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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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All sourcesNASA NTRSDOE OSTI
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.↗
NASA NTRS2002

Regional Climate Simulation of the Anomalous Events of 1998 using a Stretched-Grid GCM with Multiple Areas of Interest

The GEOS (Goddard Earth Observing System) stretched-grid (SG) GCM developed and thoroughly tested over the last few years, is used for simulating the major anomalous regional climate events of 1998. The anomalous regional climate events are simulated simultaneously during the 13 months long (November-1997 - December-1998) SG-GCM simulation due to using the new SG-design with multiple (four) areas of interest. The following areas/regions of interest (one at each global quadrant) are implemented: U.S./Northern Mexico, the El-Nino/Brazil area, India-China, and Eastern Indian Ocean/Australia.

Fox-Rabinovitz, M. S.↗
NASA NTRS1994

Venusian atmospheric equilibrium chemistry at the Pioneer Venus anomalous event altitude

No convincing explanation for the anomalous behavior of the Atmospheric Structure Experiment temperature sensors at approximately 13 km altitude has been found. It occurred on all of the widely-spaced probes, in a similar fashion. A preliminary effort has been made to determine atmospheric chemical species which might be present at 13 km. The purpose of this effort is to initiate suggestions of possible chemical interactions and to explore the effects of the presence of possible metal reactants including condensation. Equilibrium fractions of chemical species were calculated at a variety of conditions. Baseline calculations were made for the altitudes near 13 km. For comparison calculations were also made at 13 km but with the introduction of plausible metal atoms.

Craig, Roger A.↗
DOE OSTI2025

Search for the Anomalous Events Detected by ANITA Using the Pierre Auger Observatory

A dedicated search for upward-going air showers at zenith angles exceeding 110° and energies E > 0.1 EeV has been performed using the Fluorescence Detector of the Pierre Auger Observatory. The search is motivated by two “anomalous” radio pulses observed by the ANITA flights I and III that appear inconsistent with the standard model of particle physics. Using simulations of both regular cosmic-ray showers and upward-going events, a selection procedure has been defined to separate potential upward-going candidate events and the corresponding exposure has been calculated in the energy range [0.1–33] EeV. One event has been found in the search period between January 1, 2004, and December 31, 2018, consistent with an expected background of 0.27 ± 0.12 events from misreconstructed cosmic-ray showers. This translates to an upper bound on the integral flux of ( 7.2 ± 0.2 ) × 10 − 21 cm − 2 sr − 1 y − 1 and ( 3.6 ± 0.2 ) × 10 − 20 cm − 2 sr − 1 y − 1 for an E − 1 and E − 2 spectrum, respectively. An upward-going flux of showers normalized to the ANITA observations is shown to predict over 34 events for an E − 3 spectrum and over 8.1 events for a conservative E − 5 spectrum, in strong disagreement with the interpretation of the anomalous events as upward-going showers. Published by the American Physical Society 2025

Abdul Halim, A.↗
DOE OSTI2021

Robust Deep Gaussian Process-Based Probabilistic Electrical Load Forecasting Against Anomalous Events

The abnormal events, such as the unprecedented COVID-19 pandemic, can significantly change the load behaviors, leading to huge challenges for traditional short-term forecasting methods. This article proposes a robust deep Gaussian processes (DGP)-based probabilistic load forecasting method using a limited number of data. Since the proposed method only requires a limited number of training samples for load forecasting, it allows us to deal with extreme scenarios that cause short-term load behavior changes. In particular, the load forecasting at the beginning of abnormal event is cast as a regression problem with limited training samples and solved by double stochastic variational inference DGP. The mobility data are also utilized to deal with the uncertainties and pattern changes and enhance the flexibility of the forecasting model. The proposed method can quantify the uncertainties of load forecasting outcomes, which would be essential under uncertain inputs. Extensive comparison results with other state-of-the-art point and probabilistic forecasting methods show that our proposed approach can achieve high forecasting accuracies with only a limited number of data while maintaining the excellent performance of capturing the forecasting uncertainties.

anomalous events↗
NASA NTRS1991

Anomalous optical events detected by rocket-borne sensor in the WIPP campaign

This paper describes the instruments used in the Wave Induced Particle Precipitation campaign in 1987, in which one rocket and four balloons were launched near thunderstorms from the NASA Wallops Flight Facility. Examples of both the lightning events and the anomalous optical events (AOEs) detected on July 31, 1987 are presented. It is shown that the signatures of these AOEs differ from those of well-known optical sources in the atmosphere. It was found that AOEs were sometimes associated with subionospheric VLF signal perturbations (Trimpi events), suggesting a possibility that AOEs might be linked to a disturbance of the electron concentration in the high-altitude atmosphere or lower ionosphere.

Li, Ya QI↗
NASA NTRS1992

History of on-orbit satellite fragmentations

Since the first serious satellite fragmentation occurred in Jun. 1961, and instantaneously increased the total Earth satellite population by more than 400 percent, the issue of space operations within the finite region of space around the Earth has been the subject of increasing interest and concern. The prolific satellite fragmentations of the 1970's and the marked increase in the number of fragmentations in the 1980's served to widen international research into the characteristics and consequences of such events. Plans for large, manned space stations in the next decade and beyond demand a better understanding of the hazards of the dynamic Earth satellite population. The contribution of satellite fragmentations to the growth of the Earth satellite population is complex and varied. The majority of detectable fragmentation debris have already fallen out of orbit, and the effects of 40 percent of all fragmentations have completely disappeared. In this volume, satellite fragmentations are categorized by their assessed nature and to a lesser degree by their effect on the near-Earth space environment. A satellite breakup is the usually destructive disassociation of an orbital payload, rocket body, or structure, often with a wide range of ejecta velocities. A satellite breakup may be accidental or the result of intentional actions, e.g., due to a propulsion system malfunction or a space weapons test, respectively. An anomalous event is the unplanned separation, usually at low velocity, of one or more detectable objects from a satellite which remains essentially intact. Anomalous events can be caused by material deterioration of items such as thermal blankets, protective shields, or solar panels. As a general rule, a satellite breakup will produce considerably more debris, both trackable and non-trackable, than an anomalous event. From one perspective, satellite breakups may be viewed as a measure of the effects of man's activity on the environment, while anomalous events may be a measure of the environment on man-made objects.

Nauer, David J.↗
NASA NTRS1974

Solar particle events with anomalously large relative abundance of He-3

Experimental results are reported on three energetic particle events with extremely high abundance of He-3. The measurements were taken onboard the OGO-5 satellite. It was shown that the abundance of protons relative to He nuclei were significantly low in these events. Not more than four H-2 and three H-3 were detected during the entire period under study compared to 1110 H-3 nuclei. The limitations these observations place on theoretical models are discussed to explain He-3 flares.

Serlemitsos, A. T.↗
DOE OSTI2021

Classification and computation of extreme events in turbulent combustion

In the design of practical combustion systems, ensuring safety and reliability is an important requirement. For instance, reliably avoiding lean blowout, flame flashback or inlet unstart is critical for ensuring safe operation. Currently, the science of predicting such events is based on prior experience, limited modeling or diagnostic tools and purely statistical approaches. Even though computational and experimental tools for studying combustion devices have vastly advanced in the last three decades, the analysis of such failure events has not been pursued widely. While the use of data for model development and calibration is being widely accepted, the extension to failure events introduces numerous challenges. In particular, the focus here is on so-called data-poor problems, where the cost of generating data is extremely high and is not easily amenable to existing computational and experimental approaches. Data-poor problems are particularly relevant when related to extreme events (also called anomalous events) that can lead to catastrophic failure of the system. It is argued that transient events that describe such failure can have different causal mechanisms. To develop the scientific inference process, a classification of such problems is used to determine specific modeling paths as well as computational tools needed. Research opportunities in the emerging field of extreme event prediction are highlighted in order to identify critical and immediate needs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Analysis of Superconducting Magnet Quench Antenna Data

Quenching poses a serious problem for superconducting magnets operating at high currents. It occurs when the material transitions from the superconducting to the normal state, which leads to heating and potential damage to the magnet. To understand and mitigate quenching, the Magnet Department at Fermilab is developing and testing superconducting magnet quench antenna arrays. This study delves into the anomalous events preceding the quench during magnet training by analyzing the collected data. With the moving average and Fast Fourier Transform techniques, we investigate the trends and frequency patterns of the data. Moreover, we introduce an unsupervised anomaly detection algorithm based on Principal Component Analysis and DBSCAN clustering. It can autonomously identify events within background noise, without relying on any predefined event features. Our analysis reveals that the spatio-temporal distribution of these anomalous events has little connection to the quench location, indicating that a majority of them bear no relation to the quenching process.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Detecting Anomalies in Time Series Using Kernel Density Approaches

This paper introduces a novel anomaly detection approach tailored for time series data with exclusive reliance on normal events during training. Our key innovation lies in the application of kernel-density estimation (KDE) to scrutinize reconstruction errors, providing an empirically derived probability distribution for normal events post-reconstruction. This non-parametric density estimation technique offers a nuanced understanding of anomaly detection, differentiating it from prevalent threshold-based mechanisms in existing methodologies. In post-training, events are encoded, decoded, and evaluated against the estimated density, providing a comprehensive notion of normality. In addition, we propose a data augmentation strategy involving variational autoencoder-generated events and a smoothing step for enhanced model robustness. The significance of our autoencoder-based approach is evident in its capacity to learn normal representation without prior anomaly knowledge. Through the KDE step on reconstruction errors, our method addresses the versatility of anomalies, departing from assumptions tied to larger reconstruction errors for anomalous events. Our proposed likelihood measure then distinguishes normal from anomalous events, providing a concise yet comprehensive anomaly detection solution. The extensive experimental results support the feasibility of our proposed method, yielding significantly improved classification performance by nearly 10% on the UCR benchmark data.

Frehner, Robin↗
NASA NTRS1975

Solar particle events with anomalously large relative abundance of He-3

Energetic particle data are presented from a series of solar flares with a relative abundance of He-3 much higher than that of any previous events. The abundance of protons relative to He nuclei was significantly low in these events; not more than four H-2 and three H-3 were detected during the entire period under study, compared with 1110 He-3 nuclei. Results from these experiments are compared with data available from other investigations, and the limitations the former observations place on theoretical models to explain He-3-rich flares are discussed.

Serlemitsos, A. T.↗
NASA NTRS2010

An Accident Precursor Analysis Process Tailored for NASA Space Systems

Accident Precursor Analysis (APA) serves as the bridge between existing risk modeling activities, which are often based on historical or generic failure statistics, and system anomalies, which provide crucial information about the failure mechanisms that are actually operative in the system and which may differ in frequency or type from those in the various models. These discrepancies between the models (perceived risk) and the system (actual risk) provide the leading indication of an underappreciated risk. This paper presents an APA process developed specifically for NASA Earth-to-Orbit space systems. The purpose of the process is to identify and characterize potential sources of system risk as evidenced by anomalous events which, although not necessarily presenting an immediate safety impact, may indicate that an unknown or insufficiently understood risk-significant condition exists in the system. Such anomalous events are considered accident precursors because they signal the potential for severe consequences that may occur in the future, due to causes that are discernible from their occurrence today. Their early identification allows them to be integrated into the overall system risk model used to intbrm decisions relating to safety.

Groen, Frank↗
DOE OSTI2024

EV SALaD 2023 Demonstration: Best Practices and Mitigations for Protecting EVSE Infrastructure

The Electric Vehicle Secure Architecture Laboratory Demonstration (EV SALaD) program is a demonstration of cybersecurity best practices for high-power electric vehicle (EV) charging infrastructure led by Idaho National Laboratory (INL), in collaboration with other DOE National Laboratories participating in the EVs at Scale Consortium.a Sandia National Laboratories (SNL) and Pacific Northwest National Laboratory (PNNL) participated in the first 2-year (FY22-23) demonstration cycle for EV SALaD. This report documents the FY23 demonstration, the second in a series of demonstrations and collaborations in deploying and operating cybersecure EV charging infrastructure. It includes a summary of improvements from the FY22 demonstration, technical analysis of the FY23 demonstration, how the research demonstrates cyber-physical and cybersecurity best practices for high-power EV charging infrastructure, and related impacts to national and energy security. For EV SALaD, the FY22 demonstration focused on the detection, ranking, and prioritization of anomalous events for high-power EV charging. The FY23 demonstration additionally included the demonstration of cybersecurity best practices, which included protection and mitigation solutions to prevent, respond, and recover from anomalous events. During the demonstrations, the multi-lab EV SALaD team conducted a Test Effect Payload (TEP)b evaluation on extreme fast charger (XFC) hardware equipped with Cerberus, a detection and response solution, to demonstrate anomaly detection and mitigation cybersecurity best practices against cyber-enabled events.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2012

Adaptive Sampling of Time Series During Remote Exploration

This work deals with the challenge of online adaptive data collection in a time series. A remote sensor or explorer agent adapts its rate of data collection in order to track anomalous events while obeying constraints on time and power. This problem is challenging because the agent has limited visibility (all its datapoints lie in the past) and limited control (it can only decide when to collect its next datapoint). This problem is treated from an information-theoretic perspective, fitting a probabilistic model to collected data and optimizing the future sampling strategy to maximize information gain. The performance characteristics of stationary and nonstationary Gaussian process models are compared. Self-throttling sensors could benefit environmental sensor networks and monitoring as well as robotic exploration. Explorer agents can improve performance by adjusting their data collection rate, preserving scarce power or bandwidth resources during uninteresting times while fully covering anomalous events of interest. For example, a remote earthquake sensor could conserve power by limiting its measurements during normal conditions and increasing its cadence during rare earthquake events. A similar capability could improve sensor platforms traversing a fixed trajectory, such as an exploration rover transect or a deep space flyby. These agents can adapt observation times to improve sample coverage during moments of rapid change. An adaptive sampling approach couples sensor autonomy, instrument interpretation, and sampling. The challenge is addressed as an active learning problem, which already has extensive theoretical treatment in the statistics and machine learning literature. A statistical Gaussian process (GP) model is employed to guide sample decisions that maximize information gain. Nonsta tion - ary (e.g., time-varying) covariance relationships permit the system to represent and track local anomalies, in contrast with current GP approaches. Most common GP models are stationary, e.g., the covariance relationships are time-invariant. In such cases, information gain is independent of previously collected data, and the optimal solution can always be computed in advance. Information-optimal sampling of a stationary GP time series thus reduces to even spacing, and such models are not appropriate for tracking localized anomalies. Additionally, GP model inference can be computationally expensive.

Thompson, David R.↗