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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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Bias corrections of GOSAT SWIR XCO 2 and XCH 4 with TCCON data and their evaluation using aircraft measurement data

We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO 2 (XCO 2 ) and CH 4 (XCH 4 ) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from the Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO 2 and XCH 4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole-to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically derived bias correction improves the agreement between GOSAT XCO 2 /XCH 4 and the aircraft data. Finally, we present spatial distributions and temporal variations of the derived GOSAT biases.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2014

The NGEE Arctic Data Archive -- Portal for Archiving and Distributing Data and Documentation

The Next-Generation Ecosystem Experiments (NGEE Arctic) project is committed to implementing a rigorous and high-quality data management program. The goal is to implement innovative and cost-effective guidelines and tools for collecting, archiving, and sharing data within the project, the larger scientific community, and the public. The NGEE Arctic web site is the framework for implementing these data management and data sharing tools. The open sharing of NGEE Arctic data among project researchers, the broader scientific community, and the public is critical to meeting the scientific goals and objectives of the NGEE Arctic project and critical to advancing the mission of the Department of Energy (DOE), Office of Science, Biological and Environmental (BER) Terrestrial Ecosystem Science (TES) program.

Boden, Thomas A.↗
DOE OSTI2023

Data Challenges in Multi-Sensor Data Science System for Monitoring a Solvent Extraction Process

Idaho National Laboratory (INL) is maintaining and gaining knowledge into the nuclear fuel cycle by building a test bed to allow researchers the opportunity to study nuclear fuel processing operations. This includes studying solvent extraction processes that use centrifugal contactors. As part of INL’s mission, the goal of this project is to develop a system that utilizes non-traditional measurement sources such as vibration, acoustics, current, light, flow, and temperature in conjunction with data-based, machine learning techniques that will allow for signal discovery. This multisensory data can support the development of safeguards by design, provide operator process awareness, and discover process anomalies. This poster will highlight some of the data collection and analytics challenges for the multi-sensor system as well as the mitigation strategies to build a robust system. Additionally, some preliminary data from the first testing campaign will be shown to help illustrate the data needs of the system.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

A summary of the mechanical properties data developed in FY 2023 by ANL, INL and ORNL to support the data package development for the A709 Code Case

This report provides the status of tensile, creep, fatigue, and creep-fatigue testing to date conducted at Argonne National Laboratory, Idaho National Laboratory, and Oak Ridge National Laboratory to generate the data package required to qualify Alloy 709 in American Society of Mechanical Engineers, Boiler and Pressure Vessel Code, Section III, Division 5. The Division 5 Class A Alloy 709 Code Case requires data generated from a minimum of three commercial heats. Extensive mechanical properties data have been generated on two commercial heats, and some initial test data have been generated from the third commercial heat. The room temperature tensile test results for the three commercial heats met the specification minimum of the American Society of Mechanical Engineers, Boiler and Pressure Vessel Code, Section II, Part A, SA-213/SA-213M for Grade TP310MoCbN (UNS S31025) seamless tubing. These three commercial heats of Alloy 709 are suitable for generating the code case data.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Semantic Catalog of Things, Services, and Data to Support a Wind Data Management Facility

The purpose of this paper is to discuss how community vocabularies and linked open data best practices are being used to seamlessly link things, data, and off the shelf services to support scientific offshore wind energy research for the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) Wind and Water Power Program. This is largely made possible by leveraging collaborative advances in the Internet of Things (IoT), Semantic Web, Linked Services, Linked Open Data (LOD), and RDF vocabulary communities, which provide the foundation for our design. By adapting these linked community best practices, we designed a wind characterization data management facility capable of continually collecting, processing, and preservation of in situ and remote sensing instrume

atmosphere↗
DOE OSTI2019

A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation

Attributing to the flexibility in considering various types of observation error and model error, data assimilation has been increasingly applied to dynamically improve soil moisture modeling in many hydrological practices. However, accurate characterization of model error, especially the part caused by defective model structure, presents a significant challenge to the successful implementation of data assimilation. Model structural error has received limited attention relative to parameter and input errors, mainly due to our poor understanding of structural inadequacy and the difficulties in parameterizing structural error. In this paper, we present a dynamic data-driven approach to estimate the model structural error in soil moisture data assimilation without the need for identifying error generation mechanism or specifying particular form for the error model. The error model is based on the Gaussian process regression and then integrated into the ensemble Kalman filter (EnKF) to form a hybrid method for dealing with multi-source model errors. Two variants of the hybrid method in terms of two different error correction manners are proposed. The effectiveness of the proposed method is tested through a suit of synthetic cases and a real-world case. Results demonstrate the potential of the proposed hybrid method for estimating model structural error and providing improved model predictions. Compared to the traditional EnKF without explicitly considering the model structural error, parameter compensation issue is obviously reduced and soil moisture retrieval is substantially improved.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Toward a smart data transfer node

Scientific computing systems are becoming significantly more complex, with distributed teams and complex workflows spanning resources from telescopes and light sources to fast networks and Internet of Things sensor systems. In such settings, no single, centralized administrative team and software stack can coordinate and manage all resources used by a single application. Indeed, we have reached a critical limit in manageability using current human-in-the-loop techniques. We therefore argue that resources must begin to respond automatically, adapting and tuning their behavior in response to observed properties of scientific workflows. Over time, machine learning methods can be used to identify effective strategies for autonomic, goal-driven management behaviors that can be applied end-to-end across the scientific computing landscape. Using the data transfer nodes that are widely deployed in modern research networks as an example, we explore the architecture, methods, and algorithms needed for a smart data transfer node to support future scientific computing systems that self-tune and self-manage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Data-driven probability concentration and sampling on manifold

A new methodology is proposed for generating realizations of a random vector with values in a finite-dimensional Euclidean space that are statistically consistent with a dataset of observations of this vector. The probability distribution of this random vector, while a priori not known, is presumed to be concentrated on an unknown subset of the Euclidean space. A random matrix is introduced whose columns are independent copies of the random vector and for which the number of columns is the number of data points in the dataset. The approach is based on the use of (i) the multidimensional kernel-density estimation method for estimating the probability distribution of the random matrix, (ii) a MCMC method for generating realizations for the random matrix, (iii) the diffusion-maps approach for discovering and characterizing the geometry and the structure of the dataset, and (iv) a reduced-order representation of the random matrix, which is constructed using the diffusion-maps vectors associated with the first eigenvalues of the transition matrix relative to the given dataset. The convergence aspects of the proposed methodology are analyzed and a numerical validation is explored through three applications of increasing complexity. The proposed method is found to be robust to noise levels and data complexity as well as to the intrinsic dimension of data and the size of experimental datasets. Both the methodology and the underlying mathematical framework presented in this paper contribute new capabilities and perspectives at the interface of uncertainty quantification, statistical data analysis, stochastic modeling and associated statistical inverse problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

B-PINNs: Bayesian Physics-informal Neural Networks for Forward and Inverse PDE Problems with Noisy Data

We propose a Bayesian physics-informed neural network (B-PINN) to solve both forward and inverse nonlinear problems described by partial differential equations (PDEs) and noisy data. In this Bayesian framework, the Bayesian neural network (BNN) combined with a PINN for PDEs serves as the prior while the Hamiltonian Monte Carlo (HMC) or the variational inference (VI) could serve as an estimator of the posterior. B-PINNs make use of both physical laws and scattered noisy measurements to provide predictions and quantify the aleatoric uncertainty arising from the noisy data in the Bayesian framework. Compared with PINNs, in addition to uncertainty quantification, B-PINNs obtain more accurate predictions in scenarios with large noise due to their capability of avoiding overfitting. We conduct a systematic comparison between the two different approaches for the B-PINNs posterior estimation (i.e., HMC or VI), along with dropout used for quantifying uncertainty in deep neural networks. Our experiments show that HMC is more suitable than VI with mean field Gaussian approximation for the B-PINNs posterior estimation, while dropout employed in PINNs can hardly provide accurate predictions with reasonable uncertainty. Finally, we replace the BNN in the prior with a truncated Karhunen-Loève (KL) expansion combined with HMC or a deep normalizing flow (DNF) model as posterior estimators. The KL is as accurate as BNN and much faster but this framework cannot be easily extended to high-dimensional problems unlike the BNN based framework.

Non linear PDEs, Noisy data, Bysian physics inform↗
DOE OSTI2013

ParNCL and ParGAL: Data-parallel Tools for Postprocessing of Large-scale Earth Science Data

Earth science high-performance applications often require extensive analysis of their output in order to complete the scien- tific goals or produce a visual image or animation. Often this analysis cannot be done in situ because it requires calculating time-series statistics from state sampled over the entire length of the run or analyzing the relationship between similar time series from previous simulations or observations. Many of the tools used for this postprocessing are not themselves high- performance applications, but the new Parallel Gridded Analysis Library (ParGAL) provides high-performance data-parallel versions of several common analysis algorithms for data from a structured or unstructured grid simulation. The library builds on several scalable systems, including the Mesh Oriented DataBase (MOAB), a library for representing mesh data that sup- ports structured, unstructured finite element, and polyhedral grids; the Parallel-NetCDF (PNetCDF) library; and Intrepid, an extensible library for computing operators (such as gradient, curl, and divergence) acting on discretized fields. We have used ParGAL to implement a parallel version of the NCAR Command Language (NCL) a scripting language widely used in the climate community for analysis and visualization. The data-parallel algorithms in ParGAL/ParNCL are both higher performing and more flexible than their serial counterparts.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Spatial heterogeneity analysis of the effects of urbanization on airport CO2 emissions in China - A geographically weighted approach using nighttime light data

Regional disparities in carbon dioxide (CO2) emissions from airports at the city level are of increasing importance for low-carbon development of the civil aviation sector. However, CO2 emissions from airport operations have rarely been estimated and discussed. We investigate the main driving forces of airport CO2 emissions by using Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models, separately, to investigate whether urbanization drives airport CO2 emissions and to investigate spatial heterogeneity at the city level. Nighttime light (NTL) data from satellite observations are adopted as a proxy for urbanization. We obtained energy consumption data by end-use purpose for 70 airports in China and calculated the CO2 emissions from on-ground airport operations. The median CO2 emissions of the 70 sample airports are estimated to be 15.9 million tonnes for 2015. Results from the GWR model indicate that airport CO2 emissions are affected by five main factors: urbanization, foreign direct investment, the share of tertiary industry in gross domestic output, passenger turnover of civil aviation and passenger turnover of railways. The elasticity of urbanization shows an increasing trend from the east of China to the west. The spatial heterogeneity of the CO2 emissions of the five airport clusters that are located in five urban agglomerations is discussed. In order to achieve effective reductions of CO2 emissions from airports, policy-makers should consider the spatial heterogeneity of the major driving factors of carbon emissions in different regions to avoid carbon lock-in.

Airport CO2 emissions↗
DOE OSTI2024

Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge

Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. Here, to protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-time Fourier transform (STFT) and object detection network for real-time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Green Routing Fuel Saving Opportunity Assessment: A Case Study on California Large-Scale Real-World Travel Data

New technologies, such as connected and automated vehicles, have attracted more and more researchers for improving the energy efficiency and environmental impact of current transportation systems. The green routing strategy instructs a vehicle to select the most fuel-efficient route before the vehicle departs. It benefits the current transportation system with fuel saving opportunity through identifying the greenest route. This paper introduces an evaluation framework for estimating benefits of green routing based on large-scale, real-world travel data. The framework has the capability to quantify fuel savings by estimating the fuel consumption of actual routes and comparing to routes procured by navigation systems. A route-based fuel consumption estimation model, considering road traffic conditions, functional class, and road grade is proposed and used in the framework. An experiment using a large-scale data set from the California Household Travel Survey global positioning system trajectory data base indicates that 31% of actual routes have fuel savings potential with a cumulative estimated fuel savings of 12%.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

A Novel Data Segmentation Method for Data-driven Phase Identification

This paper presents a smart meter phase identification algorithm for two cases: meter-phase-label-known and meter-phase-label-unknown. To improve the identification accuracy, a data segmentation method is proposed to exclude data segments that are collected when the voltage correlation between smart meters on the same phase is weakened. Then, using the selected data segments, a hierarchical clustering method is used to calculate the correlation distances and cluster the smart meters. If the phase labels are unknown, a Connected-Triple-based Similarity (CTS) method is adapted to further improve the phase identification accuracy of the ensemble clustering method. The methods are developed and tested on both synthetic and real feeder data sets. Here, simulation results show that the proposed phase identification algorithm outperforms the state-of-the-art methods in both accuracy and robustness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Use cases of lossy compression for floating-point data in scientific data sets

Architectural and technological trends of systems used for scientific computing call for a significant reduction of scientific data sets that are composed mainly of floating-point data. Here, this article surveys and presents experimental results of currently identified use cases of generic lossy compression to address the different limitations of scientific computing systems. The article shows from a collection of experiments run on parallel systems of a leadership facility that lossy data compression not only can reduce the footprint of scientific data sets on storage but also can reduce I/O and checkpoint/restart times, accelerate computation, and even allow significantly larger problems to be run than without lossy compression. In conclusion, these results suggest that lossy compression will become an important technology in many aspects of high performance scientific computing. Because the constraints for each use case are different and often conflicting, this collection of results also indicates the need for more specialization of the compression pipelines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Observation and characterization of a cosmic muon neutrino flux from the northern hemisphere using six years of IceCube data

The IceCube Collaboration has previously discovered a high-energy astrophysical neutrino flux using neutrino events with interaction vertices contained within the instrumented volume of the IceCube detector. We present a complementary measurement using charged current muon neutrino events where the interaction vertex can be outside this volume. As a consequence of the large muon range the effective area is significantly larger but the field of view is restricted to the Northern Hemisphere. IceCube data from 2009 through 2015 have been analyzed using a likelihood approach based on the reconstructed muon energy and zenith angle. At the highest neutrino energies between 194 TeV and 7.8 PeV a significant astrophysical contribution is observed, excluding a purely atmospheric origin of these events at 5.6σ significance. The data are well described by an isotropic, unbroken power-law flux with a normalization at 100 TeV neutrino energy of (0.90{sub −0.27}{sup +0.30})×10{sup −18} GeV{sup −1} cm{sup −2} s{sup −1} sr{sup −1} and a hard spectral index of γ=2.13±0.13. The observed spectrum is harder in comparison to previous IceCube analyses with lower energy thresholds which may indicate a break in the astrophysical neutrino spectrum of unknown origin. The highest-energy event observed has a reconstructed muon energy of (4.5±1.2) PeV which implies a probability of less than 0.005% for this event to be of atmospheric origin. Analyzing the arrival directions of all events with reconstructed muon energies above 200 TeV no correlation with known γ-ray sources was found. Using the high statistics of atmospheric neutrinos we report the current best constraints on a prompt atmospheric muon neutrino flux originating from charmed meson decays which is below 1.06 in units of the flux normalization of the model in Enberg et al.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Livewire Data Platform - A Solution for EEMS Data Sharing

To bring about fundamental changes in our transportation system, data sharing must be endemic. The Livewire Data Platform is a user-friendly, searchable platform allowing easy and secure data sharing and discovery for DOE's Energy Efficient Mobility Systems (EEMS) researchers. The platform was funded by the DOE Vehicle Technologies Office (VTO) to enable a research community that builds partnerships and collaboration around data in order to find answers to today's biggest mobility challenges.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

FedFleet 2024: Fleet Data Quality - Understanding and Improving Your Agency's FAST Fleet Data Quality Metric? [Slides]

This presentation presents an overview of how the team supporting the Federal Automotive Statistical Tool (FAST) project for DOE and GSA view and assess the quality of fleet data submissions from federal agencies. It also discusses ways in which agencies can improve the quality of their fleet data and of their fleet data submissions. FAST is a web-based information system sponsored by GSA's Office of Government-wide Policy and DOE's Federal Energy Management Program to collect information about the US federal government's fleet of motor vehicles; FAST is developed, maintained, and supported by DOE's Idaho National Laboratory (INL).

99 GENERAL AND MISCELLANEOUS↗