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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Nuclear Data Management and Analysis System Plan

The United States Department of Energy Advanced Reactor Technologies Program was formed in Fiscal Year 2015 and encompasses the Next Generation Nuclear Plant Project and Very High Temperature Reactor (VHTR) Program as they were known previously. The VHTR Program was created to support design and licensing of the first VHTR nuclear plant. Data created for and used by the program must be qualified for use, stored in a readily accessible electronic form, categorized to assure the correct data are used, and controlled to prevent data corruption or inadvertent changes. The Nuclear Data Management and Analysis System was designed to support the data needs of the VHTR Program, at the time and now the Advanced Reactor Technologies Program. Since its inception, use of the Nuclear Data Management and Analysis System has expanded to support additional projects and programs with similar requirements for control, analysis, and availability of large data sets.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2013

BioWatch SMS Supervisor Guide

The BioWatch Sample Management System is software developed by Los Alamos National Laboratory to support the Department of Homeland Security BioWatch program. Field Operations Supervisors use the software to record, track, and manage data for the BioWatch sample collection process. This includes installing the software, creating and updating databases, assigning user IDs and passwords, editing and customizing reports, correcting data errors, recovering lost data, and troubleshooting. The BioWatch SMS Field Operations Supervisor Guide provides procedures for each task a Field Operations Supervisor performs. The reference information in this guide includes appendices of error messages and solutions, terms used interchangeably, and safety warnings along with a glossary, an index, and a list of suggested reference documents.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

The shape of SN 1993J re-analysed

SN 1993J is one of the best-studied Type IIb supernovae. Spectropolarimetric data analyses were published over two decades ago at a time when the field of supernova spectropolarimetry was in its infancy. Here, we present a new analysis of the spectropolarimetric data of SN 1993J and an improved estimate of its interstellar polarization (ISP) as well as a critical review of ISP removal techniques employed in the field. The polarization of SN 1993J is found to show significant alignment on the q - u plane, suggesting the presence of a dominant axis and therefore of continuum polarization. We also see strong line polarization features, including H β, He i λ5876, H α, He i λ6678, He i λ7065, and high velocity (HV) components of He i λ5876 and H α. SN 1993J is therefore the second example of a stripped-envelope supernova, alongside iPTF13bvn, with prominent HV helium polarization features, and the first to show a likely HV H α contribution. Overall, we determine that the observed features can be interpreted as the superposition of anisotropically distributed line forming regions over ellipsoidal ejecta. We cannot exclude the possibility of an off-axis energy source within the ejecta. These data demonstrate the rich structures that are inaccessible if solely considering the flux spectra but can be probed by spectropolarimetric observations. In future studies, the new ISP corrected data can be used in conjunction with 3D radiative transfer models to better map the geometry of the ejecta of SN 1993J.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series: Preprint

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2019

Discharge data collected within the East River for the Lawrence Berkeley National Laboratory Watershed Function Science Focus Area (water years 2015-present)

Discharge data collected at the East River in the Upper Colorado River Basin for the Lawrence Berkeley National Laboratory's Watershed Function Scientific Focus Area. Files contain instantaneous observed discharge data, corrected 10 min and mean daily discharge. Several sites also contain temperature and transducer depth. Please contact Rosemary Carroll if you have any questions or wish to access the raw data.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as abrupt time series data shifts. These shifts, which are usually unintentional, may be caused by software or hardware configuration changes on a PV system, and do not reflect an actual change in overall system performance. Locating these shifts and segmenting the associated time series aids in more accurate future PV analysis. In this research, an offline changepoint detection (CPD) algorithm that automatically detects these abrupt data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window for the labeled data sets. To promote further research in this space, we are releasing the labeled data shift sets on U.S. Department of Energy's (DOE) DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package.

changepoint detection↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗
DOE OSTI2016

Reconstructing misaligned x-ray CT data

Misalignment errors for x-ray computed tomography (CT) systems can manifest as artifacts and a loss of spatial and contrast resolution. To mitigate artifacts, significant effort is taken to determine the system geometry and minimizing any residual error in the system alignment. This project improved our ability to post-correct data which was acquired on a misaligned CT system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Author Correction: A universal language for finding mass spectrometry data patterns

Correction to: Nature Methodshttps://doi.org/10.1038/s41592-025-02660-z, published online 12 May 2025. This article was originally published under standard Springer Nature license (© The Author(s), under exclusive licence to Springer Nature America, Inc.). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International license, © The Author(s). The error has been corrected in the HTML and PDF versions of the article.

Damiani, Tito↗
DOE OSTI2014

TH-A-18C-10: Dynamic Intensity Weighted Region of Interest Imaging

Purpose: For image guidance tasks full image quality is not required throughout the entire image. With dynamic filtration of the kV imaging beam the noise properties of the CT image can be locally controlled, providing a high quality image around the target volume with a lower quality surrounding region while providing substantial dose sparing to the patient as well as reduced scatter fluence on the detector. Methods: A dynamic collimation device with 3mm copper blades has been designed to mount in place of the bowtie filter on the On-Board Imager (Varian Medical Systems). The beam intensity is reduced by 95% behind the copper filters and the aperture is controlled dynamically to conformally illuminate a given ROI during a standard cone-beam CT scan. A data correction framework to account for the physical effects of the collimator prior to reconstruction was developed. Furthermore, to determine the dose savings and scatter reduction a monte carlo model was built in BEAMnrc with specifics from the Varian Monte Carlo Data Package. The MC model was validated with Gafchromic film. Results: The reconstructed image shows image quality comparable to a standard scan in the specified ROI, with higher noise and streaks in the outer region but still sufficient information for alignment to high contrast structures. The monte carlo modeling showed that the scatter-to-primary ratio was reduced from 1.26 for an unfiltered scan to 0.45 for an intensity weighted scan, suggesting that image quality may be improved in the inner ROI. Dose in the inner region was reduced 10–15% due to reduced scatter and by as much as 75% in the outer region. Conclusion: Dynamic intensity-weighted ROI imaging allows reduction of imaging dose to sensitive organs away from the target region while providing images that retain their utility for patient setup and procedure guidance. Funding was provided in part by Varian Medical Systems and NIH Grants 1RO1CA120540, T32EB002103, S10 RR021039 and P30 CA14599. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of any of the supporting organizations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2013

Nuclear data processing for energy release and deposition calculations in the MC21 Monte Carlo code

With the recent emphasis in performing multiphysics calculations using Monte Carlo transport codes such as MC21, the need for accurate estimates of the energy deposition-and the subsequent heating - has increased. However, the availability and quality of data necessary to enable accurate neutron and photon energy deposition calculations can be an issue. A comprehensive method for handling the nuclear data required for energy deposition calculations in MC21 has been developed using the NDEX nuclear data processing system and leveraging the capabilities of NJOY. The method provides a collection of data to the MC21 Monte Carlo code supporting the computation of a wide variety of energy release and deposition tallies while also allowing calculations with different levels of fidelity to be performed. Detailed discussions on the usage of the various components of the energy release data are provided to demonstrate novel methods in borrowing photon production data, correcting for negative energy release quantities, and adjusting Q values when necessary to preserve energy balance. Since energy deposition within a reactor is a result of both neutron and photon interactions with materials, a discussion on the photon energy deposition data processing is also provided. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Satellite Embedding-Based Population Imputation for Areas with Missing Building Footprint Data: A Computer Vision-Based Approach

High-resolution population modeling is important for supporting effective decision-making across diverse sectors. LandScan Mosaic generates population estimates at the level of individual buildings and aggregates them to 3 arc-second grids, and this approach performs well in regions where building footprint data are comprehensive and reliable. However, large portions of the globe still suffer from incomplete, sparse, or entirely missing building stock datasets, creating a structural limitation for strictly building-based population models. To address this research gap, this study proposes a computer vision-based framework that employs Google Earth Engine satellite embeddings and UNet, which allows us to directly impute grid-level population estimates in building-data-deficient areas. Applied to Taiwan as a case study, the framework achieved strong predictive performance with R$^{2}$ of 0.89, RMSE of 18.70, and MAE of 8.41, outperforming traditional machine learning approaches. Notably, the proposed framework effectively addressed building false-positive errors inherent in Global Human Settlement Layer (GHSL) data, correctly identifying uninhabited areas that were erroneously classified as populated. The framework also offers significant advantages for global population mapping, particularly in terms of scalability and temporal consistency, thereby extending the coverage and accuracy of high-resolution population products in data-scarce regions worldwide. Urban planners, decision makers, and related stakeholders can obtain granular population distributions to support more accurate and targeted infrastructure investment, service delivery, resource allocation, and risk assessment decisions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Sensor Data Management, Validation, Correction, and Provenance for Building Technologies

Oak Ridge National Laboratory (ORNL) conducts research on technologies that use a wide range of sensors to develop and characterize building energy performance. The management of high-resolution sensor data, analysis, and tracing lineage of such activities is challenging. Missing or corrupt data due to sensor failure, fouling, drifting, calibration error, or data logger failure is another issue. This paper focuses on sensor data management, validation, correction, and provenance to combat these issues, ensuring complete and accurate sensor datasets for building technologies applications and research. The design and development of two integrated software products are discussed: Sensor Data Validation and Correction (SensorDVC) and the Provenance Data Management System (ProvDMS) platform.

Data management↗
DOE OSTI2015

A collection of sub-daily pressure and temperature observations for the early instrumental period with a focus on the "year without a summer" 1816

The eruption of Mount Tambora (Indonesia) in April 1815 is the largest documented volcanic eruption in history. It is associated with a large global cooling during the following year, felt particularly in parts of Europe and North America, where the year 1816 became known as the "year without a summer". This paper describes an effort made to collect surface meteorological observations from the early instrumental period, with a focus on the years of and immediately following the eruption (1815–1817). Although the collection aimed in particular at pressure observations, correspondent temperature observations were also recovered. Some of the series had already been described in the literature, but a large part of the data, recently digitised from original weather diaries and contemporary magazines and newspapers, is presented here for the first time. The collection puts together more than 50 sub-daily series from land observatories in Europe and North America and from ships in the tropics. The pressure observations have been corrected for temperature and gravity and reduced to mean sea level. Moreover, an additional statistical correction was applied to take into account common error sources in mercury barometers. To assess the reliability of the corrected data set, the variance in the pressure observations is compared with modern climatologies, and single observations are used for synoptic analyses of three case studies in Europe. All raw observations will be made available to the scientific community in the International Surface Pressure Databank.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Advancing $otsdaq$ for Optimized Data Acquisition

High-energy physics (HEP) experiments demand data acquisition (DAQ) systems capable of orchestrating complex detector operations, high data throughput, and responsive, real-time feedback. Traditional systems often have steep learning curves, making onboarding difficult for new users. The Off-The-Shelf Data Acquisition $otsdaq$ framework was developed to address these issues by providing a modular and flexible interface that is easier to operate while remaining customizable enough for experimental setups. As the upcoming Mu2e experiment prepares for deployment, improving stability, usability, and performance has become increasingly critical. Our work enhances $otsdaq$ with features that streamline visualization, correct data metrics, improve debugging workflows, and stabilize the user interface.

Mohammed, Ali (ORCID:0009000860386626)↗
DOE OSTI2024

Quantifying Error in Photovoltaic Installation Metadata: Preprint

In this research, we quantify the level of metadata error for a fleet of 2860 photovoltaic (PV) systems, using metadata values provided by fleet owners. Using satellite imagery and time series analysis techniques available in open-source Python packages Panel-Segmentation and PVAnalytics, respectively, we evaluate the accuracy of PV system metadata such as location, azimuth, tilt, and mounting configuration (fixed tilt vs. tracking). We find that approximately 75% of provided latitude-longitude coordinates are within 190 meters of the actual solar installation. We were unable to link 7.8% of latitude-longitude coordinates to any solar installation via satellite imagery analysis. We evaluate the level of error in owner-provided mounting configuration (fixed tilt vs. single-axis tracking), finding only 8 systems with an incorrect mounting configuration. When evaluating azimuth and tilt parameters, we find that approximately 64% of the data is correct, with data for 860 systems (approximately 30%) not provided by system owners. To illustrate the importance of having correct solar metadata, we evaluate how incorrect metadata affects solar performance estimates by modeling system AC energy output at ground-truth vs. incorrect latitude-longitude coordinates, mounting configurations, and azimuth-tilt configurations. Energy output estimates can vary significantly if incorrect metadata parameters are used, with incorrect mounting configuration leading to the largest discrepancy with over 20% variation in expected energy output.

azimuth↗
DOE OSTI2019

Attack Identification and Correction for PMU GPS Spoofing in Unbalanced Distribution Systems

Due to the vulnerability of civilian global positioning system (GPS) signals, the accuracy of phasor measurement units (PMUs) can be greatly compromised by GPS spoofing attacks (GSAs), which introduce phase shifts into true phase angle measurements. Focusing on simultaneous GSAs for multiple PMU locations, this paper proposes a novel identification and correction algorithm in distribution systems. A sensitivity analysis of state estimation residuals on a single GSA phase angle is firstly implemented. An identification algorithm using a probing technique is proposed to determine the locations of spoofed PMUs and the ranges of GSA phase shifts. Based on the identification results, these GSA phase shifts are determined via an estimation algorithm that minimizes the mismatch between measurements and system states. Finally, with the attacked PMU data corrected, the system states are recovered. Simulations in unbalanced IEEE 34-bus and 123-bus distribution systems demonstrates the efficiency and accuracy of the proposed method.

97 MATHEMATICS AND COMPUTING↗