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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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A description is given of an automatic computer controlled second generation breathing metabolic simulator (BMS). The simulator is used for evaluating and testing respiratory diagnostic, monitoring, support, and resuscitation equipment. Any desired sequence of metabolic activities can be simulated on the device for up to 15 hours. The computer monitors test procedures and provides printouts of test results.
The system breadboard for the demonstration of Automated Power Systems Management (APSM) functions has been designed, fabricated, and is in the final stages of verification and testing. APSM functions fall into categories of fault detection and correction, commanding system and subsystem test and diagnoses, relay position monitoring, data acquisition and processing, and power management. All these functions are accomplished through software residing in 8-bit microprocessors dedicated to each group of Viking Orbiter power breadboard subassemblies and a 16-bit microprocessor serving as the central processor for the power system. This paper describes key monitoring, diagnostic, and control algorithms used in the APSM breadboard.
The purpose of Space Experiments with Particle Accelerators (SEPAC) on the Atmospheric Laboratory for Applications and Science (ATLAS 1) mission, is to carry out active and interactive experiments on and in the earth's ionosphere, atmosphere, and magnetosphere. The instruments to be used are an electron beam accelerator (EBA), plasma contactor, and associated instruments the purpose of which is to perform diagnostic, monitoring, and general data taking functions. Four major classes of investigations are to be performed by SEPAC. They are: beam plasma physics, beam-atmosphere interactions, the use of modulated electron beams as transmitting antennas, and the use of electron beams for remote sensing of electric and magnetic fields. The first class consists mainly of onboard plasma physics experiments to measure the effects of phenomena in the vicinity of the shuttle. The last three are concerned with remote effects and are supported by other ATLAS 1 investigations as well as by ground-based observations.
The topics presented are covered in viewgraph form. The programmatic objectives are to develop and validate technology, design tools, and methodologies needed for the development of a new generation of lower cost, operationally-efficient, long-life, highly reliable ETO propulsion systems. The technical objectives are: (1) high quality, low cost, inspectable manufacturing; (2) safe shutdown to fault tolerance operations for safety; (3) condition monitoring diagnostics for maintainability; (4) automatic servicing and checkout for ground operations; (5) max commensurate with life for performance; and full flow, combined cycle for advanced cycles.
The overall goal of this research effort has been the development of a software system which automates tasks related to monitoring and controlling electrical power distribution in spacecraft electrical power systems. The resulting software system is called the Intelligent Power Controller (IPC). The specific tasks performed by the IPC include continuous monitoring of the flow of power from a source to a set of loads, fast detection of anomalous behavior indicating a fault to one of the components of the distribution systems, generation of diagnosis (explanation) of anomalous behavior, isolation of faulty object from remainder of system, and maintenance of flow of power to critical loads and systems (e.g. life-support) despite fault conditions being present (recovery). The IPC system has evolved out of KATE (Knowledge-based Autonomous Test Engineer), developed at NASA-KSC. KATE consists of a set of software tools for developing and applying structure and behavior models to monitoring, diagnostic, and control applications.
This describes a knowledge base (KB) partitioning approach to solve the problem of real-time performance using the CLIPS AI shell when containing large numbers of rules and facts. This work is funded under the joint USAF/NASA Advanced Launch System (ALS) Program as applied research in expert systems to perform vehicle checkout for real-time controller and diagnostic monitoring tasks. The Expert System advanced development project (ADP-2302) main objective is to provide robust systems responding to new data frames of 0.1 to 1.0 second intervals. The intelligent system control must be performed within the specified real-time window, in order to meet the demands of the given application. Partitioning the KB reduces the complexity of the inferencing Rete net at any given time. This reduced complexity improves performance but without undo impacts during load and unload cycles. The second objective is to produce highly reliable intelligent systems. This requires simple and automated approaches to the KB verification & validation task. Partitioning the KB reduces rule interaction complexity overall. Reduced interaction simplifies the V&V testing necessary by focusing attention only on individual areas of interest. Many systems require a robustness that involves a large number of rules, most of which are mutually exclusive under different phases or conditions. The ideal solution is to control the knowledge base by loading rules that directly apply for that condition, while stripping out all rules and facts that are not used during that cycle. The practical approach is to cluster rules and facts into associated 'blocks'. A simple approach has been designed to control the addition and deletion of 'blocks' of rules and facts, while allowing real-time operations to run freely. Timing tests for real-time performance for specific machines under R/T operating systems have not been completed but are planned as part of the analysis process to validate the design.
The growth of protein crystals suitable for X-ray crystal structure analysis is an important topic. The methods of protein crystal growth are under increasing study whereby different methods are being compared via diagnostic monitoring including Charge Coupled Device (CCD) video and interferometry. The quality (perfection) of protein crystals is now being evaluated by mosaicity analysis (rocking curves) and X-ray topographic images as well as the diffraction resolution limit and overall data quality. Choice of a liquid-liquid linear crystal growth geometry and microgravity can yield a spatial stability of growing crystals and fluid, as seen in protein crystallization experiments on the unmanned platform EURICA. A review is given here of existing results and experience over several microgravity missions. The results include CCD video as well as interferometry during the mission, followed, on return to earth, by rocking curve experiments and full X-ray data collection on LMS and earth control lysozyme crystals. Diffraction data recorded from LMS and ground control apocrustacyanin C(sub 1) crystals are also described.
FEAT, a software system for evaluating risks, was developed by Lockheed and later enhanced under NASA funding. FEAT uses directed graph - or digraph - models to provide information on cause and effect if a set of failure events occurs. James Miller, the program designer at Lockheed, formed DiGraphics, Inc. to market the software that has evolved from FEAT. The Diquest Analyzer, the company's flagship product, assists product designers in identifying the redundancies and weaknesses of a system. The software has applications in the chemical industry for risk assessment, design evaluation, and change management. Additional markets have been found in operations monitoring diagnostics and training of new personnel.
In planetary surface exploration mission, sensor data distribution is required in many aspects, for example, in navigation, scheduling, planning, monitoring, diagnostics, and automation of the field tasks. The challenge is to distribute such data in the robust and reliable way so that we can minimize the errors caused by miscalculations, and misjudgments that based on the error data input in the mission. The ad-hoc wireless network on planetary surface is not constantly connected because of the nature of the rough terrain and lack of permanent establishments on the surface. There are some disconnected moments that the computation nodes will re-associate with different repeaters or access points until connections are reestablished. Such a nature requires our sensor data distribution software robust and reliable with ability to tolerant disconnected moments. This paper presents a distributed components model as a framework to accomplish such tasks. The software is written in Java and utilized the available Java Message Services schema and the Boss implementation. The results of field experimentations show that the model is very effective in completing the tasks.
Carbon nanotubes (CNT) exhibit extraordinary mechanical properties and unique electronic properties and therefore, have received much attention for more than a decade now for a variety of applications ranging from nanoelectronics, composites to meeting needs in energy, environmental and other sectors. In this talk, we focus on some near term potential of CNT applications for both NASA and other Agency/societal needs. The most promising and successful application to date is a nano chem sensor at TRL 6 that uses a 16-256 sensor array in the construction of an electronic nose. Pristine, doped, functionalized and metal-loaded SWCNTs are used as conducting materials to provide chemical variation across the individual elements of the sensor array. This miniaturized sensor has been incorporated in an iPhone for homeland security applications. Gases and vapors relevant to leak detection in crew vehicles, biomedical, mining, chemical threats, industrial spills and others have been demonstrated. SWCNTs also respond to radiation exposure via a change in conductivity and therefore, a similar strategy is being pursued to construct a radiation nose to identify radiation sources (gamma, protons, neutrons, X-ray, etc.) with their energy levels. Carbon nanofibers (CNFs) grown using plasma enhanced CVD typically are vertical, individual, freestanding structures and therefore, are ideal for construction of nanoelectrodes. A nanoelectrode array (NEA) can be the basis for an affinity-based biosensor to meet the needs in applications such as lab-on-a-chip, environmental monitoring, cancer diagnostics, biothreat monitoring, water and food safety and others. A couple of demonstrations including detection of e-coli and ricin will be discussed. The NEA is also useful for implantation in the brain for deep brain stimulation and neuroengineering applications. Miniaturization of payload such as science instrumentation and power sources is critical to reduce launch costs. High current density (greater than 100 mA/per square centimeters) field emission capabilities of CNTs can be exploited for construction of electron gun for electron microscopy and X-ray tubes for spectrometers and baggage screening. A CNT pillar array configuration has been demonstrated, not only meeting the high current density needs but more importantly providing long term emitter stability. Finally, supercapacitors hold the promise to combine the high energy density of a battery with the high power density of capacitors. Traditional graphite electrodes have not delivered this promise yet. A novel design and processing approach using MWCNTs has shown a record 550 F/g capacitance along with significant device endurance. This supercapacitor is suitable for railgun launch application for NASA, powering rovers and robots, consumer electronics and future hybrid vehicles.
For any battery-powered vehicles (be it unmanned aerial vehicles, small passenger aircraft, or assets in exoplanetary operations) to operate at maximum efficiency and reliability, it is critical to monitor battery health as well performance and to predict end of discharge (EOD) and end of useful life (EOL). To fulfil these needs, it is important to capture the battery's inherent characteristics as well as operational knowledge in the form of models that can be used by monitoring, diagnostic, and prognostic algorithms. Several battery modeling methodologies have been developed in last few years as the understanding of underlying electrochemical mechanics has been advancing. The models can generally be classified as empirical models, electrochemical engineering models, multi-physics models, and molecular/atomist. Empirical models are based on fitting certain functions to past experimental data, without making use of any physicochemical principles. Electrical circuit equivalent models are an example of such empirical models. Electrochemical engineering models are typically continuum models that include electrochemical kinetics and transport phenomena. Each model has its advantages and disadvantages. The former type of model has the advantage of being computationally efficient, but has limited accuracy and robustness, due to the approximations used in developed model, and as a result of such approximations, cannot represent aging well. The latter type of model has the advantage of being very accurate, but is often computationally inefficient, having to solve complex sets of partial differential equations, and thus not suited well for online prognostic applications. In addition both multi-physics and atomist models are computationally expensive hence are even less suited to online application An electrochemistry-based model of Li-ion batteries has been developed, that captures crucial electrochemical processes, captures effects of aging, is computationally efficient, and is of suitable accuracy for reliable EOD prediction in a variety of operational profiles. The model can be considered an electrochemical engineering model, but unlike most such models found in the literature, certain approximations are done that allow to retain computational efficiency for online implementation of the model. Although the focus here is on Li-ion batteries, the model is quite general and can be applied to different chemistries through a change of model parameter values. Progress on model development, providing model validation results and EOD prediction results is being presented.
Presentation/lecture on systems health monitoring (diagnostics, prognostics, decision-making) with applications to the design phase of small satellite components and systems.
This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.
This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.
This paper presents a model-based architecture for performance trend monitoring and gas path fault diagnostics designed for analyzing streaming transient aircraft engine measurement data. The technique analyzes residuals between sensed engine outputs and model predicted outputs for fault detection and isolation purposes. Diagnostic results from the application of the approach to test data acquired from an aircraft turbofan engine are presented. The approach is found to avoid false alarms when presented nominal fault-free data. Additionally, the approach is found to successfully detect and isolate gas path seeded-faults under steady-state operating scenarios although some fault misclassifications are noted during engine transients. Recommendations for follow-on maturation and evaluation of the technique are also presented.
Tools for Engine Diagnostics is a major task in the Propulsion System Health Management area of the Single Aircraft Accident Prevention project under NASA s Aviation Safety Program. The major goal of the Aviation Safety Program is to reduce fatal aircraft accidents by 80 percent within 10 years and by 90 percent within 25 years. The goal of the Propulsion System Health Management area is to eliminate propulsion system malfunctions as a primary or contributing factor to the cause of aircraft accidents. The purpose of Tools for Engine Diagnostics, a 2-yr-old task, is to establish and improve tools for engine diagnostics and prognostics that measure the deformation and damage of rotating engine components at the ground level and that perform intermittent or continuous monitoring on the engine wing. In this work, nondestructive-evaluation- (NDE-) based technology is combined with model-dependent disk spin experimental simulation systems, like finite element modeling (FEM) and modal norms, to monitor and predict rotor damage in real time. Fracture mechanics time-dependent fatigue crack growth and damage-mechanics-based life estimation are being developed, and their potential use investigated. In addition, wireless eddy current and advanced acoustics are being developed for on-wing and just-in-time NDE engine inspection to provide deeper access and higher sensitivity to extend on-wing capabilities and improve inspection readiness. In the long run, these methods could establish a base for prognostic sensing while an engine is running, without any overt actions, like inspections. This damage-detection strategy includes experimentally acquired vibration-, eddy-current- and capacitance-based displacement measurements and analytically computed FEM-, modal norms-, and conventional rotordynamics-based models of well-defined damages and critical mass imbalances in rotating disks and rotors.
Report describes experimental evaluation of commercial electronic system designed to monitor vibration signal from accelerometer on gear-box to detect vibrations indicative of damage to gears. System includes signal-conditioning subsystem and personal computer in which analog-to-digital converter installed. Results show system fairly effective in detecting surface fatigue pits on spur-gear teeth.
Our objective is to provide a framework of automated tools and techniques for reducing operational and maintenance costs in the NASA's Deep Space Network (DSN).