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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Collision of Comet Shoemaker-Levy 9 with Jupiter: Impact Study of Two Fragments from Timing of Precursor Events

The impacts of fragments K and R of comet Shoemaker-Levy 9 are examined with the aim to interpret the timing of the observed precursors to the main thermal emission event and to correlate the results of ground-based infrared observations with a variety of observations made onboard the Galileo spacecraft. Independent observations of the impact phenomena (of K & R) are combined.

comet Shoemaker-Levy 9 Jupiter impact Jovian atmos↗
NASA NTRS1978

SAS 3 observations of two X-ray transient events with precursors

SAS 3 has observed two unusual fast transient X-ray events from different sources, one lasting about 150 s and one, approximately 1500 s. Both events were preceded by precursor pulses which lasted a few seconds and which rose and fell in less than 0.4 s. The precursors were separated from the 'main' events by several seconds, during which no X-rays were detected. There are similarities between the two main events and X-ray bursts in both their temporal and spectral evolution. The spectra of the main events started out much softer than the spectra of the precursors, became harder as they approached maximum intensity, and softened as they decayed. In the 1500-s event, X-rays with energies greater than 10 keV were delayed by about 80 s compared with 1.5-6-keV X-rays. A blackbody fit to the spectral data of the main event of approximately 1500-s duration gives a maximum temperature of 29 million K and a radius for the emitting region of at least about 9 km (at a distance of 10 kpc); this is similar to the temperature and sizes found for several X-ray burst sources.

Hoffman, J. A.↗
NASA NTRSDate not supplied

Predicting Adverse Events and their Precursors in Aviation Using Multi-Class Multiple-Instance Learning

In recent years, there has been a rapid growth in the application of machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications. However, this paper focuses on the identification of precursors, which is a relatively newer application. Precursors are events correlated with adverse events that happen prior to the adverse event itself. Therefore, precursor mining provides many benefits including understanding the reasons behind a safety incident and the ability to identify signatures, which can be tracked throughout a flight to alert the operators of an potential upcoming adverse event. This work proposes using the multiple-instance learning (MIL) framework, a weakly supervised learning task, combined with a carefully designed Multi-Head Convolutional Neural Networks-Recurrent Neural Networks (MHCNN-RNN) architecture to predict different type of adverse events for any given flights and identify their precursors with little to no post-processing.Results obtained show that the MHCNN-RNN is able to accurately forecast high speed and high path angle events during the approach, and that it is also capable of determining the aircraft’s parameters that are correlated to these events. These parameters can be considered precursors to the events.

multiple instance learning↗
NASA NTRS1985

Helios 1 energetic particle observations of the solar gamma-ray/neutron flare events of 1982 June 3 and 1980 June 21

The characteristics of the energetic particles associated with the solar gamma-ray/neutron flare events of June 3, 1982 and June 21, 1980 observed by the Goddard cosmic-ray experiment on Helios 1 (at heliocentric distances of 0.57 and 0.54 AU, respectively) differ in several important respects from typical sonar particle increases. In particular, the June 3, 1982 event has a proton energy spectrum which fits a remarkable flat power law in kinetic energy with a spectral index of 1.2, an electron/proton ratio of 1 at 4 MeV, and a small but well-defined precursor event that began some 3 hr before the impulsive flare increase. Similar energetic particle precursors were observed for the gamma-ray associated 1980 June 21 and June 7 flare events. At energies less than about 40 MeV, the particle onset time measured for the June 3, 1982 event is anomalous, suggesting that these lower energy particles may have been released at the sun about 1-2 minutes before the higher energy particles.

Mcdonald, F. B.↗
NASA NTRS2010

Precursors of Short Gamma-Ray Bursts

We carried out a systematic search of precursors on the sample of short GRBs observed by Swift. We found that approx. 8-10% of short GRBs display such early episode of emission. One burst (GRB 090510) shows two precursor events, the former approx.13 s and the latter approx. 0.5 s before the GRB. We did not find any substantial difference between the precursor and the main GRB emission, and between short GRBs with and without precursors. We discuss possible mechanisms to reproduce the observed precursor emission within the scenario of compact object mergers. The implications of our results on quantum gravity constraints are also discussed.

Troja, E.↗
NASA NTRS1995

Pilot-Induced Oscillations and Human Dynamic Behavior

This is an in-depth survey and study of pilot-induced oscillations (PIO's) as interactions between human pilot and vehicle dynamics; it includes a broad and comprehensive theory of PIO's. A historical perspective provides examples of the diversity of PIO's in terms of control axes and oscillation frequencies. The constituents involved in PIO phenomena, including effective aircraft dynamics, human pilot dynamic behavior patterns, and triggering precursor events, are examined in detail as the structural elements interacting to produce severe pilot-induced oscillations. The great diversity of human pilot response patterns, excessive lags and/or inappropriate gain in effective aircraft dynamics, and transitions in either the human or effective aircraft dynamics are among the key sources implicated as factors in severe PIO's. The great variety of interactions which may result in severe PIO's is illustrated by examples drawn from famous PIO's. These are generalized under a pilot-behavior-theory-based set of categories proposed as a classification scheme pertinent to a theory of PIO's. Finally, a series of interim prescriptions to avoid PIO is provided.

McRuer, Duane T.↗
NASA NTRS2006

What Happened, and Why: Toward an Understanding of Human Error Based on Automated Analyses of Incident Reports

The objective of the Aviation System Monitoring and Modeling (ASMM) project of NASA s Aviation Safety and Security Program was to develop technologies that will enable proactive management of safety risk, which entails identifying the precursor events and conditions that foreshadow most accidents. This presents a particular challenge in the aviation system where people are key components and human error is frequently cited as a major contributing factor or cause of incidents and accidents. In the aviation "world", information about what happened can be extracted from quantitative data sources, but the experiential account of the incident reporter is the best available source of information about why an incident happened. This report describes a conceptual model and an approach to automated analyses of textual data sources for the subjective perspective of the reporter of the incident to aid in understanding why an incident occurred. It explores a first-generation process for routinely searching large databases of textual reports of aviation incident or accidents, and reliably analyzing them for causal factors of human behavior (the why of an incident). We have defined a generic structure of information that is postulated to be a sound basis for defining similarities between aviation incidents. Based on this structure, we have introduced the simplifying structure, which we call the Scenario as a pragmatic guide for identifying similarities of what happened based on the objective parameters that define the Context and the Outcome of a Scenario. We believe that it will be possible to design an automated analysis process guided by the structure of the Scenario that will aid aviation-safety experts to understand the systemic issues that are conducive to human error.

Maille, Nicolas P.↗
NASA NTRS2006

What Happened, and Why: Toward an Understanding of Human Error Based on Automated Analyses of Incident Reports

The objective of the Aviation System Monitoring and Modeling project of NASA's Aviation Safety and Security Program was to develop technologies to enable proactive management of safety risk, which entails identifying the precursor events and conditions that foreshadow most accidents. Information about what happened can be extracted from quantitative data sources, but the experiential account of the incident reporter is the best available source of information about why an incident happened. In Volume I, the concept of the Scenario was introduced as a pragmatic guide for identifying similarities of what happened based on the objective parameters that define the Context and the Outcome of a Scenario. In this Volume II, that study continues into the analyses of the free narratives to gain understanding as to why the incident occurred from the reporter s perspective. While this is just the first experiment, the results of our approach are encouraging and indicate that it will be possible to design an automated analysis process guided by the structure of the Scenario that can achieve the level of consistency and reliability of human analysis of narrative reports.

Ferryman, Thomas A.↗
NASA NTRS2019

Deep Learning Method for Detecting Precursors to Adverse Events

With the recent advancements in Deep Learning methods, the ability to model large complex heterogeneous data sets are fundamentally changing industry and research. Coupled with hardware improvements, and ease of implementation, a wide variety of deep neural network architectures can quickly be developed to solve a sweeping range of problems such as: object detection in images, automatic healthcare diagnosis using heterogenous data sources, real time language translating and sentence prediction, upscaling low resolution images, and forecasting of multivariate timeseries. Generally, many of these architectures outperform classical machine learning approaches in their respective tasks, however, this typically comes at a cost of interpretability. These black box algorithms generally suffer from lack of transparency in both model complexity as well as the rationale behind the prediction. This lack of comprehension, is driving an emerging area of interest in “Explainable AI”. An algorithm called: “Deep Temporal Multiple Instance Learning”1 was a recently developed to identify precursors to adverse events and has been applied in the aviation domain. The deep learning architecture is designed to capture the evolution of the probability of the outcome over the time preceding the adverse event using a multiple instance learning approach as illustrated in Figure 1. Precursors are defined when the probability of the event has exceeded a threshold at some point in the timeseries, at which point, a sensitivity analysis is performed to determine contributing factors. The contributing factors are used to explain and define the precursor during the periods where the probability score is high. The identified contributing factors are then presented to subject matter experts to provide objective insights into the leading factors associated with the particular adverse event. The algorithm has been tested on flight data from a commercial airline and has the ability to discover precursors to known adverse events that take the form of safety critical operations, such as unstable approach events on final approach. Apart from detecting precursors to adverse events, the converse can also be leveraged to discover corrective actions. These positive actions manifest themselves as periods in the timeseries when the precursor score has been lowered from an elevated state; meaning that if the system had been left uncorrected, it would have eventually reached the adverse event state. Characterizing these state changes can help identify successful interventions that may not have been known before. Policy makers and procedure designers can use this additional knowledge to craft more safety and efficient resilient procedures for future operations and therefore improve the overall performance of the National Airspace.

Matthews, Bryan L.↗
NASA NTRS2018

Using ADOPT Algorithm and Operational Data to Discover Precursors to Aviation Adverse Events

The US National Airspace System (NAS) is making its transition to the NextGen system and assuring safety is one of the top priorities in NextGen. At present, safety is managed reactively (correct after occurrence of an unsafe event). While this strategy works for current operations, it may soon become ineffective for future airspace designs and high density operations. There is a need for proactive management of safety risks by identifying hidden and "unknown" risks and evaluating the impacts on future operations. To this end, NASA Ames has developed data mining algorithms that finds anomalies and precursors (high-risk states) to safety issues in the NAS. In this paper, we describe a recently developed algorithm called ADOPT that analyzes large volumes of data and automatically identifies precursors from real world data. Precursors help in detecting safety risks early so that the operator can mitigate the risk in time. In addition, precursors also help identify causal factors and help predict the safety incident. The ADOPT algorithm scales well to large data sets and to multidimensional time series, reduce analyst time significantly, quantify multiple safety risks giving a holistic view of safety among other benefits. This paper details the algorithm and includes several case studies to demonstrate its application to discover the "known" and "unknown" safety precursors in aviation operation.

aviation safet↗
NASA NTRS2017

Constraints on Mechanisms of Chondrule Formation from Chondrule Precursors and Chronology of Transient Heating Events in the Protoplanetary Disk

The mineralogy, petrography, and oxygen-isotope compositions of porphyritic chondrules—dominant chondrule type in most chondrite groups—suggest formation by incomplete melting of isotopically diverse precursors during localized transient heating events in dust-rich regions of the protoplanetary disk characterized by 16O-poor compositions (D17Odust+gas~ –7‰ to +4‰) relative to the inferred Sun’s value (D17O ~ –28 ± 2‰). The chondrule precursors included Ca,Al-rich inclusions (CAIs), amoeboid olivine aggregates (AOAs), chondrules of earlier generations, fine-grained matrix-like material, and possibly fragments of pre-existing planetesimals. Like porphyritic chondrules, igneous CAIs formed by melting of isotopically diverse precursors during transient heating events, but in an isotopically distinct, solar-like reservoir of the protoplanetary disk (D17O dust + gas ~ –24‰), probably near the proto Sun. Based on a narrow range of the initial 26Al/27Alratios inferred from the internal Al-Mg isochrons in igneous CAIs, their melting started at the very beginning of Solar System formation (t0), defined by the CV CAIs with U-corrected Pb-Pb age of 4567.3 ± 0.16 Ma and the canonical 26Al/27Al ratio of (5.25 ± 0.02) ¥ 10–5, and lasted at least 0.3 Ma. The U-corrected Pb-Pb absolute and 26Al-26Mg relative ages of porphyritic chondrules from type 3 ordinary, CO, CV, and CR carbonaceous chondrites (assuming uniform distribution of 26Al in the disk at the canonical level) suggest chondrule formation started at t0 and lasted for about 4 Ma. These observations may preclude formation of the majority of porphyritic chondrules by splashing of differentiated planetesimals and by collisions between planetesimals; instead, they are consistent with melting of dust balls by bow shocks or magnetized turbulence in the disk. Some porphyritic chondrules in equilibrated (petrologic type 4–6) ordinary chondrites contain relict fragments of coarse-grained chromite, ilmenite, phosphates, and albitic plagioclase. The similar mineral assemblage is commonly observed in type 4–6 ordinary chondrites, but is absent in type 3 chondrites, suggesting these chondrules formed by incomplete melting of thermally metamorphosed ordinary chondrite material, possibly by impacts. The CB metal-rich carbonaceous chondrites contain exclusively magnesian non-porphyritic chondrules crystallized from complete melts. These chondrules formed in a gas-melt plume generated by a hypervelocity (≥20 km/s) collision between planetesimals ~4.8 Ma after t0 in a transition or a debris disk. One of the colliding bodies was probably differentiated. The CH metal-rich carbonaceous chondrites contain chondrules formed by different mechanisms. The magnesian non-porphyritic chondrules formed in the CB impact plume ~4.8 Ma after t0. The chemically diverse (magnesian, ferroan, and Al-rich) porphyritic chondrules formed by incomplete melting of isotopically diverse precursors in the protoplanetary disk, most likely prior the CB impact plume event. We conclude that there are multiple mechanisms of chondrule formation that operated over the entire life-time of the disk.

Chondrules↗
NASA NTRS2014

Advanced Modeling and Uncertainty Quantification for Flight Dynamics; Interim Results and Challenges

As part of the NASA Vehicle Systems Safety Technologies (VSST), Assuring Safe and Effective Aircraft Control Under Hazardous Conditions (Technical Challenge #3), an effort is underway within Boeing Research and Technology (BR&T) to address Advanced Modeling and Uncertainty Quantification for Flight Dynamics (VSST1-7). The scope of the effort is to develop and evaluate advanced multidisciplinary flight dynamics modeling techniques, including integrated uncertainties, to facilitate higher fidelity response characterization of current and future aircraft configurations approaching and during loss-of-control conditions. This approach is to incorporate multiple flight dynamics modeling methods for aerodynamics, structures, and propulsion, including experimental, computational, and analytical. Also to be included are techniques for data integration and uncertainty characterization and quantification. This research shall introduce new and updated multidisciplinary modeling and simulation technologies designed to improve the ability to characterize airplane response in off-nominal flight conditions. The research shall also introduce new techniques for uncertainty modeling that will provide a unified database model comprised of multiple sources, as well as an uncertainty bounds database for each data source such that a full vehicle uncertainty analysis is possible even when approaching or beyond Loss of Control boundaries. Methodologies developed as part of this research shall be instrumental in predicting and mitigating loss of control precursors and events directly linked to causal and contributing factors, such as stall, failures, damage, or icing. The tasks will include utilizing the BR&T Water Tunnel to collect static and dynamic data to be compared to the GTM extended WT database, characterizing flight dynamics in off-nominal conditions, developing tools for structural load estimation under dynamic conditions, devising methods for integrating various modeling elements into a real-time simulation capability, generating techniques for uncertainty modeling that draw data from multiple modeling sources, and providing a unified database model that includes nominal plus increments for each flight condition. This paper presents status of testing in the BR&T water tunnel and analysis of the resulting data and efforts to characterize these data using alternative modeling methods. Program challenges and issues are also presented.

Hyde, David C.↗
NASA NTRS1990

North Atlantic weather regimes: A synoptic study of phase space

In the phase space of weather, low frequency variability (LFV) of the atmosphere can be captured in a large scale subspace, where a trajectory connects consecutive large scale weather maps, thus revealing flow changes and recurrences. Using this approach, Vautard applied the trajectory speed minimization method (Vautard and Legras) to atmospheric data. From 37 winters of 700 mb geopotential height anomalies over the North Atlantic and the adjacent land masses, four persistent and recurrent weather patterns, interpreted as weather regimes, were discernable: a blocking regime, a zonal regime, a Greenland anticyclone regime, and an Atlantic regime. These regimes are studied further in terms of maintenance and transitions. A regime survey unveils preferences regarding event durations and precursors for the onset or break of an event. The transition frequencies between regimes vary, and together with the transition times, suggest the existence of easier transition routes. These matters are more systematically studied using complete synoptic map sequences from a number of events.

Orrhede, Anna Karin↗
NASA NTRS2022

Can Machine Learning Reveal Precursors of Reversals of the Geomagnetic Axial Dipole Field?

It is well known that the axial dipole part of Earth’s magnetic field reverses polarity, so that the magnetic North Pole becomes the South Pole and vice versa. The timing of reversals is well documented for the past 160 Myr, but the conditions that lead to a reversal are still not well understood. It is not known if there are reliable ‘precursors’ of reversals (events that indicate that a reversal is upcoming) or what they might be. We investigate if machine learning (ML) techniques can reliably identify precursors of reversals based on time-series of the axial magnetic dipole field. The basic idea is to train a classifier using segments of time-series of the axial magnetic dipole. This training step requires modification of standard ML techniques to account for the fact that we are interested in rare events—a reversal is unusual, while a non-reversing field is the norm. Without our tweak, the ML classifiers lead to useless predictions. Perhaps even more importantly, the usable observational record is limited to 0–2 Ma and contains only five reversals, necessitating that we determine if the data are even sufficient to reliably train and validate an ML algorithm. To answer these questions we use several ML classifiers (linear/non-linear support vector machines and long short-term memory networks), invoke a hierarchy of numerical models (from simplified models to 3-D geodynamo simulations), and two palaeomagnetic reconstructions (PADM2M and Sint-2000). The performance of the ML classifiers varies across the models and the observational record and we provide evidence that this is not an artefact of the numerics, but rather reflects how ‘predictable’ a model or observational record is. Studying models of Earth’s magnetic field via ML classifiers thus can help with identifying shortcomings or advantages of the various models. For Earth’s magnetic field, we conclude that the ability of ML to identify precursors of reversals is limited, largely due to the small amount and low frequency resolution of data, which makes training and subsequent validation nearly impossible. Put simply: the ML techniques we tried are not currently capable of reliably identifying an axial dipole moment (ADM) precursor for geomagnetic reversals. This does not necessarily imply that such a precursor does not exist, and improvements in temporal resolution and length of ADM records may well offer better prospects in the future.

Kyle Gwirtz↗
NASA NTRS1994

Constraints placed on the chondrule-forming process by merrihueite in the Mezoe-Madaras chondrite

A few unusual chondrules in the Mezoe-Madaras L3 chondrite contain the peralkaline mineral merrihueite, (K, Na)2(Fe, Mg)5Si12O30, formed by reaction of free silica in the chondrules with K, Na, Fe-rich nebular gas. Vapor fractionation was required to create chondrules poor in Al and rich in Si, and the fractionated condensate produced had to aggregate into protochondrules very promptly (on the order of hours or less) to prevent it from mixing with dust of more generalized composition in the nebula. The traditional paradigm for chondrule formation, involving a series of unrelated events (chemical fractionation of precursor dust; clumping of the dust; melting of the dust-clumps), is inadequate for making these chondrules. A rapid process, all stages of which are related, is required. We propose that this occurred during the cooling and contraction of the volume of hot gas created by a small transient high-energy event in the nebula, of unspecified nature. Microdroplets of silicate melt created by condensation from the cooling, contracting gas had velocity vectors directed toward the center of the volume. Coalescence occurred in the crowded central zone where the microdroplets converged. The Mezoe-Madaras merriihueite-bearing chondrules may have been formed by an atypically low-energy nebular event, which achieved the needed chemical fractionations by only partially vaporizing precursor dust. More commonplace chondrule types may have been created by the same basic process, but involving higher-energy events in which precursor dust was completely vaporized and recondensed.

Wood, John A.↗
DOE OSTI2024

Role of atmospheric resonance and land–atmosphere feedbacks as a precursor to the June 2021 Pacific Northwest Heat Dome event

We demonstrate an indirect, rather than direct, role of quasi-resonant amplification of planetary waves in a summer weather extreme. We find that there was an interplay between a persistent, amplified large-scale atmospheric circulation state and soil moisture feedbacks as a precursor for the June 2021 Pacific Northwest “Heat Dome” event. An extended resonant planetary wave configuration prior to the event created an antecedent soil moisture deficit that amplified lower atmospheric warming through strong nonlinear soil moisture feedbacks, favoring this unprecedented heat event.

54 ENVIRONMENTAL SCIENCES↗