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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Faultfinder: A diagnostic expert system with graceful degradation for onboard aircraft applications

A research effort was conducted to explore the application of artificial intelligence technology to automation of fault monitoring and diagnosis as an aid to the flight crew. Human diagnostic reasoning was analyzed and actual accident and incident cases were reconstructed. Based on this analysis and reconstruction, diagnostic concepts were conceived and implemented for an aircraft's engine and hydraulic subsystems. These concepts are embedded within a multistage approach to diagnosis that reasons about time-based, causal, and qualitative information, and enables a certain amount of graceful degradation. The diagnostic concepts are implemented in a computer program called Faultfinder that serves as a research prototype.

Abbott, Kathy H.↗
NASA NTRS1988

The role of radiation-dynamics interaction in regional numerical weather prediction

The role of radiation-dynamics interaction in regional numerical weather prediction of severe storm environment and mesoscale convective systems over the United States is researched. Based upon the earlier numerical model simulation experiments, it is believed that such interaction can have a profound impact on the dynamics and thermodynamics of regional weather systems. The research will be carried out using real-data model forecast experiments performed on the Cray-X/MP computer. The forecasting system to be used is a comprehensive mesoscale prediction system which includes analysis and initialization, the dynamic model, and the post-forecast diagnosis codes. The model physics are currently undergoing many improvements in parameterizing radiation processes in the model atmosphere. The forecast experiments in conjunction with in-depth model verification and diagnosis are aimed at a quantitative understanding of the interaction between atmospheric radiation and regional dynamical processes in mesoscale models as well as in nature. Thus, significant advances in regional numerical weather prediction can be made. Results shall also provide valuable information for observational designs in the area of remote sensing techniques to study the characteristics of air-land thermal interaction and moist processes under various atmospheric conditions.

Chang, Chia-Bo↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Facing page test for the astronaut science advisor presentation

The goal of the Astronaut Science Advisor (ASA) project is to improve the scientific return of experiments performed in space by providing astronaut experimenters with an 'intelligent assistant' that encapsulates much of the domain- and experiment-related knowledge commanded by the Principal Investigator (PI) on the ground. By using expert systems technology and the availability of flight-qualified personal computers, it is possible to encode the requisite knowledge and make it available to astronauts as they perform experiments in space. The system performs four major functions: diagnosis and troubleshooting of experiment apparatus, data collection, protocol management, and detection of interesting data. The experiment used for development of the system measures human adaptation to weightlessness in the context of the neurovestibular system. This so-called 'Rotating Dome' experiment was flown on the recent Spacelab Life Sciences One (SLS-1) Mission. This mission was used as an opportunity to test some of the system's functionality. Experiment data was downlinked from the orbiter, and the system then captured the data and analyzed it in real time. The system kept track of the time being used by the experiment, recognized occurrences of interesting data, summarized data statistically and generated potential new protocols that could be used to optimize the course of the experiment.

Compton, Michael M.↗
NASA NTRS2008

Using Bayesian Networks for Candidate Generation in Consistency-based Diagnosis

Consistency-based diagnosis relies heavily on the assumption that discrepancies between model predictions and sensor observations can be detected accurately. When sources of uncertainty like sensor noise and model abstraction exist robust schemes have to be designed to make a binary decision on whether predictions are consistent with observations. This risks the occurrence of false alarms and missed alarms when an erroneous decision is made. Moreover when multiple sensors (with differing sensing properties) are available the degree of match between predictions and observations can be used to guide the search for fault candidates. In this paper we propose a novel approach to handle this problem using Bayesian networks. In the consistency- based diagnosis formulation, automatically generated Bayesian networks are used to encode a probabilistic measure of fit between predictions and observations. A Bayesian network inference algorithm is used to compute most probable fault candidates.

Narasimhan, Sriram↗
NASA NTRS1996

Telemedicine: An Application in Search of Users

Telemedicine involves the use of telecommunication technologies as a medium for the provision of medical information and services to consumers at sites that are at a distance from the provider. The concept encompasses everything from the telephone system to high-speed, wide-bandwidth transmission with use of fiberoptics, satellites, or a combination of terrestrial and satellite-communication technologies. The peripheral software could be as simple as a typewriter used to type a letter requesting an opinion or as complex as high-capacity parallel processing computers and imaging devices. Although the definition includes telephone, facsimile, and distance learning, the term "Telemedicine" is currently used as a generic label for remote consultation and diagnosis. Telemedicine is not a medical subspecialty but a facilitator of all medical and surgical specialties.

Khandheria, Bijoy K.↗
NASA NTRS2016

Probabilistic Prognosis of Non-Planar Fatigue Crack Growth

Quantifying the uncertainty in model parameters for the purpose of damage prognosis can be accomplished utilizing Bayesian inference and damage diagnosis data from sources such as non-destructive evaluation or structural health monitoring. The number of samples required to solve the Bayesian inverse problem through common sampling techniques (e.g., Markov chain Monte Carlo) renders high-fidelity finite element-based damage growth models unusable due to prohibitive computation times. However, these types of models are often the only option when attempting to model complex damage growth in real-world structures. Here, a recently developed high-fidelity crack growth model is used which, when compared to finite element-based modeling, has demonstrated reductions in computation times of three orders of magnitude through the use of surrogate models and machine learning. The model is flexible in that only the expensive computation of the crack driving forces is replaced by the surrogate models, leaving the remaining parameters accessible for uncertainty quantification. A probabilistic prognosis framework incorporating this model is developed and demonstrated for non-planar crack growth in a modified, edge-notched, aluminum tensile specimen. Predictions of remaining useful life are made over time for five updates of the damage diagnosis data, and prognostic metrics are utilized to evaluate the performance of the prognostic framework. Challenges specific to the probabilistic prognosis of non-planar fatigue crack growth are highlighted and discussed in the context of the experimental results.

Leser, Patrick E.↗
DOE OSTI2023

Direct Bayesian inference for fault severity assessment in Digital-Twin-Based fault diagnosis

For applications in condition-based maintenance of nuclear systems, the assessment of fault severity is crucial. In this work, we developed a framework that allows for direct inference of the probability distributions of possible faults in a system. Here, we employed a model-based approach with model residuals generated from analytical redundancy relations provided by physics-based models of the system components. From real-time sensor readings, the values of the model residuals can be calculated, and the posterior probability distributions of the faults can be computed directly using the methods of Bayesian networks. From the posterior distribution of each fault, one can estimate the fault probability based on a chosen threshold and assess the severity of the fault. By eliminating the discretization and simplifications in middle steps, this approach allows us to leverage the available computational resources to provide more accurate fault probability estimates and severity assessments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS2005

Foreign Object Damage Identification in Turbine Engines

This report summarizes the collective work of a five-person team from different organizations examining the problem of detecting foreign object damage (FOD) events in turbofan engines from gas path thermodynamic and bearing accelerometer sensors, and determining the severity of damage to each component (diagnosis). Several detection and diagnostic approaches were investigated and a software tool (FODID) was developed to assist researchers detect/diagnose FOD events. These approaches include (1) fan efficiency deviation computed from upstream and downstream temperature/ pressure measurements, (2) gas path weighted least squares estimation of component health parameter deficiencies, (3) Kalman filter estimation of component health parameters, and (4) use of structural vibration signal processing to detect both large and small FOD events. The last three of these approaches require a significant amount of computation in conjunction with a physics-based analytic model of the underlying phenomenon the NPSS thermodynamic cycle code for approaches 1 to 3 and the DyRoBeS reduced-order rotor dynamics code for approach 4. A potential application of the FODID software tool, in addition to its detection/diagnosis role, is using its sensitivity results to help identify the best types of sensors and their optimum locations within the gas path, and similarly for bearing accelerometers.

Strack, William↗
NASA NTRS2016

The OMICS of Sports & Space: How Genomics is Transforming Both Fields

Join top 10 New York Times Bestseller “The Sports Gene” author David Epstein and NASA Twins Study investigator Christopher E. Mason, Ph.D., in the debate as old as physical competition—nature versus nurture. From personal experience, Epstein tackles the great debate and traces how far science has come in solving this timeless riddle, and how genetics has entered into the field of sports. He’s an investigative science reporter for ProPublica and longtime contributor to Sports Illustrated. Epstein will share insights into performance-enhancing drugs, the lucky genetics that separate a professional athlete from a less talented athlete, and his research into the death of a friend with Hypertrophic Cardiomyopathy (HCM).From an epigenomic viewpoint, Mason examines the benefits and risks for astronauts who face extreme spaceflight conditions and what it means for the future of human space travel. He is an associate professor in the Department of Physiology and Biophysics, The Feil Family Brain and Mind Research Institute (BMRI) & The Institute for Computational Biomedicine at Weill Cornell Medicine. He is also part of the Tri-Institutional Program on Computational Biology and a Medicine Fellow of Genomics, Ethics, and Law in the Information Society Project at Yale Law School.The study of omics shows tremendous potential in prevention, diagnosis and treatment of injuries and diseases but genetic discrimination and molecular privacy concerns are raised in both sports and space.

Reeves, Katherine↗
NASA NTRS2008

Efficient Probabilistic Diagnostics for Electrical Power Systems

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

Mengshoel, Ole J.↗
DOE OSTI2022

Fast Evaluation of Viral Emerging Risks (FEVER): A computational tool for biosurveillance, diagnostics, and mutation typing of emerging viral pathogens

Viral pathogens can rapidly evolve, adapt to novel hosts, and evade human immunity. The early detection of emerging viral pathogens through biosurveillance coupled with rapid and accurate diagnostics are required to mitigate global pandemics. However, RNA viruses can mutate rapidly, hampering biosurveillance and diagnostic efforts. Here, we present a novel computational approach called FEVER (Fast Evaluation of Viral Emerging Risks) to design assays that simultaneously accomplish: 1) broad-coverage biosurveillance of an entire group of viruses, 2) accurate diagnosis of an outbreak strain, and 3) mutation typing to detect variants of public health importance. We demonstrate the application of FEVER to generate assays to simultaneously 1) detect sarbecoviruses for biosurveillance; 2) diagnose infections specifically caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); and 3) perform rapid mutation typing of the D614G SARS-CoV-2 spike variant associated with increased pathogen transmissibility. These FEVER assays had a high in silico recall (predicted positive) up to 99.7% of 525,708 SARS-CoV-2 sequences analyzed and displayed sensitivities and specificities as high as 92.4% and 100% respectively when validated in 100 clinical samples. The D614G SARS-CoV-2 spike mutation PCR test was able to identify the single nucleotide identity at position 23,403 in the viral genome of 96.6% SARS-CoV-2 positive samples without the need for sequencing. This study demonstrates the utility of FEVER to design assays for biosurveillance, diagnostics, and mutation typing to rapidly detect, track, and mitigate future outbreaks and pandemics caused by emerging viruses.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

A route generator concept for aircraft onboard fault monitoring

Because of the increasingly complex environments in which the flight crews of commercial aviation aircraft must operate, a research effort is currently underway at NASA Langley Research Center to investigate the potential benefits of intelligent cockpit aids, and to establish guidelines for the application of artificial intelligence techniques to advanced flight management concepts. The segment of this research area that concentrates on automated fault monitoring and diagnosis requires that a reference frame exist, against which the current state of the aircraft may be compared to determine the existence of a fault. This paper describes a computer program which generates the position of that reference frame that specifies the horizontal flight route.

Palmer, M. T.↗
NASA NTRS1990

Software For Development Of Expert Systems

Modular software system helps create efficient artificial-intelligence computer programs. STAR*TOOL system: Set of high-level software tools; assists programmers in creation of efficient knowledge-based software systems. Provides language and capabilities for compilation of application programs written in Common LISP. Features modularity enabling elimination of unnecessary capabilities from final application program and achieves greater computing performance. Runs on any computer that supports Common LISP and has sufficient memory. Provides programmer with necessary software tools to build wide variety of reasoning and inference engines for such applications as planning, diagnosis and analysis, and simulation.

James, Mark L.↗
NASA NTRS1980

Automated quantitative muscle biopsy analysis system

An automated system to aid the diagnosis of neuromuscular diseases by producing fiber size histograms utilizing histochemically stained muscle biopsy tissue. Televised images of the microscopic fibers are processed electronically by a multi-microprocessor computer, which isolates, measures, and classifies the fibers and displays the fiber size distribution. The architecture of the multi-microprocessor computer, which is iterated to any required degree of complexity, features a series of individual microprocessors P.sub.n each receiving data from a shared memory M.sub.n-1 and outputing processed data to a separate shared memory M.sub.n+1 under control of a program stored in dedicated memory M.sub.n.

Castleman, Kenneth R.↗
NASA NTRS1990

Tropical Pacific moisture variability

The objectives are to describe synoptic scale variability of moisture over the tropical Pacific Ocean and the systems leading to this variability; implement satellite analysis procedures in support of this effort, and to incorporate additional satellite information into operational analysis forecast systems at the National Meteorological Center (NMC). Composite satellite radiance patterns describe features detectable well before the development of synoptic scale tropical plumes. These typical features were extracted from historical files of Tiros Operational Vertical Sounder (TOVS) radiance observations for a pair of tropical plumes which developed during January 1989. Signals were inserted into the NMC operational medium range forecast model and a suite of model integrations were conducted. Many of the 48 h model errors of the historical forecasts were eliminated by the inclusion of more complete satellite observations. Three studies in satellite radiance analysis progressed. An analysis which blended TOVS moisture channels, OLR observations and European Center for Medium Weather Forecasts (ECMWF) model analysis to generate fields of total precipitable water comparable to those estimated from Scanning Multichannel Microwave Radiometer (SMMR) mu-wave observations. This study demonstrated that a 10 y climatology of precipitable water over the oceans is feasible, using available infrared observations (OLR and TOVS) and model analysis (ECMWF, NMC or similar quality). The estimates are sensitive to model quality and the estimating model must be updated with operational model changes. Coe developed a set of tropical plume and ITCZ composites from TOVS observations, and from NMC and ECMWF analyses which had been passed through a radiative transfer model to simulate TOVS radiances. The composites have been completed as well as many statistical diagnostics of individual TOVS channels. Analysis of the computations is commencing. Chung has initiated a study of the differences between TOVS observed vapor structure during El Nino Southern Oscillation (ENSO) (1983) and non-ENSO (1984) years. Preliminary diagnosis demonstrates gross moisture changes between warm and cold sea surface temperature episodes.

Mcguirk, James P.↗
NASA NTRS1994

Decision-Tree Program

IND computer program introduces Bayesian and Markov/maximum-likelihood (MML) methods and more-sophisticated methods of searching in growing trees. Produces more-accurate class-probability estimates important in applications like diagnosis. Provides range of features and styles with convenience for casual user, fine-tuning for advanced user or for those interested in research. Consists of four basic kinds of routines: data-manipulation, tree-generation, tree-testing, and tree-display. Written in C language.

Buntine, Wray↗
NASA NTRS2008

Implementing a Digital Phasemeter in an FPGA

Firmware for implementing a digital phasemeter within a field-programmable gate array (FPGA) has been devised. In the original application of this firmware, the phase that one seeks to measure is the difference between the phases of two nominally-equal-frequency heterodyne signals generated by two interferometers. In that application, zero-crossing detectors convert the heterodyne signals to trains of rectangular pulses, the two pulse trains are fed to a fringe counter (the major part of the phasemeter) controlled by a clock signal having a frequency greater than the heterodyne frequency, and the fringe counter computes a time-averaged estimate of the difference between the phases of the two pulse trains. The firmware also does the following: Causes the FPGA to compute the frequencies of the input signals; Causes the FPGA to implement an Ethernet (or equivalent) transmitter for readout of phase and frequency values; and Provides data for use in diagnosis of communication failures. The readout rate can be set, by programming, to a value between 250 Hz and 1 kHz. Network addresses can be programmed by the user.

Rao, Shanti R.↗