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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

Sleep Disruption Medical Intervention Forecasting (SDMIF) Module for the Integrated Medical Model

The NASA Integrated Medical Model (IMM) assesses the risk, including likelihood and impact of occurrence, of all credible in-flight medical conditions. Fatigue due to sleep disruption is a condition that could lead to operational errors, potentially resulting in loss of mission or crew. Pharmacological consumables are mitigation strategies used to manage the risks associated with sleep deficits. The likelihood of medical intervention due to sleep disruption was estimated with a well validated sleep model and a Monte Carlo computer simulation in an effort to optimize the quantity of consumables. METHODS: The key components of the model are the mission parameter program, the calculation of sleep intensity and the diagnosis and decision module. The mission parameter program was used to create simulated daily sleep/wake schedules for an ISS increment. The hypothetical schedules included critical events such as dockings and extravehicular activities and included actual sleep time and sleep quality. The schedules were used as inputs to the Sleep, Activity, Fatigue and Task Effectiveness (SAFTE) Model (IBR Inc., Baltimore MD), which calculated sleep intensity. Sleep data from an ISS study was used to relate calculated sleep intensity to the probability of sleep medication use, using a generalized linear model for binomial regression. A human yes/no decision process using a binomial random number was also factored into sleep medication use probability. RESULTS: These probability calculations were repeated 5000 times resulting in an estimate of the most likely amount of sleep aids used during an ISS mission and a 95% confidence interval. CONCLUSIONS: These results were transferred to the parent IMM for further weighting and integration with other medical conditions, to help inform operational decisions. This model is a potential planning tool for ensuring adequate sleep during sleep disrupted periods of a mission.

Lewandowski, Beth↗
NASA NTRS2008

System for the diagnosis and monitoring of coronary artery disease, acute coronary syndromes, cardiomyopathy and other cardiac conditions

Cardiac electrical data are received from a patient, manipulated to determine various useful aspects of the ECG signal, and displayed and stored in a useful form using a computer. The computer monitor displays various useful information, and in particular graphically displays various permutations of reduced amplitude zones and kurtosis that increase the rapidity and accuracy of cardiac diagnoses. New criteria for reduced amplitude zones are defined that enhance the sensitivity and specificity for detecting cardiac abnormalities.

Schlegel, Todd T.↗
NASA NTRS1971

Logic design for dynamic and interactive recovery.

Recovery in a fault-tolerant computer means the continuation of system operation with data integrity after an error occurs. This paper delineates two parallel concepts embodied in the hardware and software functions required for recovery; detection, diagnosis, and reconfiguration for hardware, data integrity, checkpointing, and restart for the software. The hardware relies on the recovery variable set, checking circuits, and diagnostics, and the software relies on the recovery information set, audit, and reconstruct routines, to characterize the system state and assist in recovery when required. Of particular utility is a handware unit, the recovery control unit, which serves as an interface between error detection and software recovery programs in the supervisor and provides dynamic interactive recovery.

Carter, W. C.↗
NASA NTRS1989

Knowledge based and interactive control for the Superfluid Helium On-orbit Transfer Project

NASA's Superfluid Helium On-Orbit Transfer (SHOOT) project is a Shuttle-based experiment designed to acquire data on the properties of superfluid helium in micro-gravity. Aft Flight Deck Computer Software for the SHOOT experiment is comprised of several monitoring programs which give the astronaut crew visibility into SHOOT systems and a rule based system which will provide process control, diagnosis and error recovery for a helium transfer without ground intervention. Given present Shuttle manifests, this software will become the first expert system to be used in space. The SHOOT Command and Monitoring System (CMS) software will provide a near real time highly interactive interface for the SHOOT principal investigator to control the experiment and to analyze and display its telemetry. The CMS software is targeted for all phases of the SHOOT project: hardware development, pre-flight pad servicing, in-flight operations, and post-flight data analysis.

Castellano, Timothy P.↗
NASA NTRS1998

Heart rate dynamics in patients with stable angina pectoris and utility of fractal and complexity measures

Dynamic analysis techniques may uncover abnormalities in heart rate (HR) behavior that are not easily detectable with conventional statistical measures. However, the applicability of these new methods for detecting possible abnormalities in HR behavior in various cardiovascular disorders is not well established. Conventional measures of HR variability were compared with short-term (< or = 11 beats, alpha1) and long-term (> 11 beats, alpha2) fractal correlation properties and with approximate entropy of RR interval data in 38 patients with stable angina pectoris without previous myocardial infarction or cardiac medication at the time of the study and 38 age-matched healthy controls. The short- and long-term fractal scaling exponents (alpha1, alpha2) were significantly higher in the coronary patients than in the healthy controls (1.34 +/- 0.15 vs 1.11 +/- 0.12 [p <0.001] and 1.10 +/- 0.08 vs 1.04 +/- 0.06 [p <0.01], respectively), and they also had lower approximate entropy (p <0.05), standard deviation of all RR intervals (p <0.01), and high-frequency spectral component of HR variability (p <0.05). The short-term fractal scaling exponent performed better than other heart rate variability parameters in differentiating patients with coronary artery disease from healthy subjects, but it was not related to the clinical or angiographic severity of coronary artery disease or any single nonspectral or spectral measure of HR variability in this retrospective study. Patients with stable angina pectoris have altered fractal properties and reduced complexity in their RR interval dynamics relative to age-matched healthy subjects. Dynamic analysis may complement traditional analyses in detecting altered HR behavior in patients with stable angina pectoris.

NASA Discipline Cardiopulmonary↗
NASA NTRS1984

Computer Analysis of Eye Blood-Vessel Images

Technique rapidly diagnoses diabetes mellitus. Photographs of "whites" of patients' eyes scanned by computerized image analyzer programmed to quantify density of small blood vessels in conjuctiva. Comparison with data base of known normal and diabetic patients facilitates rapid diagnosis.

Wall, R. J.↗
DOE OSTI2021

Performance Results for Sensor Assignment Problem as Solved on a Multi-Node Cluster

An earlier report described a procedure for optimal sensor set selection and its implementation on a computational cluster. This new and innovative capability was developed to facilitate a reduction in operations staffing levels to improve plant economics. By automating surveillance and maintenance tasks through early detection of degrading sensors and equipment, staff can be more efficiently deployed. The method uses automated reasoning and domain knowledge in the form of the conservation equations to infer from plant measurements the state of equipment health. Inclusion of domain knowledge addresses the problem that exists with pure data-driven methods that there are no rigorous guidelines for determining what constitutes an adequate sensor set. Formalizing the procedure for sensor set selection as we have done results in a more reliable and explainable diagnosis of plant equipment health. Importantly, from the standpoint of the plant owner, personnel are provided with an early and explicit diagnosis of an equipment problem. That in principle automates the process and eliminates having to send personnel into the plant to find the cause as typically occurs when a data-driven method detects an anomaly. In this report we describe first results obtained using a computational cluster to solve the sensor set selection problem as framed above. The case described addresses the problem of equipment health monitoring in the high-pressure (HP) feedwater system of a pressurized light water reactor as seen through the eyes of our collaborating utility partner. Maintenance of this system can amount to millions of dollars per year if equipment health issues go undiagnosed and lead to loss of function. On examining the potential that is inherent in the installed sensor set for diagnosing equipment health degradation, it was found that greater fault resolution capability can be achieved using a sensor set that is 20 percent fewer in number. The take-away is that compared to the installed sensor set there exists a more strategic assignment of sensors that will furnish better health monitoring capability and with fewer sensors. Where the problem defies solution by manual inspection, as is the case here, one can be found by an algorithm. The solution was obtained in four hours using 30 computational cores. The HP feedwater problem as posed above illustrates the added value of approaching the sensor selection problem as one amenable to algorithmic solution. This problem is of interest to advanced reactor designers and to utilities that are setting up remote monitoring and diagnostic centers.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1992

Onboard System Processes SAR Data

Aircraft Flight Correlator (AFC) computing system, mounted with AIRSAR synthetic-aperture-radar (SAR) system, supplements recording and postflight analysis, processing portion of data in real or nearly real time to provide imagery for rapid evaluation. Facilitates diagnosis of SAR equipment or adjustment of parameters of experiment. Operates in two modes: "quick-look" mode, data processed in 10 minutes into high-resolution image; and "real-time" mode, data processed in real time to reduced resolution. Also used as aid to navigation.

Carande, Richard E.↗
DOE OSTI2025

Explainable AI for Multivariate Time Series Pattern Exploration: Latent Space Visual Analytics With Temporal Fusion Transformer and Variational Autoencoders in Power Grid Event Diagnosis

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

Explainable AI↗
NASA NTRS2004

Real-Time 12-Lead High-Frequency QRS Electrocardiography for Enhanced Detection of Myocardial Ischemia and Coronary Artery Disease

Several studies have shown that diminution of the high-frequency (HF; 150-250 Hz) components present within the central portion of the QRS complex of an electrocardiogram (ECG) is a more sensitive indicator for the presence of myocardial ischemia than are changes in the ST segments of the conventional low-frequency ECG. However, until now, no device has been capable of displaying, in real time on a beat-to-beat basis, changes in these HF QRS ECG components in a continuously monitored patient. Although several software programs have been designed to acquire the HF components over the entire QRS interval, such programs have involved laborious off-line calculations and postprocessing, limiting their clinical utility. We describe a personal computer-based ECG software program developed recently at the National Aeronautics and Space Administration (NASA) that acquires, analyzes, and displays HF QRS components in each of the 12 conventional ECG leads in real time. The system also updates these signals and their related derived parameters in real time on a beat-to-beat basis for any chosen monitoring period and simultaneously displays the diagnostic information from the conventional (low-frequency) 12-lead ECG. The real-time NASA HF QRS ECG software is being evaluated currently in multiple clinical settings in North America. We describe its potential usefulness in the diagnosis of myocardial ischemia and coronary artery disease.

Software↗
DOE OSTI2023

Multi-fidelity physics-informed machine learning for probabilistic damage diagnosis

Machine learning (ML) models are gaining popularity in structural health monitoring (SHM) because of their ability to learn the complex relationship between damage and sensor data. However, the lack of sufficient experimental data for structures with different degrees of damage is a key problem in training ML models for SHM. This problem can be alleviated by using physics-based models to generate the required training data to build physics-informed ML (PIML) models for SHM. However, it takes significant computational effort to perform enough high-fidelity simulations of the diagnostic test. It is thus desirable to know whether the available computational resource budget should be expended on numerous low-fidelity physics simulations, or a small number of high-fidelity simulations, or their combination. In this paper, we investigate this aspect of generating adequate training data for PIML, by constructing multi-fidelity PIML models. We evaluate the performance of several PIML models, trained with different amounts of low-fidelity and high-fidelity data, in locating hidden cracks in concrete structures using a nonlinear dynamics-based diagnosis technique. Here, we find that high-fidelity physics simulations that do not cover the (test and damage) parameter space do not improve the performance of diagnostic PIML models built using data from many low-fidelity physics simulations.

42 ENGINEERING↗
NASA NTRS1985

Diagnosis of vertical motions from VAS retrievals during a convective outbreak

GOES-VAS satellite retrievals are used to investigate an intense convective outbreak over the Mississippi River Valley on 21-22 July 1982. The primary goals are to assess the strengths and weaknesses of three methods for computing vertical motion using satellite retrievals and to determine the effects of short interval observations on the calculations. Then, the vertical motions are incorporated with thermodynamic parameters to assess the usefulness of VAS data in delineating factors leading to storm formation. Results indicate that the quasi-geotrophic omega equation provided patterns and magnitudes most consistent with observed weather events and the 12 h radiosonde-derived motions. The vorticity method generally produced reasonable patterns, especially over the convective outbreak, although magnitudes were large due to its time derivative.

Funk, T. W.↗
NASA NTRS2007

POD Model Reconstruction for Gray-Box Fault Detection

Proper orthogonal decomposition (POD) is the mathematical basis of a method of constructing low-order mathematical models for the "gray-box" fault-detection algorithm that is a component of a diagnostic system known as beacon-based exception analysis for multi-missions (BEAM). POD has been successfully applied in reducing computational complexity by generating simple models that can be used for control and simulation for complex systems such as fluid flows. In the present application to BEAM, POD brings the same benefits to automated diagnosis. BEAM is a method of real-time or offline, automated diagnosis of a complex dynamic system.The gray-box approach makes it possible to utilize incomplete or approximate knowledge of the dynamics of the system that one seeks to diagnose. In the gray-box approach, a deterministic model of the system is used to filter a time series of system sensor data to remove the deterministic components of the time series from further examination. What is left after the filtering operation is a time series of residual quantities that represent the unknown (or at least unmodeled) aspects of the behavior of the system. Stochastic modeling techniques are then applied to the residual time series. The procedure for detecting abnormal behavior of the system then becomes one of looking for statistical differences between the residual time series and the predictions of the stochastic model.

Park, Han↗
NASA NTRS2004

Verifying Diagnostic Software

Livingstone PathFinder (LPF) is a simulation-based computer program for verifying autonomous diagnostic software. LPF is designed especially to be applied to NASA s Livingstone computer program, which implements a qualitative-model-based algorithm that diagnoses faults in a complex automated system (e.g., an exploratory robot, spacecraft, or aircraft). LPF forms a software test bed containing a Livingstone diagnosis engine, embedded in a simulated operating environment consisting of a simulator of the system to be diagnosed by Livingstone and a driver program that issues commands and faults according to a nondeterministic scenario provided by the user. LPF runs the test bed through all executions allowed by the scenario, checking for various selectable error conditions after each step. All components of the test bed are instrumented, so that execution can be single-stepped both backward and forward. The architecture of LPF is modular and includes generic interfaces to facilitate substitution of alternative versions of its different parts. Altogether, LPF provides a flexible, extensible framework for simulation-based analysis of diagnostic software; these characteristics also render it amenable to application to diagnostic programs other than Livingstone.

Lindsey, Tony↗
NASA NTRS1975

Cardiovascular imaging and image processing: Theory and practice - 1975; Proceedings of the Conference, Stanford University, Stanford, Calif., July 10-12, 1975

The present collection of papers outlines advances in ultrasonography, scintigraphy, and commercialization of medical technology as applied to cardiovascular diagnosis in research and clinical practice. Particular attention is given to instrumentation, image processing and display. As necessary concomitants to mathematical analysis, recently improved magnetic recording methods using tape or disks and high-speed computers of large capacity are coming into use. Major topics include Doppler ultrasonic techniques, high-speed cineradiography, three-dimensional imaging of the myocardium with isotopes, sector-scanning echocardiography, and commercialization of the echocardioscope. Individual items are announced in this issue.

Harrison, D. C.↗
NASA NTRS1993

Anomalous event diagnosis for environmental satellite systems

The National Oceanic and Atmospheric Administration's (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) is responsible for the operation of the NOAA geostationary and polar orbiting satellites. NESDIS provides a wide array of operational meteorological and oceanographic products and services and operates various computer and communication systems on a 24-hour, seven days per week schedule. The Anomaly Reporting System contains a database of anomalous events regarding the operations of the Geostationary Operational Environmental Satellite (GOES), communication, or computer systems that have degraded or caused the loss of GOES imagery. Data is currently entered manually via an automated query user interface. There are 21 possible symptoms (e.g., No Data), and 73 possible causes (e.g., Sectorizer - World Weather Building) of an anomalous event. The determination of an event's cause(s) is made by the on-duty computer operator, who enters the event in a paper based daily log, and by the analyst entering the data into the reporting system. The determination of the event's cause(s) impacts both the operational status of these systems, and the performance evaluation of the on-site computer and communication operations contractor.

Ramsay, Bruce H.↗
DOE OSTI2022

Exploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants

This report presents the project Idaho National Laboratory conducted for Nuclear Regulatory Commission to explore the advanced computational tools and techniques, such as artificial intelligence (AI) and machine learning (ML), for operating nuclear plants. The report reviews the nuclear data sources, with the focus on the operating experience data, that could be applied by advanced computational tools and techniques. Plant-specific and generic (national and international) data from different sources are described. The report describes the relationships between statistics and AI/ML and then introduces the most widely used AI/ML algorithms in both supervised and unsupervised learning. The report reviews the recent applications of advanced computational tools and techniques in various fields of nuclear industry, such as reactor system design and analysis, plant operation and maintenance, and nuclear safety and risk analysis. Finally, the report presents the insights from the project on the potential applicability of AI/ML techniques in improving advanced computational capabilities, how the advanced tools and techniques could contribute to the understanding of safety and risk, and what information would be needed to provide meaningful insights to decision makers. The report also documents an NRC survey on the current state of commercial nuclear power operations relative to the use of AI and ML tools as well as the role of AI/ML tools in nuclear power operations was published by the NRC as in FRN NRC-2021-0048 in April 2021. A summary of the survey including the survey questions, survey participants, survey responses, and the conclusions and insights derived from the survey is provided in the report. Finally, the report investigates potential applications of using AI/ML in operating NPPs and advanced reactors (both advanced LWRs and advanced NLWRs) to improve nuclear plant safety and efficiency. Three main application fields are defined and discussed: (1) plant safety and security assessments; (2) plant degradation modeling, fault and accident diagnosis and prognosis; and (3) plant operation and maintenance efficiency improvement.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗