Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Data exploration”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Developing Novel Performance Measures for Traffic Congestion Management and Operational Planning Based on Connected Vehicle Data

In this study, the authors present their efforts in exploring a new type of traffic data, referred to as internet-connected vehicle (ICV) data, for traffic congestion management and operational planning. Most currently manufactured vehicles contain onboard GPS and cellular modules, and they constantly connect to automobile manufacturers' clouds via cellular networks and upload their status. Some automobile manufacturers have recently redistributed the nonpersonal part of such data, such as geolocation, to third-party organizations for innovative applications. Compared with the traditional vehicle GPS data, the ICV data contain high-resolution GPS waypoints accompanied with the vehicles' abnormal moving events (e.g., hard braking). The ICV data also have huge potential in congestion management and operational planning. They explore to identify and analyze traffic congestion on both freeways and arterials using the ICV data. The ICV data adopted for this research are redistributed by Wejo Data Service, representing 10%-15% of all moving vehicles in the Dallas-Fort Worth (DFW) area in Texas. Through one case study for a freeway segment and one for an arterial segment, new traffic performance metrics based on the characteristics of ICV data have been presented. The highlights of these efforts are as follows: (I) queue length and propagation at freeway bottlenecks can be directly measured based on where and when most internet-connected vehicles slow down and join the queue; (II) an internet-connected vehicle's actual delay time on arterials can be directly measured according to its slow movement percentage, without assuming the nondelay travel speed; and (III) the ICV data set are also combined with the high-resolution traffic signal events to generate a ground-truth time-space diagram (TSD) on arterials - a common visualization of arterial signal performance for transportation planning and operations.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2000

Azimuth variation in microwave scatterometer and radiometer data over Antarctica

In this paper, we explore the use of NASA scatterometer (NSCAT) data, European remote sensing (ERS) advanced microwave instrument (AMI) scatterometer mode data, and special sensor microwave/imager (SSM/I) data to study surface roughness effects in Antarctica. All three sensors provide strong evidence of azimuth modulation, which is correlated with the surface slope environment and results in a katabatic wind flow regime.

azimuthal↗
DOE OSTI2025

Scale-up Unlearnable Examples Learning with High-performance Computing

Recent advancements in AI models, like ChatGPT, are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when radiologists use AI-driven diagnostic tools hosted on online platforms, there is a risk that medical imaging data may be repurposed for future AI training without explicit consent, spotlighting critical privacy and intellectual property concerns around healthcare data usage. Addressing these privacy challenges, a novel approach known as Unlearnable Examples (UEs) has been introduced, aiming to make data unlearnable to deep learning models. A prominent method within this area, called Unlearnable Clustering (UC), has shown improved UE performance with larger batch sizes but was previously limited by computational resources (e.g., a single workstation). To push the boundaries of UE performance with theoretically unlimited resources, we scaled up UC learning across various datasets using Distributed Data Parallel (DDP) training on the Summit supercomputer. Our goal was to examine UE efficacy at high-performance computing (HPC) levels to prevent unauthorized learning and enhance data security, particularly exploring the impact of batch size on UE’s unlearnability. Utilizing the robust computational capabilities of the Summit, extensive experiments were conducted on diverse datasets such as Pets, MedMNist, Flowers, and Flowers102. Our findings reveal that both overly large and overly small batch sizes can lead to performance instability and affect accuracy. However, the relationship between batch size and unlearnability varied across datasets, highlighting the necessity for tailored batch size strategies to achieve optimal data protection. The use of Summit’s high-performance GPUs, along with the efficiency of the DDP framework, facilitated rapid updates of model parameters and consistent training across nodes. Our results underscore the critical role of selecting appropriate batch sizes based on the specific characteristics of each dataset to prevent learning and ensure data security in deep learning applications. The source code is publicly available at https: // github. com/ hrlblab/ UE_ HPC .

Zhu, Yanfan [Vanderbilt University, Nashville, TN,↗
NASA NTRS1987

Vertical constituent transport in the mesosphere

Ground-based microwave spectroscopy measurements of mesospheric CO and H2O vertical mixing ratio profiles are used to infer vertical mixing rates in the upper mesosphere. The CO and H2O data consistently imply vertical eddy diffusion coefficients in the 70- to 85-km region of 100,000-200,000 sq cm/s during spring through summer at midlatidues. Although chemical acceleration of vertical transport is substantial for O and O3, below the mesopause, the divergences of their associated fluxes are modest, with at most a factor of 2 effect on the concentrations of O and O3 for measured variability in gravity wave activity. Comparison of Solar Mesosphere Explorer (SME) O3 data with model results reinforces the conclusions of slow vertical mixing in the upper mesosphere as a consequence of the reduced HO(x) catalytic loss of odd oxygen. The changes in chemical rate constants recommended by Rusch and Eckman (1985), in conjunction with slow vertical mixing, yield good agreement with SME O3 data. The slow vertical mixing deduced in this study is consistent with upper limits obtained from studies of the mesospheric heat budget and could be construed as evidence for an advectively controlled mesosphere. A comparison of the vertical eddy diffusion coefficients for momentum stresses, constituent transport, and heat transport suggests that the eddy Prandtl number must be of order 10.

Strobel, Darrell F.↗
NASA NTRS2001

Far Ultraviolet Spectroscopic Explorer Observations of the Seyfert 1.5 Galaxy NGC 5548 in a Low State

We present far-ultraviolet spectra of the Seyfert 1.5 galaxy NGC 5548 obtained in 2000 June with the Far Ultraviolet Spectroscopic Explorer (FUSE). Our data span the observed wavelength range 915-1185 A at a resolution of approximately 20 km s(exp -1). The spectrum shows a weak continuum and emission from O VI (lambda)(lambda)1032, 1038, C III (lambda)977, and He II (lambda)1085. The FUSE data were obtained when the AGN (Active Galactic Nuclei) was in a low state, which has revealed strong, narrow O VI emission lines. We also resolve intrinsic, associated absorption lines of O VI and the Lyman series. Several distinct kinematic components are present, spanning a velocity range of approximately 0 to -1300 km s(exp -1) relative to systemic, with kinematic structure similar to that seen in previous observations of longer wavelength ultraviolet (UV) lines. We explore the relationships between the far-UV (ultraviolet) absorbers and those seen previously in the UV and X-rays. We find that the high-velocity UV absorption component is consistent with being low-ionization, contrary to some previous claims, and is consistent with its non-detection in high-resolution X-ray spectra. The intermediate velocity absorbers, at -300 to -400 km s(exp -1), show H I and O VI column densities consistent with having contributions from both a high-ionization X-ray absorber and a low-ionization UV absorber. No single far-UV absorbing component can be solely identified with the X-ray absorber.

Brotherton, M. S.↗
DOE OSTI2021

First-Principles Study on the Electronic Properties of PDPP-Based Conjugated Polymer via Density Functional Theory

In this study, we focus on computational predictions of the electronic and optical properties of a one-dimensional periodic model of a single chain of a diketopyrrolopyrrole (DPP)-based conjugated polymer (PDPP3T) as a function of electronic configuration changes due to charge injection. Here, we employ density functional theory (DFT) to explore the ground-state and excited-state electronic properties as well as optical properties influenced by charge injection. We utilize both the Heyd–Scuseria–Ernzerhof (HSE06) and Perdew–Burke–Ernzerhof (PBE) functionals to predict the band gap and compute the absorption spectrum. Our DFT results point out that utilizing the HSE06 functional in conjunction with momentum sampling over the Brillouin zone can appropriately predict the band gap and absorption spectrum in good agreement with experimental data. Moreover, we explore the influence of charge-carrier injection on the electronic configuration of the PDPP3T polymer. Our results indicate that the injection of charge carriers into the PDPP3T semiconducting polymer model greatly affects the electrical properties and ends in a low band gap and high mobility of charge carriers in PDPP3T polymers, offering the potential to tailor the material electronic performance for organic photovoltaic and optoelectronic device applications.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
NASA NTRS1986

DE-2 mass spectrometer observations relevant to the Shuttle glow

A comparison of satellite mass spectrometer data from Dynamics Explorer-2 and Atmosphere Explorer-C and -D provides new evidence supporting the hypothesis that NO2 is the source of the observed continuum glow near ram-facing surfaces of the Space Shuttle. Surface reactions of thermospheric N and O in mass spectrometer ion sources produce NO and NO2 in amounts highly dependent on surface temperature and composition, with direct exposure of ion source surfaces to rammed gas a necessary condition for the production of large amounts of NO2. Initial orbit data indicate that a period of surface conditioning is necessary before these odd nitrogen molecules can be produced efficiently.

Engebretson, M. J.↗
DOE OSTI2023

Novel machine-learning method for spin classification of neutron resonances

The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit resonant structure whose shape is determined in part by the angular-momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is, therefore, paramount. In this project, we apply machine learning to automate the quantum number assignments using only the resonances' energies and widths and not relying on detailed transmission or capture measurements. The classifier used for quantum number assignment is trained using stochastically generated resonance sequences whose distributions mimic those of real data. Here we explore the use of several physics-motivated features for training our classifier. These features amount to out-of-distribution tests of a given resonance's widths and resonance-pair spacings. We pay special attention to situations where either capture widths cannot be trusted for classification purposes or where there is insufficient information to classify resonances by the total spin J. We demonstrate the efficacy of our classification approach using simulated and actual 52 Cr resonance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Implementing a neural network interatomic model with performance portability for emerging exascale architectures

The two main thrusts of computational science are increasingly accurate predictions and faster calculations; to this end, the zeitgeist in molecular dynamics (MD) simulations is pursuing machine learned and data driven interatomic models, e.g. neural network potentials, and novel hardware architectures, e.g. GPUs. Current implementations of neural network potentials are orders of magnitude slower than traditional interatomic models and while looming exascale computing offers the ability to run large, accurate simulations with these models, achieving portable performance for MD with new and varied exascale hardware requires rethinking traditional algorithms, using novel data structures, and library solutions. We re-implement a neural network interatomic model in CabanaMD, an MD proxy application, built on libraries developed for performance portability. Our implementation shows significantly improved thread scaling in this complex kernel as compared to a current LAMMPS implementation, across both strong and weak scaling. Our single-source solution enables simulations up to 20 million atoms on a single CPU node and 4 million atoms with improved performance on a single GPU. Furthermore, we also explore parallelism and data layout choices (using flexible data structures called AoSoAs) and their effect on performance, seeing up to ~50% and ~5% improvements in performance on a GPU by choosing the right level of parallelism and data layout respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Utility-Scale Solar, 2024 Edition: Empirical Trends in Deployment, Technology, Cost, Performance, PPA Pricing, and Value in the United States [Slides]

Berkeley Lab’s “Utility-Scale Solar, 2024 Edition” presents analysis of empirical plant-level data from the U.S. fleet of ground-mounted photovoltaic (PV), PV+battery, and concentrating solar-thermal power (CSP) plants with capacities exceeding 5 MWAC (PV plants of 5 MWAC or less, including residential rooftop systems, are covered separately in Berkeley Lab’s companion annual report, Tracking the Sun). Key findings from this year’s report include: -18.5 GWAC of new utility-scale PV capacity came online in 2023, bringing cumulative installed capacity to more than 80.2 GWAC across 47 states. Installed costs continued to fall in 2023. Relative to 2022, capacity-weighted averages decreased by 8% to -$\$1.43$/WAC (or $\$1.08$/WDC). Costs, based on a 7.1 GWAC sample of 76 plants completed in 2023, have fallen by 75% (averaging 10% annually) since 2010. Plant-level capacity factors vary widely, from 6% to 36% (on an AC basis), with a sample median of 24%. -Levelized cost of energy (LCOE) of new 2023 projects increased slightly to $\$46$/MWh prior to the application of tax credits but continued to fall to $\$31$/MWh when accounting for federal incentives. PPA prices have largely followed the decline in solar’s LCOE over time, but newly signed longer-term PPA prices have increased since 2021, to an average of $\$35$/MWh (levelized, in 2023 dollars). -Solar’s average energy and capacity value (i.e., ability to offset costs of other power generation sources) across the U.S. was $\$45$/MWh in 2023. Solar’s average market value was lowest in CAISO ($\$27$/MWh), the market with the greatest solar generation share, and highest in ERCOT ($\$67$/MWh). -Newer solar projects had greater market value in 2023 than their generation costs, yielding $\$1.1$ billion in benefits. Projects built in 2022 delivered on average $\$15$/MWh more market value than their costs in 2023. -Solar’s combined value from wholesale electricity markets, public health and climate damage reduction were greater than generation costs and incentives, yielding $\$13.7$ billion in net benefits in 2023. We estimate U.S. health benefits of $\$24$/MWh and reduced global climate damages of $\$101$/MWh. -Adding battery storage is one way to increase the value of solar. Deployment of 52 new PV+battery hybrid plants set a record with 5.3 GW installed in 2023. Our public data file tracks metadata and PPA prices from more than 100 PV+battery hybrid projects that are already online or that have secured offtake arrangements. -Looking ahead, a massive pipeline of at least 1,085 GW of solar capacity dominates the nation’s interconnection queues at the end of 2023. Nearly 571 GW, or 53%, of that total was paired with a battery – in CAISO it was a staggering 98%. Historically only 10% of the requested solar capacity is built. -For more information, and to explore related interactive data visualizations, go to utilityscalesolar.lbl.gov.

14 SOLAR ENERGY↗
DOE OSTI2020

Distribution Development for the RDX Regional Model at Los Alamos National Laboratory - 20385

Representing uncertainty in model inputs often means finding a balance between uncertainty and physical reality. Developing wide distributions may seem conservative in principle, but this approach may lead to unrealistic model results. Characterizing the current state of knowledge of stochastic inputs presents many challenges, especially if the data available are limited or have limited relevance to the site. Relationships among these inputs may also be important to represent but are typically complex or difficult to define. Often special adjustments must be made to account for reduced credibility in particular data. If parameters are strongly related to one another, a correlation structure may be developed for input to the model. Other techniques such as regression models may be used to incorporate relationships between the information available and the desired parameters. The process of developing distributions must consider details of the model in terms of what the distribution is meant to represent. This paper uses the example of a probabilistic fate and transport model for hexahydro-1,3,5-trinitro-1,3,5-triazine (RDX) in the regional aquifer at Los Alamos National Laboratory (LANL). For many parameters, a single draw is applied to all space and time over which the model is run, for a single iteration. This simplification is often made for many reasons, and can often be beneficial, but also adds additional complexity in the distribution development process. Defining the distributional goals as they relate to the modeling process is an important step, which should take place prior to evaluation of the data. Usually, the distributions developed are meant to characterize the average value of the parameter over the spatial and temporal domain of the model. Distribution development requires consideration of many sources of information on the parameter where available, ideally from multiple references. Examples of different sources include data from different references but also from different conditions, measurement methods, or experimental types. Depending on these conditions and the reliability or relevance of particular references, different sources of data may each contribute valuable information but have varying relevance to the site. In these cases, weighting data unequally is a useful way to incorporate this information. As an example, aqueous dispersivity data are available for a variety of rock types. Only a few values are available for the desired rock type, and this is not enough to develop a distribution. Therefore, dispersivity values from other rock types are included in distribution development but are down-weighted such that the best data have the most influence on the distribution developed. In another case, K{sub d} distributions in the model are meant to represent a known composition of multiple soil types. Data from these materials are weighted accordingly to develop a distribution for the weighted average K{sub d} across soil types. Other cases include varying reliability of different sources, and weighting data according to the confidence in these sources. In some cases, input parameters are correlated with one another. An example is advective porosity, which is positively correlated with total porosity and must be less than total porosity. Paired data with both parameters must exist to discern the relationship between the two parameters and if it is necessary to build a correlation structure into the model. In general, correlated parameters may be represented in the model by a multivariate distribution, or perhaps more desirably, capturing the correlation within the developed distributions which may be treated as independent from one another. In the example of porosity, this can be done by transforming advective porosity into a proportion of total porosity which may be drawn independently from the distribution of total porosity. This paper explores how complex data and correlations can be incorporated to meet the distributional goals of the model, using the RDX regional model as a detailed example. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

The NASA DEVELOP Model of Community Science & Engagement: Localizing Earth Science Information

The NASA DEVELOP Program conducts 10 week feasibility studies that apply Earth observation data to address community priorities and support informed decision making. Part of NASA’s Earth Action Capacity Building Program, DEVELOP builds skills in both participants ( recent graduates, and early/transitioning career professionals), who work on small interdisciplinary project teams, and partner organizations (state and local governments, federal agencies, non profit and for profit organizations, universities, and international organizations) that work closely with DEVELOP to design the project. Projects address a wide variety of environmental issues, such as disaster risk and resilience, air pollution, and the impact of urban development, with a growing number of projects exploring how satellite data can help inform decision making around environmental injustices. A subset of DEVELOP partner organizations are local municipalities or community led non profits, with the co production model serving as an effective engagement tool and an introduction for communities to engage in Earth science research and become familiar with satellite remote sensing. This presentation will highlight the DEVELOP co production model, community project use cases, and lessons learned in partnering with local communities.

Michael Pazmino↗
NASA NTRS1988

Parameter identification methods for improving structural dynamic models

There is an increasing need to develop Parameter Identification methods for improving structural dynamic models, based on the inability of engineers to produce mathematical models which correlate with experimental data. This research explores the efficiency of combining Component Mode Synthesis (substructuring) methods with Parameter Identification procedures in order to improve analytical modeling of structural components and their connections. Improvements are computed in terms of physical stiffness and damping parameters in order that the physical characteristics of the model can be better understood. Connections involving both viscous and friction damping are investigated. Substructuring methods are utilized to reduce the complexity of the identification problem. Component and inter-component structural connection properties are evaluated and identified independently, thus simplifying the identification problem. It is shown that modal test data is effective for identifying modeling problems associated with structural components, and for determining the stiffness and damping properties of intercomponent connections. In general, Parameter Identification is improved when greater quantities of experimental data are available.

Lawrence, Charles↗
DOE OSTI2024

Molecular Dynamical and Quantum Mechanical Exploration of the Site-Specific Dynamics of Cy3 Dimers Internally Linked to dsDNA

Performing spectroscopic measurements on biomolecules labeled with fluorescent probes is a powerful approach to locating the molecular behavior and dynamics of large systems at specific sites within their local environments. The indocarbocyanine dye Cy3 has emerged as one of the most commonly used chromophores. The incorporation of Cy3 dimers into DNA enhances experimental resolution owing to the spectral characteristics influenced by the geometric orientation of excitonically coupled monomeric units. Various theoretical models and simulations have been utilized to aid in the interpretation of the experimental spectra. In this study, we employ all-atom molecular dynamics simulations to study the structural dynamics of Cy3 dimers internally linked to the dsDNA backbone. We used quantum mechanical calculations to derive insights from both the linear absorption spectra and the circular dichroism data. Furthermore, we explore potential limitations within a commonly used force field for cyanine dyes. The molecular dynamics simulations suggest the presence of four possible Cy3 dimeric populations. The spectral simulations on the four populations show one of them to agree better with the experimental signatures, suggesting it to be the dominant population. Furthermore, the relative orientation of Cy3 in this population compares very well with previous predictions from the Holstein–Frenkel Hamiltonian model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1994

Statistical study of ULF wave occurrence in the dayside magnetosphere

Ultralow-frequency (ULF) waves are observed almost everywhere in the dayside magnetosphere. The mechanism by which these waves are generated and transformed in the dayside magnetosphere is still not understood. Here we report a statistical study of these waves based on magnetic field data from the International Sun-Earth Explorer 1 (ISEE 1) spacecraft. Data from the first traversal of the spacecraft through the entire dayside magnetosphere have been examined to determine the spatial distribution of wave occurrence. Successive 20-min segments of data were transformed to a field-aligned coordinate system. The parallel component was detrended and all three components of the field spectrally analyzed. Wave occurrence was defined by the presence of significant peaks in the power spectra. Wave events were categorized by three wave frequency bands: Pc 3 with T approximately 10-45 s; Pc 4 with T approximately 45-150 s; the short-period part of the Pc 5 wave band with T approximately 150-324 s. Properties of the spectral peaks were then entered into a data base. The data base was next sorted to determine the spatial occurrence pattern for the waves. Our results show that Pc 3 waves most frequently occur just outside synchronous orbit and are approximately centered on local noon. Pc 4 waves have a similar distribution with its peak further out. Pc 5 waves have high occurrence rate at the two flanks of the magnetosphere. Peaks in spectra obtained near the magnetopause are less clearly defined than those deeper in the magnetosphere.

Cao, M.↗
NASA NTRS2013

Supersonic Pitch Damping Predictions of Blunt Entry Vehicles from Static CFD Solutions

A technique for predicting supersonic pitch damping of blunt axisymmetric bodies from static CFD data is presented. The contributions to static pitching moment due to forebody and aftbody pressure distributions are broken out and considered separately. The one-dimension moment equation is cast to model the separate contributions from forebody and aftbody pressures with no traditional damping term included. The aftbody contribution to pitching moment is lagged by a phase angle of the natural oscillation period. This lag represents the time for aftbody wake structures to equilibrate while the body is oscillation. The characteristic equation of this formulation indicates that the lagged backshell moment adds a damping moment equivalent in form to a constant pitch damping term. CFD calculations of the backshell's contribution to the static pitching moment for a range of angles-of-attack is used to predict pitch damping coefficients. These predictions are compared with ballistic range data taken of the Mars Exploration Rover (MER) capsule and forced oscillation data of the Mars Viking capsule. The lag model appears to capture dynamic stability variation due to backshell geometry as well as Mach number.

Schoenenberger, Mark↗
NASA NTRS1973

Plasma sheet at lunar distance - Characteristics and interactions with the lunar surface

The plasma sheet at lunar distance is investigated with the use of data from the charged particle lunar environment experiment (CPLEE), complemented with data from the Explorer 35/ARC magnetometer. It is shown that the presence of the lunar surface does not appreciably affect measurements of the plasma sheet characteristics by the lunar-based CPLEE instrument. In particular, the lunar surface generally does not shadow plasma sheet particles. This may be due to rapid random passage (greater than 40 km/sec) of magnetotail field lines with respect to the lunar surface or to diffusion of plasma sheet electrons into the flux tubes in contact with the lunar surface. The plasma sheet is generally observed as a rapid increase in observed particle fluxes and a simultaneous decrease in field strength. A statistical analysis of the CPLEE data shows that the plasma sheet in the midnight sector has a thickness of 5 R sub E plus or minus 2 R sub E. Geomagnetic activity reduces the probability of encounters between the moon and the plasma sheet.

Rich, F. J.↗