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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Nuclear Data Sheets for A = 235

Spectroscopic data and level schemes from radioactive decay and nuclear reaction studies are presented here for all nuclei with mass number A=235. The highlight of this evaluation consists of the precise and comprehensive Coulomb excitation study (2012Wa35) on {sup 235}U, which in addition to the 7/2[743] ground state rotational band, extended the 1/2[631], 5/2[622], 5/2[752], and 3/2[631] rotational bands up to Jπ=53/2+, 49/2+, 441/2−, and 43/2+, respectively. This evaluation presents a study (2010Hu02) of the {sup 237}Np({sup 116}Sn, {sup 118}Snγ) reaction where the ground state rotational band 5/2[642] was observed up to Jπ=(53/2+). It is worth for historical knowledge to mention the report on the “Discovery of isotopes of the transuranium elements with 93≤Z≤98” (2013Fr02), where the information for elements Np, Pu, and Am with mass number A=235 is given. {sup 235}Cf has not been observed. The alpha hindrance factors (HF) presented in this evaluation were calculated using values of the radius parameter (r{sub 0}) interpolated from those for even–even adjacent nuclei given by 1998Ak04.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2018

A global coupled ensemble data assimilation system using the Community Earth System Model and the Data Assimilation Research Testbed

In this paper, we present a description of the CESM/DART ensemble coupled data assimilation (DA) system based on the Community Earth System Model (CESM) and the Data Assimilation Research Testbed (DART) assimilation software. The CESM/DART should be viewed as a flexible system to support the DA needs of the CESM research community and not as a static reanalysis product. In this implementation of the CESM/DART, conventional insitu observations of the ocean and atmosphere are assimilated into the respective component models of the CESM using a 30-member ensemble adjustment Kalman filter (EAKF). CESM/DART is run in a “weakly coupled” configuration wherein observations native to each climate system component only directly impact the state vector for that component. Information is passed between components indirectly through the short-term coupled model forecasts that provide the EAKF background ensemble. This system leverages previous ensemble DA development for the Community Atmosphere Model and Parallel Ocean Program models using the DART EAKF. The CESM/DART project is a step towards providing increasingly useful DA capabilities for the CESM research community. Results are presented for our prototype 12-year reanalysis, run from 1970 to mid 1982. Multiple lines of evidence demonstrate that the system is capable of constraining the CESM coupled model to simulate the historical variability of the climate system in the well-observed Northern Hemisphere. A collection of monthly average variables, climate mode indices, observation diagnostics and snapshots of synoptic weather in the ocean and atmosphere are compared to established datasets, showing especially good agreement in the Northern Hemisphere. A discussion of the CESM/DART as a modular, community facility and the benefits and challenges associated with this vision is also included.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Fallon FORGE: Well Data, Geophysical Data, and Geologic Maps

The data is associated to the Fallon FORGE project and includes mudlogs for all wells used to characterize the subsurface, as wells as gravity, magnetotelluric, earthquake seismicity, and temperature data from the Navy GPO and Ormat. Also included are geologic maps from the USGS and Nevada Bureau of Mines and Geology for the Fallon, NV area.

13-36↗
DOE OSTI2021

Hierarchical Data Format for Nuclear Data Sensitivities [Slides]

An HDF5-based file format was introduced for the sensitivity data calculated by TSUNAMI. The format was defined to collect the sensitivity coefficients into hyperslabs, which optimizes file reading time and therefore improves the time-to-solution for applications. In future work, this format will be extended to store sensitivity data for depletion calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

RE Data Explorer is Expanding Public Access to High-Quality Data and Analytics

The Renewable Energy (RE) Data Explorer is an innovative and intuitive web platform from the U.S. Agency for International Development (USAID)-National Renewable Energy Laboratory (NREL) Partnership that enables users to easily access high-quality renewable energy resource data and applied analytics.

ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

A comparison of opioid‐involved fatalities captured in the National Poison Data System to data derived from US death certificate literal text

Abstract Purpose The purpose of the study is to describe and compare the number and characteristics of opioid‐involved fatal cases captured in the National Poison Data System (NPDS) and in US death certificates. Methods NPDS, which collects data on all calls to US poison control centers, and Drug‐Involved Mortality (DIM), which combines information from literal text of US death certificates and National Vital Statistics Systems, were queried for opioid‐involved fatal cases from 2010 to 2015. Characteristics of the two case series were compared. Results DIM contained 154 016 opioid‐involved overdose deaths, and NPDS contained 2524 fatal opioid exposures, a ratio of 61:1. The number of opioid deaths remained stable in NPDS but increased in DIM over the 6‐year period. On average, deaths involving opioids with higher mean dosage strength (in morphine milligram equivalents) per unit among dispensed prescriptions were more likely to be captured in DIM relative to NPDS, as compared with those with a lower mean dosage strength per unit. The increase in fentanyl‐related deaths seen in DIM since 2013 was not observed in NPDS. Conclusions NPDS is a valuable drug safety surveillance resource due to its timeliness and drug specificity. However, it captures only a small fraction of opioid‐involved fatal poisonings, and comparisons with data derived from death certificate literal text indicate that caution is warranted in making inferences about opioid‐involved fatality trends over time or comparisons across opioids.

Mallama, Celeste A.↗
DOE OSTI2024

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

42 ENGINEERING↗
DOE OSTI2014

Data free inference with processed data products

Here, we consider the context of probabilistic inference of model parameters given error bars or confidence intervals on model output values, when the data is unavailable. We introduce a class of algorithms in a Bayesian framework, relying on maximum entropy arguments and approximate Bayesian computation methods, to generate consistent data with the given summary statistics. Once we obtain consistent data sets, we pool the respective posteriors, to arrive at a single, averaged density on the parameters. This approach allows us to perform accurate forward uncertainty propagation consistent with the reported statistics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Patience is a virtue: A data-driven analysis of rooftop solar PV permitting timelines in the United States

© 2020 The Authors Local permitting can ensure the safe installation and operation of rooftop solar photovoltaic (PV) systems. At the same time, burdensome local permitting processes and local variation in requirements may pose challenges to PV deployment. In this article, we explore new data on the durations between key steps in the PV permitting process in the United States. The data suggest that a typical customer can expect to wait around 25–100 days from permit application until an installed system passes inspection. Permit durations vary significantly across jurisdictions, due in part to differences in local permitting policies. However, permit durations vary as significantly within jurisdictions as across them, in part due to significant variation across installers, suggesting that installer strategies and practices play an important role in permitting timelines. Permit durations have declined over time, reflecting progress from permit streamlining policies and jurisdiction learning-by-doing, though durations have stabilized in recent years. The data suggest that typical PV customers still face long and uncertain permitting timelines in the United States.

O'Shaughnessy, E↗
DOE OSTI2019

A data-driven CO 2 leakage detection using seismic data and spatial–temporal densely connected convolutional neural networks

In carbon capture and sequestration (also known as carbon capture and storage, or CCS), developing effective monitoring methods is needed to detect and respond to CO 2 leakage. CO 2 leakage detection methods rely on geophysical observations and monitoring sensor network. However, traditional methods usually require the development of site-specific physical models and expert interpretation, and the effectiveness of these methods can be limited to different application locations, operational scenarios, and conditions. Here, we developed a novel data-driven leakage detection method based on densely connected convolutional neural networks. Our method is an end-to-end detection approach, that differs from conventional leakage monitoring methods by directly learning a mapping relationship between seismic data and the CO 2 leakage mass. To account for the spatial and temporal characteristics of seismic data, our novel networks architecture combines 1D and 2D convolutional neural networks. To overcome the computational expense of solving optimization problems, we apply a densely-connecting strategy in our network architecture that reduces the number of network parameters. Based on the features generated by our convolutional neural networks, we further incorporate a long short-term memory network to utilize time-sequential information, which further improves the detection accuracy. Lastly, we employ our detection method to synthetic seismic datasets generated based on flow simulations of a hypothetical CO 2 storage scenario with injection into a partially compartmentalized sandstone storage reservoir. To evaluate method performance, we conducted multiple experiments including a random leakage test, a sequential test, and a robustness test. Numerical results show that our CO 2 leakage detection method successfully detects the leakage and accurately predicts the leakage mass, suggesting that it has the potential for application in monitoring of real CO 2 storage sites.

58 GEOSCIENCES↗
DOE OSTI2021

Modeling atmospheric data and identifying dynamics Temporal data-driven modeling of air pollutants

Atmospheric modelling has recently experienced a surge with the advent of deep learning. Most of these models, however, predict concentrations of pollutants following a data-driven approach in which the physical laws that govern their behaviors and relationships remain hidden. Furthermore, with the aid of real-world air quality data collected hourly in different stations throughout Madrid, we present a case study using a series of data-driven techniques with the following goals: (1) Find systems of ordinary differential equations that model the concentration of pollutants and their changes over time; (2) assess the performance and limitations of our model using stability analysis; (3) reconstruct the time series of chemical pollutants not measured in certain stations using delay coordinate embedding results.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Physics-Informed CoKriging: A Gaussian-Process-Regression-Based Multifidelity Method for Data-Model Convergence

In this work, we propose a new Gaussian process regression (GPR)-based multifidelity method: physics-informed CoKriging (CoPhIK). In CoKriging-based multifidelity methods, the quantities of interest are modeled as linear combinations of multiple parameterized stationary Gaussian processes (GPs), and the hyperparameters of these GPs are estimated from data via optimization. In CoPhIK, we construct a GP representing low-fidelity data using physics-informed Kriging (PhIK), and model the discrepancy between low- and highfidelity data using a parameterized GP with hyperparameters identified via optimization. Our approach reduces the cost of optimization for inferring hyperparameters by incorporating partial physical knowledge. We prove that the physical constraints in the form of deterministic linear operators are satisfied up to an error bound. Furthermore, we combine CoPhIK with a greedy active learning algorithm for guiding the selection of additional observation locations. The eciency and accuracy of CoPhIK are demonstrated for reconstructing the partially observed modified Branin function, reconstructing the sparsely observed state of a steady state heat transport problem, and learning a conservative tracer distribution from sparse tracer concentration measurements.

Yang, Xiu↗
DOE OSTI2018

Nuclear Data Sheets for A=39☆

Experimental nuclear structure data from various reactions and decays have been evaluated for all known nuclides of mass 39 (Mg, Al, Si, P, S, Cl, Ar, K, Ca, Sc, Ti). Detailed evaluated nuclear structure information is presented for each reaction and decay and the best values combining all available data are recommended for level energies, half-lives, γ-ray energies and intensities, decay properties (energies, intensities and placement of radiations), and other spectroscopic data. The {sup 39}K, {sup 39}Ca and {sup 39}Ar are the most extensively studied nuclides; no excited states are known in {sup 39}Mg and {sup 39}Ti. This work supersedes the earlier full evaluation of A=39 by B. Singh and J. Cameron (2006Si02).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

Nuclear Data Sheets for A=254☆

Spectroscopic data for known A=254 nuclides (U, Cf, Es, Fm, Md, No, Lr and Rf) have been updated and re-evaluated, superseding the data presented in the previous evaluation 2005Bh12 published by A. Bhagwat, N.J. Thompson and J.K. Tuli. With the exception of the {sup 254}No nuclide, excited-state data for A=254 nuclei are largely unknown. The decay of the 184−μs isomer in {sup 254}No has been studied in two independent experiments, results of which differ significantly, thus precluding adoption of any one particular decay scheme. Only the features that are consistent in these studies are included in the Adopted dataset, whereas independent decay schemes are presented in separate datasets. Information is also presented here for possible production of the {sup 254}U nuclide in a study (1960Di03) of debris of the first large-scale thermonuclear test (Ivy Mike) of November 1, 1952 in the Pacific Ocean.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗