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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Diagnosing Ghost Bunches with the Upstream Extinction Monitor in the Mu2e Experiment

The Mu2e experiment has a stringent requirement for extinction of the pulsed proton beam, referring to the elimination of particles between proton bunches to a relative level of 10$^-10$, which means a single out-of-time particle in the inter-pulse gaps for every 250 complete proton pulses. As the construction of the Mu2e experiment nears completion, it is crucially important to make an early measurement of the beam extinction in its current condition. Hence the upstream extinction monitor was constructed and operated to probe for problems in the proton pulse structure or a higher than expected incidence rate of out-of-time particles. The analysis in this work comes from data taken in March 2026. The long data run showed a significant presence of out-of-time particles from ghost bunches in the Delivery Ring approximately 388 ns after the centers of the main proton pulses. These are hypothesized to be the result of a combination of a RF frequency mismatch, particle space charge, and machine impedance during the rebunching sequence in the Recycler Ring, which can lead to particles leaking into adjacent buckets, but further studies and simulations are needed to confirm this

Hensley, R. [UC, Davis] (ORCID:0000000216064485)↗
NASA NTRS2011

Improving Distributed Diagnosis Through Structural Model Decomposition

Complex engineering systems require efficient fault diagnosis methodologies, but centralized approaches do not scale well, and this motivates the development of distributed solutions. This work presents an event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, by using the structural model decomposition capabilities provided by Possible Conflicts. We develop a distributed diagnosis algorithm that uses residuals computed by extending Possible Conflicts to build local event-based diagnosers based on global diagnosability analysis. The proposed approach is applied to a multitank system, and results demonstrate an improvement in the design of local diagnosers. Since local diagnosers use only a subset of the residuals, and use subsystem models to compute residuals (instead of the global system model), the local diagnosers are more efficient than previously developed distributed approaches.

Bregon, Anibal↗
NASA NTRS2015

Effects of Mesoscale Eddies in the Active Mixed Layer: Test of the Parametrisation in Eddy Resolving Simulations

In eddy resolving simulations, we test a mixed layer mesoscale parametrisation, developed recently by Canuto and Dubovikov [Ocean Model., 2011, 39, 200-207]. With no adjustable parameters, the parametrisation yields the horizontal and vertical mesoscale fluxes in terms of coarse-resolution fields and eddy kinetic energy (EKE). We compare terms of the parametrisation diagnosed from coarse-grained fields with the eddy mesoscale fluxes diagnosed directly from the high resolution model. An expression for the EKE in terms of mean fields has also been found to get a closed parametrisation in terms of the mean fields only. In 40 numerical experiments we simulated two types of flows: idealised flows driven by baroclinic instabilities only, and more realistic flows, driven by wind and surface fluxes as well as by inflow-outflow. The diagnosed quasi-instantaneous horizontal and vertical mesoscale buoyancy fluxes (averaged over 1-2 degrees and 10 days) demonstrate a strong scatter typical for turbulent flows, however, the fluxes are positively correlated with the parametrisation with higher (0.5-0.74) correlations at the experiments with larger baroclinic radius Rossby. After being averaged over 3-4 months, diffusivities diagnosed from the eddy resolving simulations are consistent with the parametrisation for a broad range of parameters. Diagnosed vertical mesoscale fluxes restratify mixed layer and are in a good agreement with the parametrisation unless vertical turbulent mixing in the upper layer becomes strong enough in comparison with mesoscale advection. In the latter case, numerical simulations demonstrate that the deviation of the fluxes from the parametrisation is controlled by dimensionless parameter estimating the ratio of vertical turbulent mixing term to mesoscale advection. An analysis using a modified omega-equation reveals that the effects of the vertical mixing of vorticity is responsible for the two-three fold amplification of vertical mesoscale flux. Possible physical mechanisms, responsible for the amplification of vertical mesoscale flux are discussed.

simulation↗
DOE OSTI2024

Association of post-COVID phenotypic manifestations with new-onset psychiatric disease

Acute COVID-19 infection can be followed by diverse clinical manifestations referred to as Post Acute Sequelae of SARS-CoV2 Infection (PASC). Studies have shown an increased risk of being diagnosed with new-onset psychiatric disease following a diagnosis of acute COVID-19. However, it was unclear whether non-psychiatric PASC-associated manifestations (PASC-AMs) are associated with an increased risk of new-onset psychiatric disease following COVID-19. A retrospective electronic health record (EHR) cohort study of 2,391,006 individuals with acute COVID-19 was performed to evaluate whether non-psychiatric PASC-AMs are associated with new-onset psychiatric disease. Data were obtained from the National COVID Cohort Collaborative (N3C), which has EHR data from 76 clinical organizations. EHR codes were mapped to 151 non-psychiatric PASC-AMs recorded 28–120 days following SARS-CoV-2 diagnosis and before diagnosis of new-onset psychiatric disease. Association of newly diagnosed psychiatric disease with age, sex, race, pre-existing comorbidities, and PASC-AMs in seven categories was assessed by logistic regression. There were significant associations between a diagnosis of any psychiatric disease and five categories of PASC-AMs with odds ratios highest for neurological, cardiovascular, and constitutional PASC-AMs with odds ratios of 1.31, 1.29, and 1.23 respectively. Secondary analysis revealed that the proportions of 50 individual clinical features significantly differed between patients diagnosed with different psychiatric diseases. Our study provides evidence for association between non-psychiatric PASC-AMs and the incidence of newly diagnosed psychiatric disease. Significant associations were found for features related to multiple organ systems. This information could prove useful in understanding risk stratification for new-onset psychiatric disease following COVID-19. Prospective studies are needed to corroborate these findings.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2005

Hybrid Systems Diagnosis

This paper reports on an on-going Project to investigate techniques to diagnose complex dynamical systems that are modeled as hybrid systems. In particular, we examine continuous systems with embedded supervisory controllers that experience abrupt, partial or full failure of component devices. We cast the diagnosis problem as a model selection problem. To reduce the space of potential models under consideration, we exploit techniques from qualitative reasoning to conjecture an initial set of qualitative candidate diagnoses, which induce a smaller set of models. We refine these diagnoses using parameter estimation and model fitting techniques. As a motivating case study, we have examined the problem of diagnosing NASA's Sprint AERCam, a small spherical robotic camera unit with 12 thrusters that enable both linear and rotational motion.

McIlraith, Sheila↗
NASA NTRS2007

Hybrid Automated Diagnosis of Discrete/Continuous Systems

A recently conceived method of automated diagnosis of a complex electromechanical system affords a complete set of capabilities for hybrid diagnosis in the case in which the state of the electromechanical system is characterized by both continuous and discrete values (as represented by analog and digital signals, respectively). The method is an integration of two complementary diagnostic systems: (1) beacon-based exception analysis for multi-missions (BEAM), which is primarily useful in the continuous domain and easily performs diagnoses in the presence of transients; and (2) Livingstone, which is primarily useful in the discrete domain and is typically restricted to quasi-steady conditions. BEAM has been described in several prior NASA Tech Briefs articles: "Software for Autonomous Diagnosis of Complex Systems" (NPO-20803), Vol. 26, No. 3 (March 2002), page 33; "Beacon-Based Exception Analysis for Multimissions" (NPO-20827), Vol. 26, No. 9 (September 2002), page 32; "Wavelet-Based Real-Time Diagnosis of Complex Systems" (NPO-20830), Vol. 27, No. 1 (January 2003), page 67; and "Integrated Formulation of Beacon-Based Exception Analysis for Multimissions" (NPO-21126), Vol. 27, No. 3 (March 2003), page 74. Briefly, BEAM is a complete data-analysis method, implemented in software, for real-time or off-line detection and characterization of faults. The basic premise of BEAM is to characterize a system from all available observations and train the characterization with respect to normal phases of operation. The observations are primarily continuous in nature. BEAM isolates anomalies by analyzing the deviations from nominal for each phase of operation. Livingstone is a model-based reasoner that uses a model of a system, controller commands, and sensor observations to track the system s state, and detect and diagnose faults. Livingstone models a system within the discrete domain. Therefore, continuous sensor readings, as well as time, must be discretized. To reason about continuous systems, Livingstone uses monitors that discretize the sensor readings using trending and thresholding techniques. In development of the a hybrid method, BEAM results were sent to Livingstone to serve as an independent source of evidence that is in addition to the evidence gathered by Livingstone standard monitors. The figure depicts the flow of data in an early version of a hybrid system dedicated to diagnosing a simulated electromechanical system. In effect, BEAM served as a "smart" monitor for Livingstone. BEAM read the simulation data, processed the data to form observations, and stored the observations in a file. A monitor stub synchronized the events recorded by BEAM with the output of the Livingstone standard monitors according to time tags. This information was fed to a real-time interface, which buffered and fed the information to Livingstone, and requested diagnoses at the appropriate times. In a test, the hybrid system was found to correctly identify a failed component in an electromechanical system for which neither BEAM nor Livingstone alone yielded the correct diagnosis.

Park, Han↗
NASA NTRS2010

An Event-Based Approach to Distributed Diagnosis of Continuous Systems

Distributed fault diagnosis solutions are becoming necessary due to the complexity of modern engineering systems, and the advent of smart sensors and computing elements. This paper presents a novel event-based approach for distributed diagnosis of abrupt parametric faults in continuous systems, based on a qualitative abstraction of measurement deviations from the nominal behavior. We systematically derive dynamic fault signatures expressed as event-based fault models. We develop a distributed diagnoser design algorithm that uses these models for designing local event-based diagnosers based on global diagnosability analysis. The local diagnosers each generate globally correct diagnosis results locally, without a centralized coordinator, and by communicating a minimal number of measurements between themselves. The proposed approach is applied to a multi-tank system, and results demonstrate a marked improvement in scalability compared to a centralized approach.

Daigle, Matthew↗
NASA NTRS2012

Intelligent Data Visualization for Cross-Checking Spacecraft System Diagnosis

Any reasoning system is fallible, so crew members and flight controllers must be able to cross-check automated diagnoses of spacecraft or habitat problems by considering alternate diagnoses and analyzing related evidence. Cross-checking improves diagnostic accuracy because people can apply information processing heuristics, pattern recognition techniques, and reasoning methods that the automated diagnostic system may not possess. Over time, cross-checking also enables crew members to become comfortable with how the diagnostic reasoning system performs, so the system can earn the crew s trust. We developed intelligent data visualization software that helps users cross-check automated diagnoses of system faults more effectively. The user interface displays scrollable arrays of timelines and time-series graphs, which are tightly integrated with an interactive, color-coded system schematic to show important spatial-temporal data patterns. Signal processing and rule-based diagnostic reasoning automatically identify alternate hypotheses and data patterns that support or rebut the original and alternate diagnoses. A color-coded matrix display summarizes the supporting or rebutting evidence for each diagnosis, and a drill-down capability enables crew members to quickly view graphs and timelines of the underlying data. This system demonstrates that modest amounts of diagnostic reasoning, combined with interactive, information-dense data visualizations, can accelerate system diagnosis and cross-checking.

Ong, James C.↗
DOE OSTI2025

Decoding substance use disorder severity from clinical notes using a large language model

Substance use disorder (SUD) poses a major concern due to its detrimental effects on health and society. SUD identification and treatment depend on a variety of factors such as severity, co-determinants (e.g., withdrawal symptoms), and social determinants of health. Existing diagnostic coding systems used by insurance providers, like the International Classification of Diseases (ICD-10), lack granularity for certain diagnoses, but American clinicians will add this granularity (as that found within the Diagnostic and Statistical Manual of Mental Disorders classification or DSM-5) as supplemental unstructured text in clinical notes. Traditional natural language processing (NLP) methods face limitations in accurately parsing such diverse clinical language. Large language models (LLMs) offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of LLMs for extracting severity-related information for various SUD diagnoses from clinical notes. We propose a workflow employing zero-shot learning of LLMs with carefully crafted prompts and post-processing techniques. Through experimentation with Flan-T5, an open-source LLM, we demonstrate its superior recall compared to the rule-based approach. Focusing on 11 categories of SUD diagnoses, we show the effectiveness of LLMs in extracting severity information, contributing to improved risk assessment and treatment planning for SUD patients.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1974

On-line diagnosis of sequential systems, 2

The theory and techniques applicable to the on-line diagnosis of sequential systems, were investigated. A complete model for the study of on-line diagnosis is developed. First an appropriate class of system models is formulated which can serve as a basis for a theoretical study of on-line diagnosis. Then notions of realization, fault, fault-tolerance and diagnosability are formalized which have meaningful interpretations in the the context of on-line diagnosis. The diagnosis of systems which are structurally decomposed and are represented as a network of smaller systems is studied. The fault set considered is the set of faults which only affect one component system is the network. A characterization of those networks which can be diagnosed using a purely combinational detector is achieved. A technique is given which can be used to realize any network by a network which is diagnosable in the above sense. Limits are found on the amount of redundancy involved in any such technique.

Sundstrom, R. J.↗