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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Integrated Hardware and Software for No-Loss Computing

When an algorithm is distributed across multiple threads executing on many distinct processors, a loss of one of those threads or processors can potentially result in the total loss of all the incremental results up to that point. When implementation is massively hardware distributed, then the probability of a hardware failure during the course of a long execution is potentially high. Traditionally, this problem has been addressed by establishing checkpoints where the current state of some or part of the execution is saved. Then in the event of a failure, this state information can be used to recompute that point in the execution and resume the computation from that point. A serious problem arises when one distributes a problem across multiple threads and physical processors is that one increases the likelihood of the algorithm failing due to no fault of the scientist but as a result of hardware faults coupled with operating system problems. With good reason, scientists expect their computing tools to serve them and not the other way around. What is novel here is a unique combination of hardware and software that reformulates an application into monolithic structure that can be monitored in real-time and dynamically reconfigured in the event of a failure. This unique reformulation of hardware and software will provide advanced aeronautical technologies to meet the challenges of next-generation systems in aviation, for civilian and scientific purposes, in our atmosphere and in atmospheres of other worlds. In particular, with respect to NASA s manned flight to Mars, this technology addresses the critical requirements for improving safety and increasing reliability of manned spacecraft.

James, Mark↗
NASA NTRS1988

Advanced Earth-to-orbit propulsion technology program overview: Impact of civil space technology initiative

The NASA Earth-to-Orbit (ETO) Propulsion Technology Program is dedicated to advancing rocket engine technologies for the development of fully reusable engine systems that will enable space transportation systems to achieve low cost, routine access to space. The program addresses technology advancements in the areas of engine life extension/prediction, performance enhancements, reduced ground operations costs, and in-flight fault tolerant engine operations. The primary objective is to acquire increased knowledge and understanding of rocket engine chemical and physical processes in order to evolve more realistic analytical simulations of engine internal environments, to derive more accurate predictions of steady and unsteady loads, and using improved structural analyses, to more accurately predict component life and performance, and finally to identify and verify more durable advanced design concepts. In addition, efforts were focused on engine diagnostic needs and advances that would allow integrated health monitoring systems to be developed for enhanced maintainability, automated servicing, inspection, and checkout, and ultimately, in-flight fault tolerant engine operations.

Stephenson, Frank W., Jr.↗
NASA NTRS2015

Verification of Functional Fault Models and the Use of Resource Efficient Verification Tools

Functional fault models (FFMs) are a directed graph representation of the failure effect propagation paths within a system's physical architecture and are used to support development and real-time diagnostics of complex systems. Verification of these models is required to confirm that the FFMs are correctly built and accurately represent the underlying physical system. However, a manual, comprehensive verification process applied to the FFMs was found to be error prone due to the intensive and customized process necessary to verify each individual component model and to require a burdensome level of resources. To address this problem, automated verification tools have been developed and utilized to mitigate these key pitfalls. This paper discusses the verification of the FFMs and presents the tools that were developed to make the verification process more efficient and effective.

reliability↗
NASA NTRSDate not supplied

Gateway Power Quality Lessons Learned

Power Quality is a physical description of the electrical characteristics that allow the system to function properly without significant loss of performance or life. This physical description includes steady state voltage limits, transient voltage limits in normal/abnormal conditions, ripple voltage, stability, fault conditions, and more. All which are vital for improving reliability, ensuring stable operation, defining proper fault recovery, and ensuring a ‘plug and play’ approach to design and integration. Typically, a specification for Power Quality is created based on expected system performance or an existing standard, such as the International Space Power System Interoperability Standards (ISPSIS). One example of such a specification is the Gateway Power Quality Specification. It defines the requirements and characteristics of the 120 Volt direct current electrical power system for the Gateway Electrical Power System (EPS) and the Gateway Electrical Power Consuming Equipment (EPCE). This specification also maintains a separate requirement verification section that defines test methods for requirement verification. The test methods include analysis, test, inspection, and demonstration. These test methodologies and requirements are used to ensure that the loads operate when connected to the specified power quality and performance as defined by this specification. The challenge with developing a specification is that desired system characteristics are not always fully matured before the specification is needed and many performance requirements may be application specific. This drives the need to utilize lessons learned through extensive analysis and testing as well as historical knowledge to finalize requirements. Some example requirements where this is important are Small and Large Signal Stability, Ripple Voltage, Inrush/Surge Currents, and Fault Containment. Lessons learned are also important in the testing, analysis, and verification to ensure consistent and accurate results to verify performance. This presentation will cover the lessons learned for power quality relative to ripple, inrush/surge, fault containment, testing/verification, and more.

Power Quality↗
NASA NTRS1993

An architecture for object-oriented intelligent control of power systems in space

A control system for autonomous distribution and control of electrical power during space missions is being developed. This system should free the astronauts from localizing faults and reconfiguring loads if problems with the power distribution and generation components occur. The control system uses an object-oriented simulation model of the power system and first principle knowledge to detect, identify, and isolate faults. Each power system component is represented as a separate object with knowledge of its normal behavior. The reasoning process takes place at three different levels of abstraction: the Physical Component Model (PCM) level, the Electrical Equivalent Model (EEM) level, and the Functional System Model (FSM) level, with the PCM the lowest level of abstraction and the FSM the highest. At the EEM level the power system components are reasoned about as their electrical equivalents, e.g, a resistive load is thought of as a resistor. However, at the PCM level detailed knowledge about the component's specific characteristics is taken into account. The FSM level models the system at the subsystem level, a level appropriate for reconfiguration and scheduling. The control system operates in two modes, a reactive and a proactive mode, simultaneously. In the reactive mode the control system receives measurement data from the power system and compares these values with values determined through simulation to detect the existence of a fault. The nature of the fault is then identified through a model-based reasoning process using mainly the EEM. Compound component models are constructed at the EEM level and used in the fault identification process. In the proactive mode the reasoning takes place at the PCM level. Individual components determine their future health status using a physical model and measured historical data. In case changes in the health status seem imminent the component warns the control system about its impending failure. The fault isolation process uses the FSM level for its reasoning base.

Holmquist, Sven G.↗
NASA NTRS2008

Algorithms for Spectral Decomposition with Applications to Optical Plume Anomaly Detection

The analysis of spectral signals for features that represent physical phenomenon is ubiquitous in the science and engineering communities. There are two main approaches that can be taken to extract relevant features from these high-dimensional data streams. The first set of approaches relies on extracting features using a physics-based paradigm where the underlying physical mechanism that generates the spectra is used to infer the most important features in the data stream. We focus on a complementary methodology that uses a data-driven technique that is informed by the underlying physics but also has the ability to adapt to unmodeled system attributes and dynamics. We discuss the following four algorithms: Spectral Decomposition Algorithm (SDA), Non-Negative Matrix Factorization (NMF), Independent Component Analysis (ICA) and Principal Components Analysis (PCA) and compare their performance on a spectral emulator which we use to generate artificial data with known statistical properties. This spectral emulator mimics the real-world phenomena arising from the plume of the space shuttle main engine and can be used to validate the results that arise from various spectral decomposition algorithms and is very useful for situations where real-world systems have very low probabilities of fault or failure. Our results indicate that methods like SDA and NMF provide a straightforward way of incorporating prior physical knowledge while NMF with a tuning mechanism can give superior performance on some tests. We demonstrate these algorithms to detect potential system-health issues on data from a spectral emulator with tunable health parameters.

Srivastava, Askok N.↗
NASA NTRS2003

System Modeling and Diagnostics for Liquefying-Fuel Hybrid Rockets

A Hybrid Combustion Facility (HCF) was recently built at NASA Ames Research Center to study the combustion properties of a new fuel formulation that burns approximately three times faster than conventional hybrid fuels. Researchers at Ames working in the area of Integrated Vehicle Health Management recognized a good opportunity to apply IVHM techniques to a candidate technology for next generation launch systems. Five tools were selected to examine various IVHM techniques for the HCF. Three of the tools, TEAMS (Testability Engineering and Maintenance System), L2 (Livingstone2), and RODON, are model-based reasoning (or diagnostic) systems. Two other tools in this study, ICS (Interval Constraint Simulator) and IMS (Inductive Monitoring System) do not attempt to isolate the cause of the failure but may be used for fault detection. Models of varying scope and completeness were created, both qualitative and quantitative. In each of the models, the structure and behavior of the physical system are captured. In the qualitative models, the temporal aspects of the system behavior and the abstraction of sensor data are handled outside of the model and require the development of additional code. In the quantitative model, less extensive processing code is also necessary. Examples of fault diagnoses are given.

Poll, Scott↗
NASA NTRS1992

Fault management for the Space Station Freedom control center

This paper describes model based reasoning fault isolation in complex systems using automated digraph analysis. It discusses the use of the digraph representation as the paradigm for modeling physical systems and a method for executing these failure models to provide real-time failure analysis. It also discusses the generality, ease of development and maintenance, complexity management, and susceptibility to verification and validation of digraph failure models. It specifically describes how a NASA-developed digraph evaluation tool and an automated process working with that tool can identify failures in a monitored system when supplied with one or more fault indications. This approach is well suited to commercial applications of real-time failure analysis in complex systems because it is both powerful and cost effective.

Clark, Colin↗
NASA NTRS1990

Low cost management of replicated data in fault-tolerant distributed systems

Many distributed systems replicate data for fault tolerance or availability. In such systems, a logical update on a data item results in a physical update on a number of copies. The synchronization and communication required to keep the copies of replicated data consistent introduce a delay when operations are performed. A technique is described that relaxes the usual degree of synchronization, permitting replicated data items to be updated concurrently with other operations, while at the same time ensuring that correctness is not violated. The additional concurrency thus obtained results in better response time when performing operations on replicated data. How this technique performs in conjunction with a roll-back and a roll-forward failure recovery mechanism is also discussed.

Joseph, Thomas A.↗
NASA NTRSDate not supplied

Role of PHM in Autonomous Decision-Making: Aerospace applications

There is an increased need for onboard decision-making capabilities in cyber-physical systems be it in energy, automotive, aviation, space, or other industries as they aim for increased efficiency, resiliency, and mission assurance capabilities. Emerging next-gen technologies such as multi-rover planetary missions, distributed satellites, unmanned ground and aerial vehicle operations and smart grid systems rely on in-time risk assessment and autonomous decision-making. One critical piece of the autonomy puzzle is reliable prediction of system behavior under time-varying and potentially uncertain environmental conditions. Further, if agent states change during operation such as initiation of faults or degradation, reliable diagnostic tools need to be investigated. In this tutorial, we will revise approaches that integrates existing physics-based and data-driven models of agents interacting with probability models of the environment and component operation state. Role of existing PHM methodologies as they feed into decision-making under uncertainty will be studied. Balancing critical trade-offs between high-fidelity prognostic models, prediction time-horizons and the computational requirements for in-time cost-effective decision-making will be discussed through the implementation of surrogate models. Finally, the audience will be introduced to a real-time application of in-time trajectory planning of an unmanned aerial system (UAS) based on its PHM assessments under uncertain and varying wind conditions.

decision-making↗
NASA NTRS2013

Model-Based Diagnosis and Prognosis of a Water Recycling System

A water recycling system (WRS) deployed at NASA Ames Research Center s Sustainability Base (an energy efficient office building that integrates some novel technologies developed for space applications) will serve as a testbed for long duration testing of next generation spacecraft water recycling systems for future human spaceflight missions. This system cleans graywater (waste water collected from sinks and showers) and recycles it into clean water. Like all engineered systems, the WRS is prone to standard degradation due to regular use, as well as other faults. Diagnostic and prognostic applications will be deployed on the WRS to ensure its safe, efficient, and correct operation. The diagnostic and prognostic results can be used to enable condition-based maintenance to avoid unplanned outages, and perhaps extend the useful life of the WRS. Diagnosis involves detecting when a fault occurs, isolating the root cause of the fault, and identifying the extent of damage. Prognosis involves predicting when the system will reach its end of life irrespective of whether an abnormal condition is present or not. In this paper, first, we develop a physics model of both nominal and faulty system behavior of the WRS. Then, we apply an integrated model-based diagnosis and prognosis framework to the simulation model of the WRS for several different fault scenarios to detect, isolate, and identify faults, and predict the end of life in each fault scenario, and present the experimental results.

Roychoudhury, Indranil↗
NASA NTRSDate not supplied

Model Based Approaches for Fault Detection, Prognostics, Decision Making in Complex Systems

The presentation discusses application of model based approaches to complex systems. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage.

Physics Informed↗
NASA NTRS2009

Solder Joint Health Monitoring Testbed

A method of monitoring the health of selected solder joints, called SJ-BIST, has been developed by Ridgetop Group Inc. under a Small Business Innovative Research (SBIR) contract. The primary goal of this research program is to test and validate this method in a flight environment using realistically seeded faults in selected solder joints. An additional objective is to gather environmental data for future development of physics-based and data-driven prognostics algorithms. A test board is being designed using a Xilinx FPGA. These boards will be tested both in flight and on the ground using a shaker table and an altitude chamber.

Delaney, Michael M.↗
NASA NTRS1989

SSME fault monitoring and diagnosis expert system

An expert system, called LEADER, has been designed and implemented for automatic learning, detection, identification, verification, and correction of anomalous propulsion system operations in real time. LEADER employs a set of sensors to monitor engine component performance and to detect, identify, and validate abnormalities with respect to varying engine dynamics and behavior. Two diagnostic approaches are adopted in the architecture of LEADER. In the first approach fault diagnosis is performed through learning and identifying engine behavior patterns. LEADER, utilizing this approach, generates few hypotheses about the possible abnormalities. These hypotheses are then validated based on the SSME design and functional knowledge. The second approach directs the processing of engine sensory data and performs reasoning based on the SSME design, functional knowledge, and the deep-level knowledge, i.e., the first principles (physics and mechanics) of SSME subsystems and components. This paper describes LEADER's architecture which integrates a design based reasoning approach with neural network-based fault pattern matching techniques. The fault diagnosis results obtained through the analyses of SSME ground test data are presented and discussed.

Ali, Moonis↗
NASA NTRS2008

Spaceborne Processor Array

A Spaceborne Processor Array in Multifunctional Structure (SPAMS) can lower the total mass of the electronic and structural overhead of spacecraft, resulting in reduced launch costs, while increasing the science return through dynamic onboard computing. SPAMS integrates the multifunctional structure (MFS) and the Gilgamesh Memory, Intelligence, and Network Device (MIND) multi-core in-memory computer architecture into a single-system super-architecture. This transforms every inch of a spacecraft into a sharable, interconnected, smart computing element to increase computing performance while simultaneously reducing mass. The MIND in-memory architecture provides a foundation for high-performance, low-power, and fault-tolerant computing. The MIND chip has an internal structure that includes memory, processing, and communication functionality. The Gilgamesh is a scalable system comprising multiple MIND chips interconnected to operate as a single, tightly coupled, parallel computer. The array of MIND components shares a global, virtual name space for program variables and tasks that are allocated at run time to the distributed physical memory and processing resources. Individual processor- memory nodes can be activated or powered down at run time to provide active power management and to configure around faults. A SPAMS system is comprised of a distributed Gilgamesh array built into MFS, interfaces into instrument and communication subsystems, a mass storage interface, and a radiation-hardened flight computer.

Chow, Edward T.↗
NASA NTRS1992

An architecture for the development of real-time fault diagnosis systems using model-based reasoning

Presented here is an architecture for implementing real-time telemetry based diagnostic systems using model-based reasoning. First, we describe Paragon, a knowledge acquisition tool for offline entry and validation of physical system models. Paragon provides domain experts with a structured editing capability to capture the physical component's structure, behavior, and causal relationships. We next describe the architecture of the run time diagnostic system. The diagnostic system, written entirely in Ada, uses the behavioral model developed offline by Paragon to simulate expected component states as reflected in the telemetry stream. The diagnostic algorithm traces causal relationships contained within the model to isolate system faults. Since the diagnostic process relies exclusively on the behavioral model and is implemented without the use of heuristic rules, it can be used to isolate unpredicted faults in a wide variety of systems. Finally, we discuss the implementation of a prototype system constructed using this technique for diagnosing faults in a science instrument. The prototype demonstrates the use of model-based reasoning to develop maintainable systems with greater diagnostic capabilities at a lower cost.

Hall, Gardiner A.↗
NASA NTRS2013

Goal-Function Tree Modeling for Systems Engineering and Fault Management

This paper describes a new representation that enables rigorous definition and decomposition of both nominal and off-nominal system goals and functions: the Goal-Function Tree (GFT). GFTs extend the concept and process of functional decomposition, utilizing state variables as a key mechanism to ensure physical and logical consistency and completeness of the decomposition of goals (requirements) and functions, and enabling full and complete traceabilitiy to the design. The GFT also provides for means to define and represent off-nominal goals and functions that are activated when the system's nominal goals are not met. The physical accuracy of the GFT, and its ability to represent both nominal and off-nominal goals enable the GFT to be used for various analyses of the system, including assessments of the completeness and traceability of system goals and functions, the coverage of fault management failure detections, and definition of system failure scenarios.

Johnson, Stephen B.↗