Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “data locality”

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 1,711 records · Page 95
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Integrated Nano-Domains of Disordered and Ordered Spinel Phases in LiNi 0.5 Mn 1.5 O 4 for Li-Ion Batteries

Recent calculations and experimental data suggest that understanding the local ordering behavior of Ni/Mn will be critical to optimize the electrochemical properties of LiNi 0.5 Mn 1.5 O 4 (LNMO) high voltage spinel. In this study, we systematically controlled the evolution of Ni and Mn ordering in LNMO samples by annealing them at 700 °C in air for different dwelling times, followed by quenching to room temperature. The long- and short-range ordering behavior of Ni and Mn were analyzed by combining neutron powder diffraction, X-ray powder diffraction (XRD), transmission electron microscopy (TEM), and Fourier transform infrared spectroscopy (FT-IR) data. The results show that the fraction of ordered phase increases rapidly during initial annealing at 700 °C for 6 h, and accompanied by decreasing amounts of secondary phases. Annealing longer than 6 h led to the growth in size of ordered domains (i.e., increased segregation of ordered and disordered domains) along with a slow increase in the fraction of ordered phase. The dependence of open circuit voltages (OCVs) of the LNMO on the degree of ordering agrees well with recent calculations using the density functional theory. The increase in the degree of ordering increases the open circuit voltage (OCV) and the initial capacity but reduces cycle life and rate capability. The LNMO delivered optimal battery performances (capacity, cycle life, and rate capability) after annealing at 700 °C for 2 h. This partially ordered sample showed the respective advantages from both disordered and ordered spinels: better spinel-phase purity (thus, higher initial capacity) from the ordered LNMO and better cycle life and rate capability from the disordered LNMO.

25 ENERGY STORAGE↗
DOE OSTI2017

The need for accurate redshifts in supernova cosmology

Recent papers have shown that a small systematic redshift shift (Δ z ∼ 10{sup −5}) in measurements of type Ia supernovae can cause a significant bias (∼1%) in the recovery of cosmological parameters. Such a redshift shift could be caused, for example, by a gravitational redshift due to the density of our local environment. The sensitivity of supernova data to redshift shifts means supernovae make excellent probes of inhomogeneities. We therefore invert the analysis, and try to diagnose the nature of our local gravitational environment by fitting for Δ z as an extra free parameter alongside the usual cosmological parameters. Using the Joint Light-curve SN Ia dataset we find the best fit includes a systematic redshift shift of Δ z = (2.6{sup +2.7}{sub −2.8}) × 10{sup −4}. This is a larger shift than would be expected due to gravitational redshifts in a standard Λ-Cold Dark Matter universe (though still consistent with zero), and would correspond to a monopole Doppler shift of about 100 km s{sup −1} moving away from the Milky-Way. However, since most supernova measurements are made to a redshift precision of no better than 10{sup −3}, it is possible that a systematic error smaller than the statistical error remains in the data and is responsible for the shift; or that it is an insignificant statistical fluctuation. We find that when Δ z is included as a free parameter while fitting to the JLA SN Ia data, the constraints on the matter density shifts to Ω {sub m} = 0.313{sup +0.042}{sub −0.040}, bringing it into better agreement with the CMB cosmological parameter constraints from Planck. A positive Δ z ∼ 2.6×10{sup −4} would also cause us to overestimate the supernova measurement of Hubble's constant by Δ H {sub 0} ∼ 1 kms{sup −1}Mpc{sup −1}. However this overestimation should diminish as one increases the low-redshift cutoff, and this is not seen in the most recent data.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Distributed Hierarchical Contour Trees

Contour trees are a significant tool for data analysis as they capture both local and global variation. However, their utility has been limited by scalability, in particular for distributed computation and storage. We report a distributed data structure for storing the contour tree of a data set distributed on a cluster, based on a fan-in hierarchy, and an algorithm for computing it based on the boundary tree that represents only the superarcs of a contour tree that involve contours that cross boundaries between blocks. This allows us to limit the communication cost for contour tree computation to the complexity of the block boundaries rather than of the entire data set.

Carr, Hamish A↗
NASA NTRS2012

Axisymmetric Flow Properties for Magnetic Elements of Differing Strength

Aspects of the structure and dynamics of the flows in the Sun's surface shear layer remain uncertain and yet are critically important for understanding the observed magnetic behavior. In our previous studies of the axisymmetric transport of magnetic elements we found systematic changes in both the differential rotation and the meridional flow over the course of Solar Cycle 23. Here we examine how those flows depend upon the strength (and presumably anchoring depth) of the magnetic elements. Line of sight magnetograms obtained by the HMI instrument aboard SDO over the course of Carrington Rotation 2097 were mapped to heliographic coordinates and averaged over 12 minutes to remove the 5-min oscillations. Data masks were constructed based on the field strength of each mapped pixel to isolate magnetic elements of differing field strength. We used Local Correlation Tracking of the unmasked data (separated in time by 1- to 8-hours) to determine the longitudinal and latitudinal motions of the magnetic elements. We then calculated average flow velocities as functions of latitude and longitude from the central meridian for approx 600 image pairs over the 27-day rotation. Variations with longitude indicate and characterize systematic errors in the flow measurements associated with changes in the signal from disk center to limb. Removing these systematic errors reveals changes in the axisymmetric flow properties that reflect changes in flow properties with depth in the surface shear layer.

Rightmire-Upton, Lisa↗
NASA NTRSDate not supplied

Predicting Airport Runway Configurations for Decision-Support Using Supervised Learning

One of the most challenging tasks for air traffic controllers is runway configuration management (RCM). It deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction, other environmental variables, noise constraints, and several other airport-specific factors. It affects the efficiency of the National Airspace System (NAS) and both surface and airspace operations can benefit from better understanding future runway configurations. In this paper, we present a comprehensive implementation of predictive models for runway configuration estimation from large volumes of historical data. Specifically, operational data from two full years (2018 and 2019) is collected, analyzed, and fused together to build the data product used in this work. The data set differs from prior work in the field in terms of its scope, resolution, and variety of factors collected and considered. Meteorological data is collected from two different sources – current weather conditions from METAR (Meteorological Terminal Aviation Routine Weather Report) and forecast weather conditions from Localized Aviation MOS Program (LAMP). Operational data from the Federal Aviation Administration (FAA) Aviation System Performance Metrics (ASPM) related to scheduled and actual number of arrivals and departures, average taxi times, etc. are collected. NASA’s Sherlock Data Warehouse is used to identify critical information such as go-arounds, and other events that might impact RCM decision-making. All data is collected and aggregated over 15-minute intervals throughout the two years. This provides a resolution like the timescales that might be necessary for runway configuration management decision-making. A variety of supervised learning algorithms are tested including Support Vector Machine, Random Forest, Gradient Boosting, etc. including tuning of the model hyperparameters. The modeling process is applied and presented on two representative U.S. airports – Charlotte Douglas International Airport (KCLT) and Denver International Airport (KDEN). The two airports present different levels of complexity in terms of the total number of configurations used and provide a balanced perspective on the generalizability of the developed approach to other airports in the NAS. Initial results are promising (F1 score of 0.91 at KCLT and 0.83 at KDEN) for data in the test set. The final paper will contain a comprehensive comparison between different models and model building strategies as well as further refined results. Most important predictors for each airport will be identified along with a discussion and recommendations on adapting the framework to other scenarios.

Tejas G Puranik↗
DOE OSTI2022

Rapid assessment of REBCO CC angular critical current density J c (B, T = 4.2 K, θ) using torque magnetometry up to at least 30 tesla

Abstract Detailed design of REBa 2 Cu 3 O 7 − x (REBCO)-based magnets ideally relies on knowledge of the full angular and wide temperature range characterization of the critical current I c of the REBCO coated conductors (CC) at high magnetic fields. In practice, however, obtaining I c ( B , T , θ ) data by the commonly used electrical transport technique is expensive, tedious, and difficult, due to high critical current values that exceed 2000 A for B | | a b -plane ( θ = 90 deg). The conductors are often damaged during angular transport measurements at angles approaching the ab -plane. Therefore, so far, REBCO magnets have been designed without full I c ( B , T , θ ) data sets. Here, we present J c ( B , T , θ ) results for more than twenty samples of CC all made to the same advanced pinning specification produced by SuperPower Inc. For this, we employed torque magnetometry, benchmarking the results to transport I c measurements mostly made away from ab -plane, finding good agreement with scaling factors ≈1–1.3. What is striking is the huge variety of properties exhibited by the samples, even for CC made recently. Given the huge attention now being paid to the effects of screening currents and to the torques generated by offsets between the tape plane and the local magnetic field vector, our data set suggests some caution in detailed design of such magnets without having data of the type presented here.

Physics↗
DOE OSTI2021

Reclaimed Carbon Fiber Reinforced Automotive Part Using 3-DEP® Preforming Technology on Additive Manufacturing Tool Made with Reclaimed Carbon Fibers

The project focused on how well tools designed and built using reclaimed carbon fiber on the Big Area Additive Manufacturing (BAAM) can be used to manufacture preforms made with reclaimed carbon fibers. The project supported multiple Institute for Advanced Composite Manufacturing Innovation (IACMI) goals: • Enables the use of recycled carbon fibers in two areas: o Tooling material compounded with reclaimed carbon fiber. o Preforms made with recycled carbon fiber (rCF). • Lowers manufacturing costs by producing low cost 3-DEP® preform tooling. • Reduces manufacturing cycle time with quick additive manufacturing techniques. • Produces lightweight automotive components that will increase fuel economy which will in turn reduce greenhouse gas emissions. The project demonstrated a tool made from reclaimed carbon fibers, gathered technical data that will guide optimization of tooling materials, evaluated preforms made with the BAAM printed tool, and developed cost evaluations. This project has been a total success. First the project demonstrated that reclaimed carbon fiber can be compounded and successfully printed in the BAAM equipment. Second preforms were successfully made from reclaimed carbon fiber using a reclaimed fiber printed tool. Third the team successfully printed a molding tool out of reclaimed carbon fiber. Fourth the project went beyond the scope of this phase I project in 3 areas: (1)- Techmer PM compounded rCF in 2 resin systems instead of just one system (2)- Local Motors evaluated and collected data that will support their IACMI 3.6 project (Robert Bedsole, 2017) . (3)- University of Tennessee Knoxville (UTK) molded a part using the reclaimed preform on a molding tool made from printed reclaim fiber.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The Source Physics Experiment Large N Array

To improve the understanding of S wave generation from an explosion, a temporary deployment of 996 geophones, including both one component (Z) and three component sensors (3C), was installed from April 15 to May 23, 2016 at the Nevada National Security Site (NNSS). Sensor spacing varied from 25 to 100 m and consisted of 500 Z and 496 3C 5-Hz geophones. Data were continuously recorded during the deployment at low-gain (0 db) from April 15 to April 28 and high-gain (36 db) from April 29 to May 23. A buried (76.5 m depth) 5035 kg TNT equivalent chemical explosion (SPE-5) was recorded on April 26. It was situated in a weathered granite body surrounded by volcanic tuffs, Paleozoic carbonates, and alluvium. The array was deployed approximately 400 to 3000 m from the explosion. A set of large weight-drop shots (13,000 kg source) at 53 locations both inside and outside the geophone array were also recorded, as were local, regional, and teleseismic earthquakes. Data recovery was good, with 95% of data recovered from the chemical explosion and up to 99% in the following weeks, including both the weightdrop shots campaign and the continuous data. Important initial results from the deployment include estimates of the spatial correlation length of velocity heterogeneities and a higher resolution velocity model. Observations of the data and synthetics, indicate that some far-field (elastic) S wave energy is generated by scattering and conversion outside the near-field (inelastic) region. Interferometric processing was conducted on a Hadoop ‘big-data’ cluster.

58 GEOSCIENCES↗
DOE OSTI2023

Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. Here we present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

MetaStrider: Architectures for Scalable Memory-centric Reduction of Sparse Data Streams

Reduction is an operation performed on the values of two or more key-value pairs that share the same key. Reduction of sparse data streams finds application in a wide variety of domains such as data and graph analytics, cybersecurity, machine learning, and HPC applications. However, these applications exhibit low locality of reference, rendering traditional architectures and data representations inefficient. This article presents MetaStrider, a significant algorithmic and architectural enhancement to the state-of-the-art, SuperStrider. Furthermore, these enhancements enable a variety of parallel, memory-centric architectures that we propose, resulting in demonstrated performance that scales near-linearly with available memory-level parallelism.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1987

Binary-Symmetry Detection

Transmission errors for zeros and ones tabulated separately. Binary-symmetry detector employs psuedo-random data pattern used as test message coming through channel. Message then modulo-2 added to locally generated and synchronized version of test data pattern in same manner found in manufactured test sets of today. Binary symmetrical channel shows nearly 50-percent ones to 50-percent zeroes correspondence. Degree of asymmetry represents imbalances due to either modulation, transmission, or demodulation processes of system when perturbed by noise.

Lopez, Hiram↗
NASA NTRS2002

SAGE (version 5.96) Ozone Trends in the Lower Stratosphere

Ozone retrievals from Stratospheric Aerosol and Gas Experiment (SAGE) II version 5.96 (v5.96) below approx. 25 km altitude are discussed. This version of the algorithm includes improved constraints on the wavelength dependence of aerosol extinctions based on the ensemble of aerosol size distribution measurements. This results in a reduction of SAGE ozone errors in the 2 years after the Mount Pinatubo eruption. However, SAGE ozone concentrations are still approx. 10% larger than ozonesonde and Halogen Occultation Experiment (HALOE) measurements below 20 km altitude under nonvolcanic conditions (and by more than this in the tropics). The analysis by Steele and Turco suggests that the SAGE ozone overpredictions are in the wrong direction to be explained by aerosol extinction extrapolation errors. Moreover, preliminary SAGE 11 v6.0a retrievals suggest that they are partially accounted for by geometric difficulties at low altitudes in v5.96 and prior retrievals. SAGE ozone trends for the 1979-1996 and 1984-1996 periods are calculated and compared, and the sources of trend errors are discussed. These calculations are made after filtering out ozone data during periods of high, local aerosol extinctions. In the lower stratosphere, below approx. 28 km altitude, there is shown to be excellent agreement in the altitudinal structure of ozone decreases at 45 deg N between SAGE and ozonesondes with the largest decrease in both between 1979 and 1996 having occurred below 20 km altitude, amounting to 0.9 +/- 0.7% yr (2sigma) at 16 km altitude. However, in contrast to the fairly steady decreases at 45 deg N, both SAGE measurements and Lauder ozonesondes show ozone increases at 45 deg S over the period from the mid-1980s to 1996 of 0.2 +/- 0.5%/yr (2sigma) from 15 to 20 km altitude. The SAGE data suggest that this increase is a wintertime phenomenon which occurs in the 15-20 km height range. Changes in dynamics are suggested as the most likely cause of this increase. These hemispheric differences in ozone trends are supported by ozone column measurements by the Total Ozone Mapping Spectrometer (TOMS).

Cunnold, D. M.↗
DOE OSTI2014

SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1985

Whistler-triggered VLF noise bursts observed on the DE-1 satellite and simultaneously at Antarctic ground stations

Simultaneous observations of whistler-triggered very low-frequency noise bursts on the ground at Anarctic stations, Halley and Siple, and on the high-altitude satellite DE-1 are reported. Results of a case study from June 25, 1982 in which the satellite data were recorded near 25 deg south magnetic latitude and the L = 4.7 magnetic shell, are presented. Analysis indicates that the chorus bursts that are triggered in whistler ducts travel downwards in the ducts to low altitudes in the ionosphere, and that propagation to DE-1 is by upward reflection into a nonducted mode. A means of estimating the propagation characteristics of the wave bursts is provided by comparisons of nonducted signals from the Siple transmitter and discrete periodic emissions. The ducted-nonducted mode conversion process is a mechanism for the large-scale spreading into the magnetosphere of coherent whistler-mode wave energy which is generated, amplified, or triggered in small localized ducts. The DE-1 data show that a strong interaction exists between whistler-triggered noise bursts and prevailing hiss levels.

Smith, A. J.↗
DOE OSTI2013

Coherent microscopy at resolution beyond diffraction limit using post-experimental data extrapolation

Conventional microscopic records represent intensity distributions whereby local sample information is mapped onto local information at the detector. In coherent microscopy, the superposition principle of waves holds; field amplitudes are added, not intensities. This non-local representation is spread out in space and interference information combined with wave continuity allows extrapolation beyond the actual detected data. Established resolution criteria are thus circumvented and hidden object details can retrospectively be recovered from just a fraction of an interference pattern.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Adaptive Fault Detection Based on Neural Networks and Multiple Sampling Points for Distribution Networks and Microgrids

Smart networks such as microgrid (MG) and active distribution networks (ADN) have been recently playing an important role in power system operation. The design and implementation of appropriate protection systems for such networks must be addressed, which imposes new technical challenges. This paper presents the implementation and validation aspects of an adaptive fault detection strategy based on neural networks (NNs) and multiple sampling points for ADN and MG. The solution is implemented on an edge device. Artificial NNs are used to derive a data-driven model that uses only local measurements to detect fault states of the network without the need for communication infrastructure. Multiple sampling points are used to derive a data-driven model, which allows the generalization considering the implementation in physical systems. The adaptive fault detector model is implemented on a Jetson Nano system, which is a single-board computer (SBC) with a small Graphic Processing Unit (GPU) intended to run machine learning loads at the edge. The proposed method is tested in a physical, real-life, low-voltage network located at Universidad del Norte, Colombia. This testing network is based on the IEEE-13 Node Test Feeder scaled down to 220 V. The validation in a simulation environment shows the accuracy and dependability above 99.6%, while the real-time tests show the accuracy and dependability of 95.5% and 100%, respectively. Without hard-to-derive parameters, the easy-to-implement embedded model highlights the potential for real-life applications.

42 ENGINEERING↗
DOE OSTI2019

Deconvolving distribution of relaxation times, resistances and inductance from electrochemical impedance spectroscopy via statistical model selection: Exploiting structural-sparsity regularization and data-driven parameter tuning

The distribution of relaxation times (DRT) has drawn increasing attention for interpreting electrochemical impedance spectroscopy (EIS). Deconvolution of DRT from EIS is a challenging ill-posed problem that requires regularization methods. In this work, we formulate DRT reconstruction task as a statistical model selection problem with structural-sparsity penalties. We utilize the Elastic net regularization that simultaneously benefits from Ridge and Lasso regularizations with optimal tuning parameter automatically determined by the information criteria. We benchmark our approach on four synthetic experiments (a ZARC element, ZARC mixtures, a RC circuit and a Fractal element) and two real EIS datasets of a Lithium ion battery and an organic-inorganic halide class of perovskites in oxygen environment at different gas pressures. We demonstrate the superiority of proposed model selection procedure, that is capable of eliminating pseudo peaks and representing asymmetries in DRT as well as precisely estimating resistances. Here, we highlight our approach is robust to reducing and subsampling EIS frequency range, making it a promising tool for timing-resolved, localized and large scale EIS data analysis. For the Lithium ion battery data analysis, we extend the classical DRT model to incorporate the inductive effect and illustrate DRT as a guidance for equivalent circuit modeling to refine impedance reconstruction at low risks of overfitting. Furthermore, the structural-sparsity regularization could be extended for multidimensional and Bayesian EIS data analysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2014

Using Social Media and Mobile Devices to Discover and Share Disaster Data Products Derived From Satellites

Data products derived from Earth observing satellites are difficult to find and share without specialized software and often times a highly paid and specialized staff. For our research effort, we endeavored to prototype a distributed architecture that depends on a standardized communication protocol and applications program interface (API) that makes it easy for anyone to discover and access disaster related data. Providers can easily supply the public with their disaster related products by building an adapter for our API. Users can use the API to browse and find products that relate to the disaster at hand, without a centralized catalogue, for example floods, and then are able to share that data via social media. Furthermore, a longerterm goal for this architecture is to enable other users who see the shared disaster product to be able to generate the same product for other areas of interest via simple point and click actions on the API on their mobile device. Furthermore, the user will be able to edit the data with on the ground local observations and return the updated information to the original repository of this information if configured for this function. This architecture leverages SensorWeb functionality [1] presented at previous IGARSS conferences. The architecture is divided into two pieces, the frontend, which is the GeoSocial API, and the backend, which is a standardized disaster node that knows how to talk to other disaster nodes, and also can communicate with the GeoSocial API. The GeoSocial API, along with the disaster node basic functionality enables crowdsourcing and thus can leverage insitu observations by people external to a group to perform tasks such as improving water reference maps, which are maps of existing water before floods. This can lower the cost of generating precision water maps. Keywords-Data Discovery, Disaster Decision Support, Disaster Management, Interoperability, CEOS WGISS Disaster Architecture

0↗