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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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

The interplanetary shock event of November 11/12 1978 - A comprehensive test of acceleration theory

A comprehensive study of the November 11, 12, 1978 shock event based on energetic particle, solar wind, magnetic field and wave data from the ISEE-3, -1 and -2 spacecraft has been undertaken both from the energetic and the collisionless shock point of view. The energy density of 10-50 keV protons accelerated by the shock is found to be equivalent to the upstream magnetic field energy density. The observations are in quantitative agreement with Lee's (1983) self consistent theory for the excitation of hydromagnetic waves and the acceleration of ions upstream of interplanetary shocks.

Wenzel, K.-P.↗
NASA NTRS1975

Vibrations measured in the passenger cabins of two jet transport aircraft

Accelerations in the lateral and vertical directions were measured at two locations on the floor of a three-jet-engine aircraft and at two locations on the floor of a two-jet-engine aircraft during a total of 13 flights, each of which included taxiing, takeoff, ascent, cruise, descent, and landing. Accelerations over the frequency range 0 to 25 Hz were recorded continuously on magnetic tape and were synchronized with the VGH recorders in the aircraft so that vibratory accelerations could be correlated with the operating conditions of the aircraft. From the results it was indicated that the methodology used in segmenting the data, which were obtained in a continuous and repetitive manner, contributes to establishing baseline data representative of the flight characteristics of aircraft. Significant differences among flight conductions were found to occur. The lateral accelerations were approximately 15 percent of the vertical accelerations during flight but as much as 50 to 100 percent of the vertical accelerations during ground operations. The variation between the responses of the two aircraft was not statistically significant. The results also showed that more than 90 percent of the vibratory energy measured during flight occurred in the 0- to 3.0-Hz frequency range. Generally, the vibration amplitudes were normally distributed.

Catherines, J. J.↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗
DOE OSTI2025

Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights

GeoGov Summit Final Presentation entitled "Utilizing AI and Spatial Data to Identify & Rapidly Disseminate Energy Infrastructure Insights". Maintaining the integrity of energy infrastructure plays a critical role in ensuring energy security. Robust foundational AI models using data from federal, state, industry, and other sources can help address integrity risk management & mitigation issues as well as evaluate extended use strategies. Trusted foundational models can help with industry adoption and accelerate innovation by enhancing integrity predictions, reduce costs, and informing infrastructure build-out. Coordination, collaboration & data sharing to develop robust models to aid in: Optimizing operations; Minimizing costs; Ensuring energy security.

Advanced Infrastructure Integrity Model (AIIM)↗
NASA NTRS2009

NEXT Long-Duration Test After 11,570 h and 237 kg of Xenon Processed

The NASA s Evolutionary Xenon Thruster (NEXT) program is developing the next-generation ion propulsion system with significant enhancements beyond the state-of-the-art in ion propulsion to provide future NASA science missions with enhanced mission capabilities at a low total development cost. As part of a comprehensive thruster service life assessment utilizing both testing and analyses, a Long-Duration Test (LDT) was initiated to validate and qualify the NEXT propellant throughput capability to a qualification-level of 450 kg, 1.5 times the mission-derived throughput requirement of 300 kg. This wear test is being conducted with a modified, flight-representative NEXT engineering model ion thruster, designated EM3. As of September 1, 2007, the thruster has accumulated 11,570 h of operation primarily at the thruster full-input-power of 6.9 kW with 3.52 A beam current and 1800 V beam power supply voltage. The thruster has processed 237 kg of xenon surpassing the NSTAR propellant throughput demonstrated during the extended life testing of the Deep Space 1 (DS1) flight spare. The NEXT LDT has demonstrated a total impulse of 9.78 10(exp 6) N(dot)s; the highest total impulse ever demonstrated by an ion thruster. Thruster performance tests are conducted periodically over the entire NEXT throttle table with input power ranging 0.5 to 6.9 kW. Thruster performance parameters including thrust, input power, specific impulse, and thruster efficiency have been nominal with little variation to date. Lifetime-limiting component erosion rates have been consistent with the NEXT service life assessment, which predicts the earliest failure sometime after 750 kg of xenon propellant throughput; well beyond the mission-derived lifetime requirement. The NEXT wear test data confirm that the erosion of the discharge keeper orifice, enlarging of nominal-current-density accelerator grid aperture cusps at full-power, and the decrease in cold grid-gap observed during NSTAR wear testing have been mitigated in the NEXT design. NEXT grid-gap data indicate a hot grid-gap at full-power that is 60 percent of the nominal cold grid-gap. This paper presents the status of the NEXT LDT to date with emphasis on comparison to the NSTAR extended life test results.

Soulas, George C.↗
DOE OSTI2026

Developing a complete AI-accelerated workflow for superconductor discovery

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T c > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

Gibson, Jason B. [Quantum Formatics, Cambridge, MA↗
DOE OSTI2026

Platform for Automated Anomaly Detection in the Mercury Process System at the Target System in the Spallation Neutron Source

The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory accelerates proton beams, which are directed toward a mercury target to generate the world’s most intense neutron beams via spallation. The target system consists of several interconnected subsystems and accounts for a major share of the facility’s overall downtime. Early detection of anomalies in the target system response can thus provide the possibility of taking corrective actions to reduce downtime. Accelerator facilities have largely focused on the beam side for data-driven fault prognostics. On the target side, SNS relies on operational shift technicians (OSTs), who respond to alarms and manually flag anomalies onto the System Tracking and Reliability (STAR) platform. This paper presents one of the first studies of using machine learning (ML) to automate anomaly detection in the target system. The study focused on the mercury process system as the first use case and employed reconstruction-based anomaly detection on minutely sampled time series signals. The pipeline was integrated into the STAR platform to autonomously rank and flag anomalies every week. The STAR platform provides a user interface for the OSTs to evaluate the flagged anomalies, thereby incorporating human feedback.

Anomaly detection↗
DOE OSTI2024

A machine learning approach for particle accelerator errant beam prediction using spatial phase deviation

Particle accelerators are extremely complex systems that are expected to operate on high availability. Predicting impending failures only by utilizing data collected from diagnostic equipment already on board can help operators to avoid installing expensive sensors, unscheduled downtime and associated costs. For this purpose we explore the predictive power of Machine Learning algorithms to detect faulty beams prior to the failure. In this study, we propose a Machine Learning approach to model mapping from a pair of sensors located across the accelerator. While the model is trained to represent normal operation, we evaluate the predictive performance on known faulty beam pulses. We also investigate the model performance on unseen data through k-fold cross-validation. Then we recap the analysis with a neural architecture search and hyperparameter optimization study to fine tune our initial model. In conclusion, this paper will also introduce a sustainable framework that can standardize Machine Learning workflow applied to particle accelerators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Operation of the ATLAS trigger system in Run 2

The ATLAS experiment at the Large Hadron Collider employs a two-level trigger system to record data at an average rate of 1 kHz from physics collisions, starting from an initial bunch crossing rate of 40 MHz. During the LHC Run 2 (2015–2018), the ATLAS trigger system operated successfully with excellent performance and flexibility by adapting to the various run conditions encountered and has been vital for the ATLAS Run-2 physics programme. For proton-proton running, approximately 1500 individual event selections were included in a trigger menu which specified the physics signatures and selection algorithms used for the data-taking, and the allocated event rate and bandwidth. The trigger menu must reflect the physics goals for a given data collection period, taking into account the instantaneous luminosity of the LHC and limitations from the ATLAS detector readout, online processing farm, and offline storage. This document discusses the operation of the ATLAS trigger system during the nominal proton-proton data collection in Run 2 with examples of special data-taking runs. Aspects of software validation, evolution of the trigger selection algorithms during Run 2, monitoring of the trigger system and data quality as well as trigger configuration are presented.

43 PARTICLE ACCELERATORS↗
NASA NTRS1994

Enhanced orbit determination filter sensitivity analysis: Error budget development

An error budget analysis is presented which quantifies the effects of different error sources in the orbit determination process when the enhanced orbit determination filter, recently developed, is used to reduce radio metric data. The enhanced filter strategy differs from more traditional filtering methods in that nearly all of the principal ground system calibration errors affecting the data are represented as filter parameters. Error budget computations were performed for a Mars Observer interplanetary cruise scenario for cases in which only X-band (8.4-GHz) Doppler data were used to determine the spacecraft's orbit, X-band ranging data were used exclusively, and a combined set in which the ranging data were used in addition to the Doppler data. In all three cases, the filter model was assumed to be a correct representation of the physical world. Random nongravitational accelerations were found to be the largest source of error contributing to the individual error budgets. Other significant contributors, depending on the data strategy used, were solar-radiation pressure coefficient uncertainty, random earth-orientation calibration errors, and Deep Space Network (DSN) station location uncertainty.

Estefan, J. A.↗
NASA NTRS2019

Variation in Ion Acceleration Characteristics of the HERMeS Hall Thruster During Magnetic Optimization

To reduce design risks for future magnetically shielded Hall thrusters, a test was performed on the HERMeS to obtain data for optimizing the effect of magnetic shielding. As a part of this test, laser-induced fluorescence velocimetry was used to characterize the variations in the ion acceleration with different magnetic configurations. Four magnetic configurations representing varying amounts of magnetic shielding between the high-energy discharge plasma and the discharge channel walls were tested. The ion velocity data points to the possibility that different plasma-wall interaction physics applies to a magnetically shielded thruster than a non-shielded thruster. The transition point is very prominent and can potentially be used to test whether a thruster is fully magnetically shielded.

Hall thrusters↗
NASA NTRS2019

Variation in Ion Acceleration Characteristics of the HERMeS Hall Thruster During Magnetic Optimization

To reduce design risks for future magnetically shielded Hall thrusters, a test was performed on the HERMeS to obtain data for optimizing the effect of magnetic shielding. As a part of this test, laser-induced fluorescence velocimetry was used to characterize the variations in the ion acceleration with different magnetic configurations. Four magnetic configurations representing varying amounts of magnetic shielding between the high-energy discharge plasma and the discharge channel walls were tested. The ion velocity data points to the possibility that different plasma-wall interaction physics applies to a magnetically shielded thruster than a non-shielded thruster. The transition point is very prominent and can potentially be used to test whether a thruster is fully magnetically shielded.

HERMeS↗
DOE OSTI2021

Genome Extraction from Shotgun Metagenome Sequence Data

Uncultivated Bacteria and Archaea comprise the vast majority of species on Earth, but obtaining their genomes directly from the environment, using shotgun sequencing, has only recently become possible. To realize the hope of capturing Earth’s microbial genetic complement, technologies that accelerate recovery of high-quality genomes are necessary. We present a series of analysis steps and data products for the extraction of high quality metagenome-assembled genomes (MAGs) from microbiomes using the U.S. Department of Energy Systems Biology Knowledgebase (KBase) platform (http://www.kbase.us/). In KBase, the process is end-to-end, allowing a user to go from the initial sequencing reads all the way through to MAG genomes, which can then be analyzed with other KBase capabilities such as phylogenetic placement, functional assignment, metabolic modeling, pangenome functional profiling, RNA-Seq, and others. While portions of such capabilities are individually available from other resources, the combination of the intuitive usability, data interoperability, and integration of tools in a freely available compute resource makes KBase a uniquely powerful platform for obtaining MAGs from microbiomes. While this workflow offers tools for each of the key steps in the genome extraction process, it also provides a scaffold that can be easily extended, with additional MAG recovery and analysis tools, via the KBase SDK (Software Development Kit).

Chivian, Dylan↗
NASA NTRS1998

Analysis of the pre-flight and post-flight calibration procedures performed on the Liulin space radiation dosimeter

Liulin, a dosimetry-radiometry system, was developed to satisfy the requirements for active flux and dose rate measurements for the flight of the second Bulgarian cosmonaut in 1988. The system consists of a compact battery-operated silicon solid state detector unit and a read/write microcomputer and telemetry unit. We describe the pre-flight calibrations with charged particles, using radioactive sources and accelerated 170 MeV/nucleon proton and alpha particles at the Dubna, Russia cyclotron. We discuss comparisons with data obtained on Mir with the French-built tissue equivalent LET spectrometer NAUSICAA. Lastly, we describe post-flight calibrations performed with 1 GeV/nucleon 56Fe ions at the Brookhaven National Laboratory AGS accelerator, where the instrument was mounted in tandem with several thin position-sensitive silicon detectors behind a stopping target. The silicon detectors provided an energy spectrum for the surviving charged nuclear fragments for which the flux and absorbed dose were recorded by Liulin.

long duration↗
DOE OSTI2021

GPU-accelerated multitiered iterative phasing algorithm for fluctuation X-ray scattering

The multitiered iterative phasing (MTIP) algorithm is used to determine the biological structures of macromolecules from fluctuation scattering data. It is an iterative algorithm that reconstructs the electron density of the sample by matching the computed fluctuation X-ray scattering data to the external observations, and by simultaneously enforcing constraints in real and Fourier space. This paper presents the first ever MTIP algorithm acceleration efforts on contemporary graphics processing units (GPUs). The Compute Unified Device Architecture (CUDA) programming model is used to accelerate the MTIP algorithm on NVIDIA GPUs. The computational performance of the CUDA-based MTIP algorithm implementation outperforms the CPU-based version by an order of magnitude. Furthermore, the Heterogeneous-Compute Interface for Portability (HIP) runtime APIs are used to demonstrate portability by accelerating the MTIP algorithm across NVIDIA and AMD GPUs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Efficient detection and signal parameter estimation with application to high dynamic GPS receiver

In a system for deriving position, velocity, and acceleration information from a received signal emitted from an object to be tracked wherein the signal comprises a carrier signal phase modulated by unknown binary data and experiencing very high Doppler and Doppler rate, this invention provides combined estimation/detection apparatus for simultaneously detecting data bits and obtaining estimates of signal parameters such as carrier phase and frequency related to receiver dynamics in a sequential manner. There is a first stage for obtaining estimates of the signal parameters related to phase and frequency in the vicinity of possible data transitions on the basis of measurements obtained within a current data bit. A second stage uses the estimates from the first stage to decide whether or not a data transition has actually occurred. There is a third stage for removing data modulation from the received signal when a data transition has occurred and a fourth stage for using the received signal with data modulation removed therefrom to update global parameters which are dependent only upon receiver dynamics and independent of data modulation. Finally, there is a fifth stage for using the global parameters to determine the position, velocity, and acceleration of the object.

Kumar, Rajendra↗
NASA NTRS1994

Double vision: Remote sensing of a flux transfer event with ISEE 1 and 2

A flux transfer event recorded by the International Sun-Earth Explorers (ISEE) 1 and 2 spacecraft in the northern hemisphere near local noon is examined using analysis techniques developed recently for determination from single satellite magnetometer data of the orientation and cross-sectional shape of a two-dimensional disturbance moving along the magnetopause. The analysis is performed independently on the magnetic field data recorded by each satellite, enabling us to check the consistency of the results and thus to benchmark the method. The analysis is also extended to utilize the dual magnetometer measurements for determination of the event speed and size (which for a single spacecraft requires use of three-dimensional plasma velocity data to obtain a deHoffmann-Teller frame velocity) and for accommodating acceleration of the magnetopause normal to itself, which results in a curved spacecraft trajectory in the frame of reference moving with the disturbance. The analysis of the disturbance reveals that while its overall size is about 34,000 km in a direction parallel to the magnetopause but perpendicular to the event axis, the actual size of the bulge causing the field disturbance may have been as small as 9000 km with a dimension perpendicular to the magnetopause of the order of 1200 km. The bulge is found to be traveling at a speed of about 140 km/s toward the northern dawn quadrant of the dayside magnetopause. While plasma data from ISEE 2 provides evidence of plasma jetting, which suggests the event may be associated with reconnection, the orientation of the flux tube axis obtained from analysis is significantly different from the expected reconnection line direction. A discussion of possible interpretations of this bulge orientation and motion is provided.

Walthour, D. W.↗