture with standardized protocol modules/stacks to interface with industry standards and commonly used software. One major benefit for deploying the virtual sensor test instrumentation is the ability, through a plug-and-play common interface, to convert raw sensor data in either analog or digital form, to an IEEE 1451 standard-based smart sensor, which has instructions to program sensors for a wide variety of functions. The sensor data is processed in a distributed fashion across the network, providing a large pool of resources in real time to meet stringent latency requirements.

Wang, Roy↗
NASA NTRS2004

Wind Tunnel Test of an RPV with Shape-Change Control Effector and Sensor Arrays

A variety of novel control effector concepts have recently emerged that may enable new approaches to flight control. In particular, the potential exists to shift the composition of the typical aircraft control effector suite from a small number of high authority, specialized devices (rudder, aileron, elevator, flaps), toward larger numbers of smaller, less specialized, distributed device arrays. The concept envisions effector and sensor networks composed of relatively small high-bandwidth devices able to simultaneously perform a variety of control functions using feedback from disparate data sources. To investigate this concept, a remotely piloted flight vehicle has been equipped with an array of 24 trailing edge shape-change effectors and associated pressure measurements. The vehicle, called the Multifunctional Effector and Sensor Array (MESA) testbed, was recently tested in NASA Langley's 12-ft Low Speed wind tunnel to characterize its stability properties, control authorities, and distributed pressure sensitivities for use in a dynamic simulation prior to flight testing. Another objective was to implement and evaluate a scheme for actively controlling the spanwise pressure distribution using the shape-change array. This report describes the MESA testbed, design of the pressure distribution controller, and results of the wind tunnel test.

Raney, David L.↗