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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 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
NASA NTRS1981

Mechanism of parallel electric fields inferred from observations

Data from various experiments of the Atmosphere Explorer satellites are analyzed to test their consistency with the model of single-particle linear acceleration through a static parallel potential drop. Theoretical current/voltage and energy flux/voltage relations are applied to the data to predict the densities of source current carriers above the acceleration region for upward Birkeland currents. Comparison of these model predictions with simultaneous electron data indicates that the observed field-aligned potential drops are consistent with direct acceleration in the absence of anomalous resistivity.

Yeh, H.-C.↗
DOE OSTI2023

Advanced data analysis in inertial confinement fusion and high energy density physics

Bayesian analysis enables flexible and rigorous definition of statistical model assumptions with well-characterized propagation of uncertainties and resulting inferences for single-shot, repeated, or even cross-platform data. This approach has a strong history of application to a variety of problems in physical sciences ranging from inference of particle mass from multi-source high-energy particle data to analysis of black-hole characteristics from gravitational wave observations. The recent adoption of Bayesian statistics for analysis and design of high-energy density physics (HEDP) and inertial confinement fusion (ICF) experiments has provided invaluable gains in expert understanding and experiment performance. In this Review, we discuss the basic theory and practical application of the Bayesian statistics framework. We highlight a variety of studies from the HEDP and ICF literature, demonstrating the power of this technique. Due to the computational complexity of multi-physics models needed to analyze HEDP and ICF experiments, Bayesian inference is often not computationally tractable. Two sections are devoted to a review of statistical approximations, efficient inference algorithms, and data-driven methods, such as deep-learning and dimensionality reduction, which play a significant role in enabling use of the Bayesian framework. We provide additional discussion of various applications of Bayesian and machine learning methods that appear to be sparse in the HEDP and ICF literature constituting possible next steps for the community. We conclude by highlighting community needs, the resolution of which will improve trust in data-driven methods that have proven critical for accelerating the design and discovery cycle in many application areas.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Use of the Tool to Support Renewable Energy Project Development (RE Data Explorer)

The Renewable Energy (RE) Data Explorer is a user-friendly, geospatial analysis tool for analyzing renewable energy potential and informing decisions. Developed by the National Renewable Energy Laboratory (NREL) and supported by the U.S. Agency for International Development (USAID), RE Data Explorer performs visualization and analysis of renewable energy potential that can be customized for different scenarios. RE Data Explorer can support prospecting, integrated planning, policymaking, and other decision-making activities to accelerate renewable energy deployment. This document provides information on how the RE Data Explorer can support private sector utility-scale renewable energy project development.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1990

Vibration environment - Acceleration mapping strategy and microgravity requirements for Spacelab and Space Station

In order to define the acceleration requirements for future Shuttle and Space Station Freedom payloads, methods and hardware characterizing accelerations on microgravity experiment carriers are discussed. The different aspects of the acceleration environment and the acceptable disturbance levels are identified. The space acceleration measurement system features an adjustable bandwidth, wide dynamic range, data storage, and ability to be easily reconfigured and is expected to fly on the Spacelab Life Sciences-1. The acceleration characterization and analysis project describes the Shuttle acceleration environment and disturbance mechanisms, and facilitates the implementation of the microgravity research program.

Martin, Gary L.↗
NASA NTRS1998

g-LIMIT: A Vibration Isolation System for the Microgravity Science Glovebox

For many microgravity science experiments using the Microgravity Science Glovebox (MSG), the ambient acceleration environment will exceed desirable levels. To provide a more quiescent acceleration environment, a vibration isolation system named g-LIMIT (GLovebox Integrated Microgravity Isolation Technology) is being designed. g-LIMIT is the next generation of technology developed for and demonstrated by STABLE on the USML-2 mission in October 1995. Although g-LIMIT is a sub-rack level isolation system that can be used in a variety of applications, g-LIMIT is uniquely optimized for MSG implementation. Standard MSG structural and umbilical interfaces will be used so that the isolation mount is transparent to the user with no additional accommodation requirements. g-LIMIT consists of three integrated isolator modules, each of which is comprised of a dual axis actuator, two axes of acceleration sensing, two axes of position sensing, control electronics, and data transmission capabilities in a minimum-volume package. In addition, this system provides the unique capability for measuring absolute acceleration of the experiment independent of accelerometers as a by-product of the control system and will have the capability of generating pristine accelerations to enhance experiment operations. g-LIMIT is scheduled for flight during the UF-2 mission and will be available to glovebox investigators immediately after characterization testing.

Whorton, Mark S.↗
NASA NTRS2000

g-LIMIT Status Briefing

For many microgravity science experiments in the International Space Station, the ambient acceleration environment will be exceed desirable levels. To provide a more quiescent acceleration environment to the microgravity payloads, a vibration isolation system named g-LIMIT (GLovebox Integrated Microgravity Isolation Technology) is being designed. g-LIMIT is a sub-rack level isolation system that can be tailored to a variety of applications. Scheduled for launch on the UF-1 mission, the initial implementation of g-LIMIT will be a Characterization Test in the Microgravity Science Glovebox (MSG). g-LIMIT will be available to glovebox investigators immediately after characterization testing. Standard MSG structural and umbilical interfaces will be used so that the isolation mount is transparent to the user with no additional accommodation requirements. g-LIMIT consists of three integrated isolator modules, each of which is comprised of a dual axis actuator, two axes of acceleration sensing, two axes of position sensing, control electronics, and data transmission capabilities in a minimum-volume package. In addition, this system provides the unique capability for measuring absolute acceleration of the experiment independent of accelerometers as a by-product of the control system and will have the capability of generating pristine accelerations to enhance experiment operations.

Whorton, Mark↗
DOE OSTI2023

Cutting force estimation from machine learning and physics-inspired data-driven models utilizing accelerometer measurements

Monitoring cutting forces for process control may be challenging because force measurements typically require invasive instrumentation. To remedy this situation, two new methods were recently developed to estimate cutting forces in real time based on the use of on-machine accelerometer measurements. One method uses machine learning, while another uses a physics-inspired data-driven approach, to generate a model that estimates cutting forces from on-machine accelerations. The estimated forces from both approaches were compared against cutting force data collected during various milling operations on several machine tools. The results reveal the advantages and disadvantages of each model to estimate real-time cutting forces.

Vogl, Greg↗
DOE OSTI2024

Privacy Preserving Federated Learning for Advanced Scientific Ecosystems

We present a framework to provide privacy preserving (PP) federating learning (FL) across multiple computational and experimental facilities. This work joins the compute capabilities of National Energy Research Scientific Computing Center (NERSC) and Oak Ridge National Laboratory Research Cloud (ORC) with simulated experimental data, such as those produced at the SLAC National Accelerator Laboratory and Spallation Neutron Source (SNS). We describe the software infrastructure developed to provide privacy for computational and experimental networks. We developed algorithmic privacy across the federated system by embedding database security, computation, and communication into the federation architecture, utilizing scientific tools developed by the experimental community.

Archibald, Rick [ORNL] (ORCID:0000000245389780)↗
NASA NTRS1983

Microwave and hard X-ray imaging of a solar flare on 1980 November 5

VLA and SMM hard X ray data on the solar flares of November 5, 1980 are analyzed and compared with data from other sources. The VLA provided measurements at 15 GHz at 10 sec intervals, using left and right circular polarizations with a 0.6 arcsec resolution. The hard X ray imaging spectrometer on the SMM obtained data in six bands from 3.5-30 keV, with 8 x 8 arcsec resolution and 1.5 sec separation. The data were examined for a possible nonthermal source for the microwave component of the emissions detected, the origin of 16-30 keV excess fluxes, the relation between the X ray and microwave sources, the magnetic connection between observed loops, and the physical characteristics of the radiating loop. The data were consistent with a model that assumes fast electrons are accelerated to a single power-law energy distribution and freely stream along the magnetic field. The data also agreed with a thick-target model for solar flare X ray emission.

Hoyng, P.↗
DOE OSTI2023

Diesel Particulate Filter Durability Performance Comparison Using Metals Doped B20 vs. Conventional Diesel Part I: Accelerated Ash Loading and DPF Performance Evaluation

The project objective was to generate experimental data to evaluate the impact of metals doped B20 on DPF ash loading and performance compared to that of conventional petrodiesel. Accelerated ash loading was conducted on two DPFs - one exposed to regular diesel fuel and the other to B20 containing metal dopants equivalent to 4 ppm B100 total metals (currently total metals are limited to 10 ppm in ASTM D6751, the standard for B100). Periodic performance evaluations were conducted on the DPFs at 10 g/L ash loading intervals. After the evaluations at 30 g/L, the DPF was cleaned with a commercial DPF cleaning machine and another round of DPF evaluations were conducted. A comparison of the effect of ash loading with the two fuels and DPF cleaning is presented. The metals doped B20 fuel resulted in ash that was similar to that deposited when exposed to ULSD (lube oil ash) and exhibited similar ash cleaning removal efficiency. Metals doped B20 resulted in faster ash accumulation within the DPF, as expected, but did not appear to have a negative effect on DPF pressure drop or regeneration rates (up to 30 g/L of ash) or exhibit deleterious physical effects on the DPF substrate.

ADVANCED PROPULSION SYSTEMS,BIOMASS FUELS↗
DOE OSTI2020

Generation and evaluation of synthetic patient data

Background: Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges. Methods: In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed. Results: While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases. Conclusions: We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1984

Free-flyer biostack experiment

The free-flyer biostack experiment is part of a radiobiological space research program that includes experiments in space as well as in accelerators on Earth. The program has been specially designed to increase knowledge concerning the importance, effectiveness, and hazards of the structured components of cosmic radiation to man and to any biological specimen in space. Up to now, our understanding of the ways in which HZE particles might affect biological matter is based on a few spaceflight experiments from the last Apollo missions and on the limited data available from heavy-ion irradiation from accelerators. In the near future, accelerators capable of accelerating particles up to higher atomic numbers and higher energies will promote increased activity in ground-based studies on biological effects of HZE particles. Comparison of data from such irradiation experiments on Earth with those from an actual spaceflight experiment will show any potential influence of the inevitably attendant spaceflight factors (e.g., weightlessness) on the radiobiological events.

Buecker, H.↗
DOE OSTI2026

Machine learning at the Spallation Neutron Source accelerator and target

We describe the ongoing efforts to apply Machine Learning techniques to improve the performance of our accelerator and target. Specially, we are looking to minimize halo beam losses in the absence of a proper physics model, automatically detect and log anomalies in the target support systems such as cooling, and detect and prevent errant beam pulses in the linac. We also describe the infrastructure we use to acquire and stream data to the GPU cluster for training, our code development cycle, and edge computing for model inference. To minimize halo beam losses, we use a Reinforcement Learning technique tested on a virtual accelerator. The target anomaly detection is trained on archived data using incomplete physics models and is made part of the existing target reporting system. The errant beam prevention analyzes beam current and beam phase waveforms as well as accelerator configuration data to predict errant pulses. We also develop continual learning to adapt to changes in the accelerator.

Accelerator Physics↗
DOE OSTI2025

Jipole: A Differentiable ipole-based Code for Radiative Transfer in Curved Spacetimes

Recent imaging of supermassive black holes by the Event Horizon Telescope has relied on exhaustive parameter-space searches, matching observations to large, precomputed libraries of theoretical models. As observational data become increasingly precise, the limitations of this computationally expensive approach grow more acute, creating a pressing need for more efficient methods. In this work, we present Jipole, an automatically differentiable (AD), ipole-based code for radiative transfer in curved spacetimes, designed to compute image gradients with respect to underlying model parameters. These gradients quantify how parameter changes—such as the black hole’s spin or the observer’s inclination—affect the image, enabling more efficient parameter estimation and reducing the number of required images. We validate Jipole against ipole in two analytical tests and then compare pixelwise intensity derivatives from AD with those from finite-difference methods. We then demonstrate the utility of these gradients by performing parameter recovery for an analytical model in three increasingly complex cases for the injected image: ideal, blurred, and blurred with added noise. In most cases, high-accuracy fits are obtained in only a few optimization steps, failing only in cases with extremely low signal-to-noise ratios. These results highlight the potential of AD-based methods to accelerate robust, high-fidelity model-data comparisons in current and future black hole imaging efforts.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Experimental Verification of Integrability in a Danilov-Nagaitsev Lattice using Machine Learning

In non-linear optics, achieving integrability can enhance the dynamic aperture in storage rings. We analyze turn-by-turn phase-space data from our Danilov-Nagaitsev lattice implementation at Fermilab's Integrable Optics Test Accelerator using machine learning. \textit{AI Poincar\'e} estimates conserved quantities from experimental data without prior knowledge of the invariant structure, showing qualitative agreement with theoretical predictions. Additionally, one of the two learned invariants exhibits comparable or better conservation compared to known theoretical expressions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1978

The effect of helmet loader G-cueing on pilot's simulator performance

A helmet loader has been designed and tested in a differential maneuvering simulator which provides the proper cues without restricting the pilot's movement or requiring cumbersome attachments. An experiment was conducted to determine the effect of the loader on both test pilots and operational F-15 pilots. Both the objective and subjective data from the experiment were used to determine the effect of the helmet provided acceleration cues on the simulator pilot's performance. An analysis of the obtained data indicates that the helmet-loader does have measurable effect on the pilot/simulator system. The effect is mainly seen in the transition portion of the task. The pilots significantly increase their control outputs for pitch which causes a significant increase in aircraft pitch rate.

Ashworth, B. R.↗
DOE OSTI2020

Pass-efficient methods for compression of high-dimensional turbulent flow data

The future of high-performance computing, specifically on future Exascale computers, will presumably see memory capacity and bandwidth fail to keep pace with data generated, for instance, from massively parallel partial differential equation (PDE) systems. Current strategies proposed to address this bottleneck entail the omission of large fractions of data, as well as the incorporation of in situ compression algorithms to avoid overuse of memory. To ensure that post-processing operations are successful, this must be done in a way that a sufficiently accurate representation of the solution is stored. Moreover, in situations where the input/output system becomes a bottleneck in analysis, visualization, etc., or the execution of the PDE solver is expensive, the number of passes made over the data must be minimized. In the interest of addressing this problem, this work focuses on the utility of pass-efficient, parallelizable, low-rank, matrix decomposition methods in compressing high-dimensional simulation data from turbulent flows. Additionally, a particular emphasis is placed on using coarse representation of the data – compatible with the PDE discretization grid – to accelerate the construction of the low-rank factorization. This includes the presentation of a novel single-pass matrix decomposition algorithm for computing the so-called interpolative decomposition. The methods are described extensively and numerical experiments on two turbulent channel flow data are performed. In the first (unladen) channel flow case, compression factors exceeding 400 are achieved while maintaining accuracy with respect to first- and second-order flow statistics. In the particle-laden case, compression factors of 100 are achieved and the compressed data is used to recover particle velocities. These results show that these compression methods can enable efficient computation of various quantities of interest in both the carrier and disperse phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗