Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “distribution selection”

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 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

3002 Humidified Tandem Differential Mobility Analyzer (HTDMA) Instrument Handbook

The Brechtel Manufacturing Inc. (BMI) Humidified Tandem Differential Mobility Analyzer (HT-DMA Model 3002) (Brechtel and Kreidenweis 2000a,b, Henning et al. 2005, Xerxes et al. 2014) measures how aerosol particles of different initial dry sizes grow or shrink when exposed to changing relative humidity (RH) conditions. It uses two different mobility analyzers (DMA) and a humidification system to make the measurements. One DMA selects a narrow size range of dry aerosol particles, which are exposed to varying RH conditions in the humidification system. The second (humidified) DMA scans the particle size distribution output from the humidification system. Scanning a wide range of particle sizes enables the second DMA to measure changes in size or growth factor (growth factor = humidified size/dry size), due to water uptake by the particles. A Condensation Particle Counter (CPC) downstream of the second DMA counts particles as a function of selected size in order to obtain the number size distribution of particles exposed to different RH conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Advanced Research Projects Agency - Energy Annual Report for FY 2014

This report presents a summary of the activities of the Advanced Research Projects Agency-Energy (ARPA-E) during Fiscal Year 2014 (FY2014). In FY2014, ARPA-E issued funding solicitations and selected projects for three new programs covering a broad array of energy technologies, including: 27 million dollars to develop next-generation power switching devices that improve energy efficiency in a wide range of applications; 30 million dollars for hybrid solar energy beyond current photovoltaic (PV) and concentrated solar power (CSP) technologies; and 33 million dollars for electrochemical technologies to enable low-cost distributed power generation. ARPA-E released three additional funding opportunities in FY2014 with project selections that were ultimately announced in FY2015. These programs include: 30 million dollars for localized heating and cooling devices to expand temperature ranges within buildings reducing energy consumption; 30 million dollars for low-cost, high-sensitivity systems to detect and measure methane emissions; and 30 million dollars for low-cost tools to aid in the future development of fusion power. ARPA-E also continued the use of a rolling open solicitation to quickly support innovative applied energy research that has the potential to lead to new focused programs. In addition to these new programs, ARPA-E hosted the fifth annual Energy Innovation Summit from February 24-26, 2014. The Summit brought together leaders from academia, government, and business to discuss the foremost energy issues, showcase the latest technology innovations, and cultivate relationships to help advance cutting-edge technologies to market. The event drew over 2,100 attendees and featured over 120 speakers and keynote addresses. At the Summit, ARPA-E announced at least 24 ARPA-E project teams that have formed new companies and more than 16 ARPA-E projects, which have partnered with other government agencies for further development. Additionally, 22 ARPA-E projects have attracted more than 625 million dollars in private-sector follow-on funding after ARPA-E’s investment of approximately 95 million dollars. Lastly, ARPA-E continued to focus on providing awardees with practical training and critical business information as part of the Agency’s Technology-to-Market program. This support equips projects with a clear understanding of market needs to guide technical development and help projects succeed the marketplace.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Boundary-Aware Adversarial Learning Domain Adaption and Active Learning for Cross-Sensor Building Extraction

The use of convolutional neural networks (CNNs) for building extraction from remote sensing images has been widely studied and many public datasets have been made available for accelerating development of these CNN models. Yet adapting pretrained models at scale in real-world scenarios remains a challenging task. The main barrier is that certain new labels are still needed to compensate for domain shifting between the labeled data and new images that potentially cover new geographic locations or that are from a different sensor. In this article, we propose to add informatively labeled samples from a new image pool under the paradigm of active learning. To select the most useful samples based on model uncertainty, we first tackle the problem of uncalibrated uncertainty estimation due to distribution shifting by adapting feature extractors with boundary-based adversarial learning. Calibrated uncertainty is used as the query criterion in the active learning process, where the most uncertain samples are selected for annotation and included for model retraining. The proposed workflow was tested with three data pairs in which each workflow represents a scenario often encountered in real-world applications, including adapting pretrained models to new images collected with different sensors or to new geographic areas where appearances and types of buildings are very different. Compared to several baselines, including random sampling, temperature scaling (a well-known uncertainty calibration technique), different query strategies, and active domain adaptation methods, the proposed workflow shows that strategically querying a smaller set of samples for labeling achieves comparable or better building extraction performance. The proposed method reduces the number of labeled samples required to achieve sufficient model accuracy, thus significantly reducing hundreds of person-hours for labeled data creation. In addition, we include a few considerations when deploying this workflow in a GPU cluster that can be easily adapted to achieve operational building extraction model retraining.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1995

The bright knots at the tops of soft X-ray flare loops: Quantitative results from Yohkoh

Soft X-ray Telescope (SXT) observations from the Japanese Yohkoh spacecraft have shown that confined bright regions are common features at the tops of flare loops throughout most of the duration of the flares. In this paper we present quantitative results for these flare knots, in relation to other flare regions, for four relatively 'simple' flares. Emission measure distributions, electron temperatures, and electron densities are derived from SXT and Yohkoh Bragg Crystal Spectrometer (BCS) observations. The four flares selected are dominated by what appear to be single-loop structures, with bright knots at the loop tops. The flares are neither long-duration nor impulsive events. The spatial distributions of brightness and emission measure in the flares are found to be quite similar for all four events, even though there are significant differences in dynamical behavior between at least two of the events. Temperatures and densities calculated for these flares are consistent with previous results from many solar experiments. An investigation of intensity correlations between adjacent pixels at the tops of the loops suggests the existence of local disturbances in the magnetic loops that occur on spatial scales less than the radii of the loops.

Doschek, G. A.↗
NASA NTRS1995

Shadowing the soft x-ray background by infrared cirrus: A study of selected regions

Using the ROSAT and IRAS data achieves, we studied shadows in the diffuse 0.25 keV background toward infrared cirrus clouds to infer the distribution of the hot interstellar medium (ISM). Surprisingly, we found that anticorrelation between infrared emission from cirrus clouds and background x-ray intensity was present in all five galactic latitude (b approx. greater than 25 deg) fields that we selected. In two fieds, the anticorrelation is more than 30% deep, and shows particularly convincing evidence that they are due to x-ray absorption by the clouds. Assuming an average density for the local x-ray-emitting gas (T approximately 10(exp 6) K), we estimated the minimum distances to the clouds based on our best-fit x-ray intensities originating in front of the clouds. These distances are in the range of 60 to 160 pc. Therefore, we conclude that there are a considerable number of degree-size, x-ray-absorbing gas clouds at approximately 10(exp 2) pc and that large amounts of x-ray-emitting gas exist in regions beyonds these clouds.

Wang, Q. Daniel↗
DOE OSTI2019

Using Ultramicroporous Carbon for the Selective Removal of Nitrate with Capacitive Deionization

The contamination of water resources with nitrate is a growing and significant problem. Here we report the use of ultramicroporous carbon as a capacitive deionization (CDI) electrode for selectively removing nitrate from an anion mixture. Through moderate activation, we achieve a micropore-size distribution consisting almost exclusively of narrow (<1 nm) pores that are well suited for adsorbing the planar, weakly hydrated nitrate molecule. Cyclic voltammetry measurements reveal an enhanced capacitance for nitrate when compared to chloride as well as significant ion sieving effects when sulfate is the only anion present. We measure high selectivities ( S ) of both nitrate over sulfate ( S NO 3 /SO 4 = 17.8 ± 3.6 at 0.6 V) and nitrate over chloride ( S NO 3 /Cl = 6.1 ± 0.4 at 0.6 V) when performing a constant voltage CDI separation on 3.33 mM/3.33 mM/1.67 mM Cl/NO 3 /SO 4 feedwater. Furthermore, these results are particularly encouraging considering that a divalent interferant was present in the feed. Using molecular dynamics simulations, we examine the solvation characteristics of these ions to better understand why nitrate is preferentially electrosorbed over sulfate and chloride.

36 MATERIALS SCIENCE↗
DOE OSTI2024

SCALE 6.3 Validation: Radiation Shielding

Safe and reliable use of scientific and engineering computer codes requires validation for the types of applications in which they will be used. An example in the nuclear reactor engineering and licensing field is radiation transport employed in shielding analyses. The validity of computer codes for shielding applications is demonstrated in this report for SCALE version 6.3.0. Representative benchmarks corresponding to shielding analyses are selected for the validation study. Typical measurement results analyzed from these benchmarks include neutron fluxes, detector count rates, detector energy response functions, neutron and gamma dose rates, neutron activation rates and activities, neutron leakage fluxes, and skyshine dose rates. Thousands of points of comparison between measurement and calculation are presented in this work. Other than rare outliers typically explained by either a lack of information or large uncertainties in the experiment conditions, material, or dimensions, the Monaco with Automated Variance Reduction using Importance Calculations (MAVRIC) radiation transport computer code with built-in variance reduction methods distributed with the SCALE computer code system agrees well with the measurement results. In selected benchmarks, MAVRIC is also compared to Monte Carlo N- Particle® (MCNP® ) 1 calculations. Both computer codes generally agree well within the estimated uncertainties. With the release of SCALE 6.3.0, Shift was integrated as an alternative transport solver in MAVRIC, denoted MAVRIC-Shift. Although the traditional MAVRIC using Monaco was used primarily in this validation study, many results have also been generated using MAVRIC-Shift. Agreement between MAVRIC-Monaco and MAVRIC-Shift is generally very good. The benchmarks presented in this report were obtained from reliable sources such as the International Criticality Safety Benchmark Evaluation Project Handbook, the Shielding Integral Benchmark Archive & Database, and other shielding validation work found in the literature. Additional datapoints and benchmarks will be added to future versions of this report to expand the shielding validation suite.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

Evaluation and Comparison of Machine Learning Techniques for Rapid QSTS Simulations

Rapid and accurate quasi-static time series (QSTS) analysis is becoming increasingly important for distribution system analysis as the complexity of the distribution system intensifies with the addition of new types, and quantities, of distributed energy resources (DER). The expanding need for hosting capacity analysis, control systems analysis, photovoltaic (PV) and DER impact analysis, and maintenance cost estimations are just a few reasons that QSTS is necessary. Historically, QSTS analysis has been prohibitively slow due to the number of computations required for a full-year analysis. Therefore, new techniques are required that allow QSTS analysis to rapidly be performed for many different use cases. This research demonstrates a novel approach to doing rapid QSTS analysis for analyzing the number of voltage regulator tap changes in a distribution system with PV components. A representative portion of a yearlong dataset is selected and QSTS analysis is performed to determine the number of tap changes, and this is used as training data for a machine learning algorithm. The machine learning algorithm is then used to predict the number of tap changes in the remaining portion of the year not analyzed directly with QSTS. The predictions from the machine learning algorithms are combined with the results of the partial year simulation for a final prediction for the entire year, with the goal of maintaining an error <10% on the full-year prediction. Five different machine learning techniques were evaluated and compared with each other; a neural network ensemble, a random forest decision tree ensemble, a boosted decision tree ensemble, support vector machines, and a convolutional neural network deep learning technique. A combination of the neural network ensemble together with the random forest produced the best results. Using 20% of the year as training data, analyzed with QSTS, the average performance of the technique resulted in ~2.5% error in the yearly tap changes, while maintaining a <10% 99.9 th percentile error bound on the results. This is a 5x speedup compared to a standard, full-length QSTS simulation. These results demonstrate the potential for applying machine learning techniques to facilitate modern distribution system analysis and further integration of distributed energy resources into the power grid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Distributed Wind: Supporting the Clean Energy Transition

Distributed wind technologies have advanced a great deal over the last decade, developing from a very selective market to one that is poised to play a significant role in supporting a clean energy transition. Summarizing technical and modeling advances, most of them supported by program efforts undertaken by the Wind Energy Technology Office of the U.S. Department of Energy, this presentation provides an overview of the current state of distributed wind technologies and how the technology could support the wider distributed energy development. The presentation also provides an short introduction to the challenges and ongoing research to address these challenges.

distributed energy↗
DOE OSTI2026

Experimental longitudinal emittance manipulation using laser-based photoionization in the Fermilab Linac

A series of simulations and beam studies were conducted at Fermilab’s linear accelerator to evaluate the effectiveness of longitudinal emittance control via laser-induced photoionization. While similar laser techniques have been employed at Fermilab to enhance injection and extraction efficiency into the Booster, the work presented here focuses on extending these methods to bunch-by-bunch manipulation. This approach utilizes fine-scale correction of the H- bunches’ longitudinal spatial distribution. In theory, loosely confined particles in longitudinal phase space contribute to emittance growth during acceleration. By selectively removing these outlying particles through laser scraping (H- + γ → H + e-), this growth can be reduced. This report presents experimental results from both symmetric and asymmetric longitudinal scraping of H⁻ bunches in the Fermilab linac, which were subsequently injected into Booster, and evaluates the broader applicability of this method for future high-intensity accelerator operations.

Landon, Parker [Boston U.; Fermilab]↗
DOE OSTI2017

Unambiguous observation of F-atom core-hole localization in CF 4 through body-frame photoelectron angular distributions

A dramatic symmetry breaking in K-shell photoionization of the CF 4 molecule in which a core-hole vacancy is created in one of four equivalent fluorine atoms is displayed in the molecular frame angular distribution of the photoelectrons. In observing the photoejected electron in coincidence with an F + atomic ion after Auger decay we see how selecting the dissociation path where the core hole was localized was almost exclusively on that atom. A combination of measurements and ab initio calculations of the photoelectron angular distribution in the frame of the recoiling CF 3 + and F + atoms elucidates the underlying physics that derives from the Ne-like valence structure of the F(1s -1 ) core-excited atom.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1979

Feasibility of automated dropsize distributions from holographic data using digital image processing techniques

An automated technique for measuring particle diameters and their spatial coordinates from holographic reconstructions is being developed. Preliminary tests on actual cold-flow holograms of impinging jets indicate that a suitable discriminant algorithm consists of a Fourier-Gaussian noise filter and a contour thresholding technique. This process identifies circular as well as noncircular objects. The desired objects (in this case, circular or possibly ellipsoidal) are then selected automatically from the above set and stored with their parametric representations. From this data, dropsize distributions as a function of spatial coordinates can be generated and combustion effects due to hardware and/or physical variables studied.

Feinstein, S. P.↗
DOE OSTI2013

Generative model selection using a scalable and size-independent complex network classifier

Real networks exhibit nontrivial topological features, such as heavy-tailed degree distribution, high clustering, and small-worldness. Researchers have developed several generative models for synthesizing artificial networks that are structurally similar to real networks. An important research problem is to identify the generative model that best fits to a target network. In this paper, we investigate this problem and our goal is to select the model that is able to generate graphs similar to a given network instance. By the means of generating synthetic networks with seven outstanding generative models, we have utilized machine learning methods to develop a decision tree for model selection. Our proposed method, which is named “Generative Model Selection for Complex Networks,” outperforms existing methods with respect to accuracy, scalability, and size-independence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS2012

Monitoring of International Space Station Telemetry Using Shewhart Control Charts

Shewhart control charts have been established as an expedient method for analyzing dynamic, trending data in order to identify anomalous subsystem performance as soon as such performance would exceed a statistically established baseline. Additionally, this leading indicator tool integrates a selection methodology that reduces false positive indications, optimizes true leading indicator events, minimizes computer processor unit duty cycles, and addresses human factor concerns (i.e., the potential for flight-controller data overload). This innovation leverages statistical process control, and provides a relatively simple way to allow flight controllers to focus their attention on subtle system changes that could lead to dramatic off-nominal system performance. Finally, this capability improves response time to potential hardware damage and/or crew injury, thereby improving space flight safety. Shewhart control charts require normalized data. However, the telemetry from the ISS Early External Thermal Control System (EETCS) was not normally distributed. A method for normalizing the data was implemented, as was a means of selecting data windows, the number of standard deviations (Sigma Level), the number of consecutive points out of limits (Sequence), and direction (increasing or decreasing trend data). By varying these options, and treating them like dial settings, the number of nuisance alerts and leading indicators were optimized. The goal was to capture all leading indicators while minimizing the number of nuisances. Lean Six Sigma (L6S) design of experiment methodologies were employed. To optimize the results, Perl programming language was used to automate the massive amounts of telemetry data, control chart plots, and the data analysis.

Fitch, Jeffery T.↗
DOE OSTI2014

The importance of accurate adiabatic interaction potentials for the correct description of electronically nonadiabatic vibrational energy transfer: A combined experimental and theoretical study of NO(v = 3) collisions with a Au(111) surface

We present a combined experimental and theoretical study of NO(v = 3 → 3, 2, 1) scattering from a Au(111) surface at incidence translational energies ranging from 0.1 to 1.2 eV. Experimentally, molecular beam–surface scattering is combined with vibrational overtone pumping and quantum-state selective detection of the recoiling molecules. Theoretically, we employ a recently developed first-principles approach, which employs an Independent Electron Surface Hopping (IESH) algorithm to model the nonadiabatic dynamics on a Newns-Anderson Hamiltonian derived from density functional theory. This approach has been successful when compared to previously reported NO/Au scattering data. The experiments presented here show that vibrational relaxation probabilities increase with incidence energy of translation. The theoretical simulations incorrectly predict high relaxation probabilities at low incidence translational energy. We show that this behavior originates from trajectories exhibiting multiple bounces at the surface, associated with deeper penetration and favored (N-down) molecular orientation, resulting in a higher average number of electronic hops and thus stronger vibrational relaxation. The experimentally observed narrow angular distributions suggest that mainly single-bounce collisions are important. Restricting the simulations by selecting only single-bounce trajectories improves agreement with experiment. The multiple bounce artifacts discovered in this work are also present in simulations employing electronic friction and even for electronically adiabatic simulations, meaning they are not a direct result of the IESH algorithm. This work demonstrates how even subtle errors in the adiabatic interaction potential, especially those that influence the interaction time of the molecule with the surface, can lead to an incorrect description of electronically nonadiabatic vibrational energy transfer in molecule-surface collisions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Fast, quantitative, and nondestructive evaluation of hydrided LWR fuel cladding by small angle incoherent neutron scattering of hydrogen

A non-destructive neutron scattering method to precisely measure the uptake of hydrogen and the distribution of hydride precipitates in light water reactor (LWR) fuel cladding was developed. Zircaloy-4 cladding used in commercial LWRs was used to produce hydrided specimens. The hydriding apparatus consists of a closed stainless steel vessel that contains Zr alloy specimens and hydrogen gas. Following hydrogen charging, the hydrogen content of the hydrided specimens was measured using the vacuum hot extraction method, by which the samples with desired hydrogen concentration were selected for the neutron study. Optical microscopy shows that our hydriding procedure results in uniform distribution of circumferential hydrides across the wall. Small angle neutron incoherent scattering was performed in the High Flux Isotope Reactor at Oak Ridge National Laboratory. This study demonstrates that the hydrogen in commercial Zircaloy-4 cladding can be measured very accurately in minutes by this nondestructive method over a wide range of hydrogen concentrations from a very small amount ( 20 ppm) to over 1000 ppm. The hydrogen distribution in a tube sample was obtained by scaling the neutron scattering rate with a factor determined by a calibration process using standard, destructive direct chemical analysis methods on the specimens. This scale factor will be used in future tests with unknown hydrogen concentrations, thus providing a nondestructive method for absolute hydrogen concentration determination.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2017

Milky Way Tomography with K and M Dwarf Stars: The Vertical Structure of the Galactic Disk

We use the number density distributions of K and M dwarf stars with vertical height from the Galactic disk, determined using observations from the Sloan Digital Sky Survey, to probe the structure of the Milky Way disk across the survey’s footprint. Using photometric parallax as a distance estimator we analyze a sample of several million disk stars in matching footprints above and below the Galactic plane, and we determine the location and extent of vertical asymmetries in the number counts in a variety of thin- and thick-disk subsamples in regions of some 200 square degrees within 2 kpc in vertical distance from the Galactic disk. These disk asymmetries present wave-like features as previously observed on other scales and at other distances from the Sun. We additionally explore the scale height of the disk and the implied offset of the Sun from the Galactic plane at different locations, noting that the scale height of the disk can differ significantly when measured using stars only above or only below the plane. Moreover, we compare the shape of the number density distribution in the north for different latitude ranges with a fixed range in longitude and find the shape to be sensitive to the selected latitude window. We explain why this may be indicative of a change in stellar populations in the latitude regions compared, possibly allowing access to the systematic metallicity difference between thin- and thick-disk populations through photometry.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Neural network modeling of in situ fluid-filled pore size distributions in subsurface shale reservoirs under data constraints

Subsurface nuclear magnetic resonance (NMR) logs acquired in the wellbore environment are sensitive to fluid-filled pore size distribution, fluid mobility, permeability, and porosity in the near-wellbore reservoir volume. NMR response of a formation layer is processed to extract the T2 distribution, which approximates the fluid-filled pore size distribution. NMR logs are acquired in limited number of wells due to financial and operational challenges, which adversely affects reservoir characterization. We developed here two neural-network-based machine learning techniques, long short-term memory (LSTM) network and variational autoencoder with a convolutional layer (VAEc) network, to process the ‘easy-to-acquire’ formation mineral and fluid saturation logs to generate synthetic NMR T2 distributions in the absence of ‘hard-to-acquire’ NMR T2 distribution log. Both the predictive models are trained and tested on limited wireline log measurements randomly selected from a 300-ft depth interval of the Bakken shale formation. Synthesis performances of LSTM and VAEc models in terms of R 2 are 0.78 and 0.75, respectively. Noise is inevitable in logging data due to the complex wellbore and formation conditions. Notably, both the predictive models robustly synthesize the fluid-filled pore size distributions in the presence of 50% noise in input logs and 30% noise in training T2 data. The performance of the proposed methodology improves with access to larger volume of training data from other formation types. The proposed method is critical to the synthesis of in situ fluid-filled pore size distributions in shale formations under data constraints due to financial and operational challenges.

58 GEOSCIENCES↗