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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Pion and kaon structure functions at an electron-ion collider

Pions and kaons are, along with protons and neutrons, the main building blocks of nuclear matter. They are connected to the Goldstone modes of dynamical chiral symmetry breaking, the mechanism thought to generate all hadron mass in the visible universe. The distribution of the fundamental constituents, the quarks and gluons, is expected to be different in pions, kaons, and nucleons. However, experimental data are sparse. As a result, there has been persistent doubt about the behaviour of the pion's valence quark structure function at large Bjorken-$x$ and virtually nothing is known about the contribution of gluons. The Electron-Ion Collider (EIC) with an acceptance optimized for forward physics could provide access to structure functions over a larger kinematic region. This would allow for measurements testing if the origin of mass is encoded in the differences of gluons in pions, kaons, and nucleons, and measurements that could serve as a test of assumptions used in the extraction of structure functions and the pion and kaon form factors. Furthermore, measurements at an EIC would also allow to explore the effect of gluons at high x.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Moment extraction using an unfolding protocol without binning

Deconvolving (“unfolding”) detector distortions is a critical step in the comparison of cross-section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our moment unfolding technique uses machine learning and is inspired by Boltzmann weight factors and generative adversarial networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our moment unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

W2VPCA: A Machine Learning Method for Measuring Attitudes With Natural Language

Company strategy influences many decisions in freight transportation. Behavioral models of company decision-making therefore could benefit from including strategy variables. However, strategy is difficult to observe and quantify. Attitudinal surveys of company executives can be used to collect measurements of latent strategy to use in quantitative models. However, surveys are costly and burdensome. Text mining methods to collect measurements overcome these issues somewhat, but typically require manual intervention and ignore the context of words, which can be problematic. This study introduces a new machine learning method to generate strategy measurement data from existing big text data. The new method, called W2VPCA, combines Natural Language Processing and Principal Components Analysis. W2VPCA produces measurement data that serve as quantitative indicators of latent strategy in behavioral models. W2VPCA is unsupervised, data-driven, and uses information on word context. We apply W2VPCA to generate measurements of latent strategies using readily available, large-scale text data: annual company reports. The empirical measurements are used successfully to associate two latent strategies, one focusing on distribution and the other on products, with truck fleet and distribution center outsourcing decisions. The main empirical outcome is that the W2VPCA measurements outperform Bag-of-Words measurements in a psychometric analysis of latent firm strategies. While this study focuses on freight behavioral models, W2VPCA may also have applications in behavioral modeling in other domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Using Signal Clustering Similarity for Detecting CAN Masquerade Attacks

The computer code assumes that time series representing the physical signals of the vehicle have been extracted from the CAN bus. The main input of the computer code is the multivariate time series representation of the signals in the CAN bus. The computer code cluster these time series using agglomerative hierarchical clustering from benign and attack datasets. Based on this, it generates probability distributions from the similarity of the obtained clusters based in each scenario---benign and attack---using the CluSim method (https://github.com/Hoosier-Clusters/clusim). Finally, it compares how a new data collection compares with the previous distribution to provide and probability score for an intrusion.

Moriano, Pablo↗
DOE OSTI2020

Ordovician-Cambrian Units: Hierarchical Evaluation of Geologic Carbon Storage Resource Estimates

The Indiana Geological and Water Survey (IGWS) led subtask 1.1 to assess the regional distribution and estimate the storage capacity of Ordovician-Cambrian stratigraphic units located within the partnership region. A comprehensive data set of wireline logs and petrophysical information was used to generate these interpretations. These data include core analysis for porosity and permeability, mercury injection capillary pressure (MICP), and existing well data including location and stratigraphic information. This report includes storage resource estimates (SREs) for three potential storage reservoirs(limestone and dolostone from the Upper Ordovician Trenton Limestone/Black River Group and equivalent units, the Middle Ordovician St. Peter Sandstone, and primary target reservoir rocks of the Lower Ordovician and Upper Cambrian Knox Supergroup and equivalent units) calculated using six methodologies: (1) a fixed value of porosity of 10 percent in all units evaluated; (2) a unique average porosity (per well) from wireline-derived porosity (neutron, sonic, and/or density porosity for each unit); (3) porosity values from core analysis; (4) a depth-dependent porosity model (Knox Supergroup only); (5) porosity based on a model based on petrophysical facies; and (6) SREs using National Energy Technology Laboratory’s CO2 Storage prospeCtive Resource Estimation Excel aNalysis (CO2-SCREEN beta V2). All methods used the same values for thickness for each unit. However, the areal extent of each assessment was limited by the data available for each method. Estimated volumes were calculated in 1-by-1 kilometer grid cells and summarized as county and total stratigraphic unit volumes. The resultant SREs mass are displayed using boxplots, which allow for comparing data statistics (mean values and variability) between methods. Differences observed in SRE results from the six methods are mainly attributable to differences in the data and conceptual models used to interpret or estimate porosity in each method. Based on this systematic variability between methods, it is inferred that methods 1, 4, and 6 are best used for regional-scale reconnaissance estimates of storage capacity while methods 2, 3, and 5 are more appropriate for local scales where more data is required. All estimates are data-density dependent and different methods require different amounts of data for reasonable assessments. ArcMap 10.5.1 software was used to portray SREs to help visualize spatial variance of estimates for each methodology, and more importantly, to highlight those areas having the greatest total storage potential estimates.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Damage and Failure in a Statistical Crack Model

In this paper I will take a close look at a statistical crack model (SCM) as is used in engineering computer codes to simulate fracture at high strain rates. My general goal is to understand the macroscopic behavior effected by the microphysical processes incorporated into an SCM. More specifically, I will assess the importance of including local interactions between cracks into the growth laws of an SCM. My strategy will be to construct a numerical laboratory that represents a single computational cell containing a realization of a statistical distribution of cracks. The cracks will evolve by the microphysical models of the SCM, leading to quantifiable damage and failure of the computational cell. I will use the numerical data generated by randomly generated ensembles of the fracture process to establish scaling laws that will modify and simplify the implementation of the SCM into large scale engineering codes.

42 ENGINEERING↗
NASA NTRS1979

Chorus, energetic electrons and magnetospheric substorms

The paper investigates possible chorus generation mechanisms by means of simultaneous OGO-5 measurements of chorus, energetic (79 + or - 23 keV) electron fluxes and pitch angle distributions, and ambient magnetic fields. Further, the solar wind data during high-latitude dayside chorus events are examined. It is reported that the highly eccentric orbit had an apogee of 24 earth radii and an inclination of 30 deg to the geographic equator.

Tsurutani, B. T.↗
NASA NTRS1982

The global distribution of water vapor in the middle atmosphere of Venus

Near-IR measurements are presented of the mean vertical and horizontal distribution of water vapor in the Venus clouds as measured by the Pioneer Venus Orbiter IR radiometer, and comparisons are made with previous data. Six thermal channels were used to generate several hundred thousand readings for determination of the mean mixing ratio. Averaging was performed as a function of the solar zenith angle, with profiles retrieved with a relaxation method applied to radiance data at 45 microns. Consideration was given to mean cloud models and temperature profiles obtained from the five temperature sounding channels scanning from 11.5-15 microns. Laboratory tests were effected to validate the transmission functions. The results included a maximum column abundances above the cloud optical depth in the early afternoon in the equatorial regions. Mixing ratio enhancement was highest on the dayside and at high altitudes, with a mean ratio of 0.0001 at a 40% uncertainty level. Day-to-day fluctuations in the pressure level at 11.5 microns was larger than 10%, far below the factors of 2-3 determined by other investigators.

Schofield, J. T.↗
NASA NTRS1983

A study of the kinetic energy generation with general circulation models

The history data of winter simulation by the GLAS climate model and the NCAR community climate model are used to examine the generation of atmospheric kinetic energy. The contrast between the geographic distributions of the generation of kinetic energy and divergence of kinetic energy flux shows that kinetic energy is generated in the upstream side of jets, transported to the downstream side and destroyed there. The contributions from the time-mean and transient modes to the counterbalance between generation of kinetic energy and divergence of kinetic energy flux are also investigated. It is observed that the kinetic energy generated by the time-mean mode is essentially redistributed by the time-mean flow, while that generated by the transient flow is mainly responsible for the maintenance of the kinetic energy of the entire atmospheric flow.

Chen, T.-C.↗
NASA NTRS1985

Preliminary design of the redundant software experiment

The goal of the present experiment is to characterize the fault distributions of highly reliable software replicates, constructed using techniques and environments which are similar to those used in comtemporary industrial software facilities. The fault distributions and their effect on the reliability of fault tolerant configurations of the software will be determined through extensive life testing of the replicates against carefully constructed randomly generated test data. Each detected error will be carefully analyzed to provide insight in to their nature and cause. A direct objective is to develop techniques for reducing the intensity of coincident errors, thus increasing the reliability gain which can be achieved with fault tolerance. Data on the reliability gains realized, and the cost of the fault tolerant configurations can be used to design a companion experiment to determine the cost effectiveness of the fault tolerant strategy. Finally, the data and analysis produced by this experiment will be valuable to the software engineering community as a whole because it will provide a useful insight into the nature and cause of hard to find, subtle faults which escape standard software engineering validation techniques and thus persist far into the software life cycle.

Campbell, Roy↗
NASA NTRS1995

A Neural Network/Acoustic Emission Analysis of Impact Damaged Graphite/Epoxy Pressure Vessels

Acoustic emission (AE) signal analysis has been used to measure the effects of impact damage on burst pressure in 5.75 inch diameter, inert propellant filled, filament wound pressure vessels. The AE data were collected from fifteen graphite/epoxy pressure vessels featuring five damage states and three resin systems. A burst pressure prediction model was developed by correlating the AE amplitude (frequency) distribution, generated during the first pressure ramp to 800 psig (approximately 25% of the average expected burst pressure for an undamaged vessel) to known burst pressures using a four layered back propagation neural network. The neural network, trained on three vessels from each resin system, was able to predict burst pressures with a worst case error of 5.7% for the entire fifteen bottle set.

Walker, James L.↗
NASA NTRS2002

MODIS Observations of Smoke and Fires

The MODIS (Moderate Resolution Imaging Spectroradiometer) instruments collect daily measurements of our planet since early 2000 from the Terra spaceborne polar platform. It has unique channels to observe smoke over land and ocean and to observe fires. Using unsaturated channels at 3.9 micron MODIS detects the fires and estimates the fire radiative energy. Using solar channels in the visible (0.47 and 0.66 micron) and in the mid IR (2.1 micron) MODIS measures the smoke optical thickness distribution and evolution over the land. Seven Channels in the solar spectrum are used to detect the smoke properties and distribution over the oceans. Data from the Aerosol Robotic Network, are used to validate the MODIS observations. The MODIS aerosol data presented in a movie form is used to observe the generation of smoke plumes and their dispersion around the globe. For example a key conclusion is that smoke in particular from Southern Africa can pollute significantly the 'pristine' Southern Hemisphere zonal range of 45'S-60'S, and the Northern Pacific.

Kaufman, Yoram↗
NASA NTRS2003

Continuous Improvement in Battery Testing at the NASA/JSC Energy System Test Area

The Energy Systems Test Area (ESTA) at the Lyndon B. Johnson Space Center in Houston, Texas conducts development and qualification tests to fulfill Energy System Division responsibilities relevant to ASA programs and projects. EST A has historically called upon a variety of fluid, mechanical, electrical, environmental, and data system capabilities spread amongst five full-service facilities to test human and human supported spacecraft in the areas of propulsion systems, fluid systems, pyrotechnics, power generation, and power distribution and control systems. Improvements at ESTA are being made in full earnest of offering NASA project offices an option to choose a thorough test regime that is balanced with cost and schedule constraints. In order to continue testing of enabling power-related technologies utilized by the Energy System Division, an especially proactive effort has been made to increase the cost effectiveness and schedule responsiveness for battery testing. This paper describes the continuous improvement in battery testing at the Energy Systems Test Area being made through consolidation, streamlining, and standardization.

Boyd, William↗
NASA NTRS2018

NASA GSFC Avionics Architectures and Future Directions

NASA Goddard Spaceflight Center (GSFC) employs standard avionics hardware and software architectures. Hardware architectures include GMSA (Goddard Modular Smallsat Architecture), MUSTANG (Modular Unified Space Technology Avionics for Next Generation), SpaceCube. The spaceflight software architecture is based on cFS (Core Flight System). Future driving requirements for avionics architectures include increase sensor data rates, increased onboard processing, autonomous applications, and distributed space missions. Architectural concepts that can meet these requirements include the use of the High Performance Spaceflight Computing (HPSC) Chiplet, employing hybrid computing architectures, and increasing network bandwidths.

architectures↗
NASA NTRS2020

Winter Arctic Sea Ice Thickness from ICESat-2 Freeboards

NASA’s ICESat-2 mission was launched in September 2018 with the primary goal of monitoring our rapidly changing polar regions. The sole instrument onboard, the Advanced Topographic Laser Altimeter System (ATLAS), is now providing routine, very high-resolution, surface elevation data across the globe, including the Arctic and Southern Oceans. In this study we demonstrate our new processing chain for converting the along-track ICESat-2 sea ice freeboard product (ATL10) into sea ice thickness, focusing our initial efforts on the Arctic Ocean. For this conversion we primarily make use of snow depth and density data from the NASA Eulerian Snow on Sea Ice Model (NESOSIM). The coarse resolution (~100 km) snow data are redistributed onto the high resolution (~30 - 100 m) ATL10 freeboards using relationships obtained from snow depth and freeboard data collected by NASA’s Operation IceBridge mission. We present regional sea ice thickness distributions and highlight their seasonal evolution through our first winter-season of data collection. We include ice thickness uncertainty estimates, while also acknowledging the limitations of these estimates. We generate a gridded monthly thickness product and compare this with various monthly sea ice thickness estimates obtained from ESA’s CryoSat-2 satellite mission, with ICE 33 Sat-2 showing consistently lower thicknesses. Finally, we compare our February/March 2019 thickness estimates to ICESat February/March (19th February – 21st March) 2008 ice thickness estimates using the same input assumptions, which show a ~0.40 m or ~21% thinning across an inner Arctic Ocean domain in this 11-year time period.

Alek A Petty↗
DOE OSTI2026

A Physics-Based Digital Twin for Wave Elevation and Seabed Moment Estimation of Offshore Monopiles: Preprint

In this work, we present a proof of concept of a physics-based digital twin for a monopile structure (with overhead inertia) subjected to wave loading. The digital twin is formulated using reduced-order models derived from first principles and combined with a Kalman filter for state estimation. The proposed framework estimates the monopile top motion, the wave elevation, and the section forces and moments along the pile using primarily acceleration measurements at the monopile top. Key innovations include the use of a hydrodynamic shape function to represent distributed wave loading in a compact and computationally efficient manner, and the introduction of a shaping filter to augment the state-space with wave kinematics. Synthetic measurement data are generated using OpenFAST and used as a reference to assess the performance of the digital twin. Results demonstrate that the wave elevation can be accurately reconstructed without direct sea-state measurements as long as the wave regime is inertia-dominated. Under the ideal tested conditions, the total hydrodynamic force and sea-bed bending moment are estimated with relative errors on the order of 1% and correlation coefficients exceeding 96%. Future work will evaluate the estimator's performance under operational uncertainties and more complex loading conditions.

17 WIND ENERGY↗
DOE OSTI2021

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms. In this paper, a new method, called the pruned physics-aware neural network (P2N2), is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one- year smart meter data of load consumptions for three-phase power flow is developed to generate the measurement and voltage state data. The IEEE 123-node system is selected as the test network to benchmark the proposed algorithm against the classic weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS in terms of data redundancy and estimation accuracy.

distribution system state estimation↗
DOE OSTI2025

$\mathrm{SageNet}$: Fast Neural Network Emulation of the Stiff-amplified Gravitational Waves from Inflation

Accurate modeling of the inflationary gravitational waves (GWs) requires time-consuming, iterative numerical integrations of differential equations to take into account their backreaction on the expansion history. To improve computational efficiency while preserving accuracy, we present the Stiff-amplified Gravitational-wave Emulator Network (SageNet), a deep learning framework designed to replace conventional numerical solvers (code available at https://github.com/YifangLuo/SageNet). SageNet employs a long short-term memory architecture to emulate the present-day energy density spectrum of the inflationary GWs with possible stiff amplification, Ω GW (f). Trained on a data set of 25,689 numerically generated solutions, SageNet allows accurate reconstructions of Ω GW (f) and generalizes well to a wide range of cosmological parameters; 90.9% of the test emulations with randomly distributed parameters exhibit errors of under 4%. In addition, SageNet demonstrates its ability to learn and reproduce the artificial, adaptive sampling patterns in numerical calculations, which implement denser sampling of frequencies around changes in spectral indices in Ω GW (f). The dual capability of learning both physical and artificial features of the numerical GW spectra establishes SageNet as a robust alternative to exact numerical methods. Finally, our benchmark tests show that SageNet reduces the computation time from tens of seconds to milliseconds, achieving a speedup of ∼10 4 times over standard CPU-based numerical solvers with the potential for further acceleration on GPU hardware. These capabilities make SageNet a powerful tool for accelerating Bayesian inference procedures for extended cosmological models. In a broad sense, the SageNet framework offers a fast, accurate, and generalizable solution to modeling cosmological observables whose theoretical predictions demand costly differential equation solvers.

Astronomy data modeling↗