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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 1,027 records · Page 57
All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

SU-E-T-237: Monte Carlo Dosimetric Characterization of the Mobetron Mobile Linac

Purpose: The aim of this work is to characterize dosimetrically a clinical intraoperative electron beam accelerator, Mobetron (IntraOp Medical, Inc.) in clinical use in our Hospital. Once this first step is completed our purpose is to evaluate shielding requirements for such a device by preparing adequate phase space files. Methods: It is known that electron beam simulation parameters required for state-of-the-art Monte Carlo codes to obtain a good match with measured data, like the mean energy or the FWHM, may not be code-independent due to the different set of process simulated and formalisms involved. Then, to cross-check our results against any issue in the simulation we have compared experimental data (PDD and profiles for electrons in the range 4 to 12 MeV) with simulations performed independently using both Penelope2011 and Geant4 codes. To do so, the geometry and materials of the head of the accelerator have been fully characterized following information provided by the manufacturer. Results: Both simulations agree with experimental data within experimental uncertainties (±1 mm displacement), although small variations (less than 10%) in the mean energy and FWHM are required to match measured values depending on the code used. Conclusion: Independent Monte Carlo simulations were used to obtain an excellent match to measured electron dose distributions. This opens the road to use such data for evaluating shielding requirements which is the main objective of this project.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

Web-Based Reanalysis Intercomparison Tools (WRIT) for Analysis and Comparison of Reanalyses and Other Datasets

While atmospheric reanalysis datasets are widely used in climate science, many technical issues hinder comparing them to each other and to observations. The reanalysis fields are stored in diverse file architectures, data formats, and resolutions. Their metadata, such as variable name and units, can also differ. Individual users have to download the fields, convert them to a common format, store them locally, change variable names, regrid if needed, and convert units. Even if a dataset can be read via the Open-Source Project for a Network Data Access Protocol (commonly known as OPeNDAP) or a similar protocol, most of this work is still needed. All of these tasks take time, effort, and money. Our group at the Cooperative Institute for Research in the Environmental Sciences at the University of Colorado and affiliated colleagues at the NOAA's Earth System Research Laboratory Physical Sciences Division have expertise both in making reanalysis datasets available and in creating web-based climate analysis tools that have been widely used throughout the meteorological community. To overcome some of the obstacles in reanalysis intercomparison, we have created a set of web-based Reanalysis Intercomparison Tools (WRIT) at www.esrl.noaa.gov/psd/data/writ/. WRIT allows users to easily plot and compare reanalysis datasets, and to test hypotheses. For standard pressure-level and surface variables there are tools to plot trajectories, monthly mean maps and vertical cross sections, and monthly mean time series. Some observational datasets are also included. Users can refine date, statistics, and plotting options. WRIT also facilitates the mission of the Reanalyses.org website as a convenient toolkit for studying the reanalysis datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

SU-D-BRA-02: Motion Assessment During Open Face Mask SRS Using CBCT and Surface Monitoring

Purpose: To assess the robustness of immobilization using open-face mask technology for linac-based stereotactic radiosurgery (SRS) with multiple non-coplanar arcs via repeated CBCT acquisition, with comparison to contemporaneous optical surface tracking data. Methods: 25 patients were treated in open faced masks with cranial SRS using 3–4 non-coplanar arcs. Repeated CBCT imaging was performed to verify the maintenance of proper patient positioning during treatment. Initial patient positioning was performed based on prescribed shifts and optical surface tracking. Positioning refinements employed rigid 3D-matching of the planning CT and CBCT images and were implemented via automated 6DOF couch control. CBCT imaging was repeated following the treatment of all non-transverse beams with associated couch kicks. Detected patient translations and rotations were recorded and automatically corrected. Optical surface tracking was applied throughout the treatments to monitor motion, and this contemporaneous patient positioning data was recorded to compare against CBCT data and 6DOF couch adjustments. Results: Initial patient positions were refined on average by translations of 3±1mm and rotations of ±0.9-degrees. Optical surface tracking corroborated couch corrections to within 1±1mm and ±0.4-degrees. Following treatment of the transverse and subsequent superior-oblique beam, average translations of 0.6±0.4mm and rotations of ±0.4-degrees were reported via CBCT, with optical surface tracking in agreement to within 1.1±0.6mm and ±0.6-degrees. Following treatment of the third beam, CBCT indicated additional translations of 0.4±0.2mm and rotations of ±0.3-degrees. Cumulative couch corrections resulted in 0.7 ± 0.4mm average magnitude translations and rotations of ±0.4-degrees. Conclusion: Based on CBCT measurements of patients during SRS, the open face mask maintained patient positioning to within 1.5mm and 1-degree with >95% confidence. Patient positioning determined by optical surface tracking agreed with CBCT assessment to within 1±1mm and ±0.6-degree rotations. These data support the use of 1–2mm PTV margins and repeated CBCT to maintain stereotactic positioning tolerances.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Quinoa Phenotyping Methodologies: An International Consensus

Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Predicting permeation of compounds across the outer membrane of P. aeruginosa using molecular descriptors

The ability Gram-negative pathogens have at adapting and protecting themselves against antibiotics has increasingly become a public health threat. Data-driven models identifying molecular properties that correlate with outer membrane (OM) permeation and growth inhibition while avoiding efflux could guide the discovery of novel classes of antibiotics. Here we evaluate 174 molecular descriptors in 1260 antimicrobial compounds and study their correlations with antibacterial activity in Gram-negative Pseudomonas aeruginosa. The descriptors are derived from traditional approaches quantifying the compounds’ intrinsic physicochemical properties, together with, bacterium-specific from ensemble docking of compounds targeting specific MexB binding pockets, and all-atom molecular dynamics simulations in different subregions of the OM model. Using these descriptors and the measured inhibitory concentrations, we design a statistical protocol to identify predictors of OM permeation/inhibition. We find consistent rules across most of our data highlighting the role of the interaction between the compounds and the OM. An implementation of the rules uncovered in our study is shown, and it demonstrates the accuracy of our approach in a set of previously unseen compounds. Our analysis sheds new light on the key properties drug candidates need to effectively permeate/inhibit P. aeruginosa, and opens the gate to similar data-driven studies in other Gram-negative pathogens.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

Accelerating the discovery of advanced materials is essential for human welfare and sustainable, clean energy. In this paper, we introduce the Materials Project (www.materialsproject.org), a core program of the Materials Genome Initiative that uses high-throughput computing to uncover the properties of all known inorganic materials. This open dataset can be accessed through multiple channels for both interactive exploration and data mining. The Materials Project also seeks to create open-source platforms for developing robust, sophisticated materials analyses. Future efforts will enable users to perform ''rapid-prototyping'' of new materials in silico , and provide researchers with new avenues for cost-effective, data-driven materials design.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Modern Ethernet Data Acquisition Architecture for Fermilab Beam Instrumentation

The Fermilab Accelerator Division, Instrumentation Department is adopting an open-source framework to replace our embedded VME-based data acquisition systems. Utilizing an iterative methodology, we first moved to embedded Linux, removing the need for VxWorks. Next, we adopted Ethernet on each data acquisition module eliminating the need for the VME backplane in addition to communicating with a rack mount server. Development of DDCP (Distributed Data Communications Protocol), allowed for an abstraction between the firmware and software layers. Each data acquisition module was adapted to read out using 1 GbE and aggregated at a switch which up linked to a 10 GbE network. Current development includes scaling the system to aggregate more modules, to increase bandwidth to support multiple systems and to adopt MicroTCA as a crate technology. The architecture was utilized on various beamlines around the Fermilab complex including PIP2IT, FAST/IOTA and the Muon Delivery Ring. In summary, we were able to develop a data acquisition framework which incrementally replaces VxWorks & VME hardware as well as increases our total bandwidth to 10 Gbit/s using off the shelf Ethernet technology.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Using Low-Cost “Garage Band” Recording Technology for Acquiring High Resolution High-Speed Data

The Oak Ridge National Laboratory (ORNL) has developed and tested a novel system architecture for acquiring high fidelity high-speed data. The approach uses a consumer grade audio recording device that is normally associated with “garage band” recording of music. ORNL has coupled this low-cost data acquisition hardware with computing technology running open-source software. The main advantage of this approach is per-channel cost; an instrument grade data acquisition system typically costs between $800 to $2000 per channel compared to less than $50 per channel for these consumer grade components. Three systems, each featuring four channels, have been deployed for acquiring data from geophones and the electrical supply system that supports the High Flux Isotope Reactor (HFIR) and the Radiochemical Engineering Development Center (REDC) at ORNL. Each channel samples at 96 kHz at 24-bit resolution. The deployed systems operate continuously 24/7 and produce about 4 terabytes of data per month per system. This paper provides a technical overview of this approach, its implementation, and some preliminary results from qualification testing. This work was conducted in support of the Multi-Informatics for Nuclear Operations Scenarios (MINOS).Keywords: Data acquisition Measurement Informatics

Wetherington Jr, G↗
DOE OSTI2025

Data-driven Community-centered Resilient Assessment and Planning Toolkit for Nexus of Energy and Water (DCRAPT-NEW)

Urban areas, including Detroit and Pittsburgh, have suffered significant dual outages of the electrical and water infrastructure in the past decade due, in part, to the increasing number of extreme weather events. With increasing temperatures and rainfall intensity, these regions need to prepare for increasing extreme events through community-based energy and water resilience analysis, planning, and enhancement. This project developed a suite of open-source, open-access, community-centered, data-driven assessment and distributed energy resource (DER) and planning tools for energy and water resilience enhancement in urban areas. Through establishing a multi-level community awareness and engagement mechanism and a comprehensive collection of power outage and flooding data, an innovative group of community energy and water resilience assessment and planning tools have been developed for a wide range of users with differing and variable sets of data available to them. The developed tools include (1) DOE EAGLE-I data-driven, deep-learning assisted resilience assessment and DER planning tools at the county level with socioeconomic factors incorporated; (2) Utility annual power outage data-driven tools for long term resilience assessment and DER planning and 15-min power outage data-driven tools for short term resilience assessment and planning; (3) Detailed engineering tools for energy and water systems resilience assessment and planning when the system topology and component fragility curves are available; (4) Alternative Resiliency Metric Calculation that extracts and separates outage and restoration processes; and (5) Co-optimization tools that evaluate the resilience of the power and sewage system and allow users to conduct joint planning with energy and wastewater systems. The developed tools provide planners, decision-makers, and stakeholders with powerful capabilities to systematically evaluate system/community resilience and optimal and actionable guidance for enhancing resilience while prioritizing DER investments. The tools have been used and validated in Detroit and Pittsburgh and can be used in other areas of the nation. In addition, this project will (1) advance the knowledge and applications of machine-learning methods in analyzing and fusing different layers of information and generating meaningful data points such as generating rare weather events; (2) significantly improve the energy and water resilience of the identified communities in Detroit and Pittsburgh and prepare for more frequent and severe weather conditions; (3) help communities assess extreme weather event impacts and address short-term and long-term resilience-related issues The developed tools have been made public via GitHub and demonstrated to community stakeholders and utility companies via the two annual workshops and numerous community engagement meetings. The project outcomes are also disseminated through publications in various journals and conference proceedings, and presentations at top conferences.

13 HYDRO ENERGY↗
DOE OSTI2026

A Public Data Set of Auto-Generated Geotagged PV Site Equipment, Generated via Deep Learning

In this research, we present a data set over 100 photovoltaic (PV) sites in TX, which have been automatically geotagged via a fully autonomous deep learning (DL) pipeline. Specifically, locations of inverters, tracker/fixed tilt rows, batteries, and substations are labeled algorithmically. To ensure high data quality, all systems have been reviewed manually and any deep learning errors have been corrected. This public data set, as well as the open-sourced pipeline used to generate it, is valuable for site planning, modelling, and insurance purposes. Given time and resources, we hope to extend the data set to additional states/regions in the US.

14 SOLAR ENERGY↗
DOE OSTI2017

Scanning ARM Precipitation Radar (SAPR) Velocity Azimuth Display (VAD)

The Velocity Azimuth Display (VAD, historical term) product contains retrievals of the zonal (north/south) and meridional (east/west) wind speed components, in precipitating systems, as a function of time and height. This product, which uses azimuthal variations in radial air velocity, allows quick insight into wind speed and wind direction in the vicinity of the radar when rainfall or snowfall is present. VAD data are derived from Corrected Moments in Antenna Coordinates Version 2 (CMAC2) using code within the Python ARM Radar Toolkit (Py-ART), an open-source architecture for interacting with radar data in the Python programming language.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Scanning ARM Precipitation Radar (SAPR) Quasi-Vertical Profile (QVP)

The Quasi-Vertical Profiles (QVP) VAP averages quality-controlled radar measurements along the axis of the azimuth. QVP allows quick insight into domain mean hydrometeor (e.g., snow, rain, melting layer) location and polarimetric data. QVP data are derived from Corrected Moments in Antenna Coordinates Version 2 (CMAC2) using code within the Python ARM Radar Toolkit (Py-ART), an open-source architecture for interacting with radar data in the Python programming language.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the possibility of training novel neural network architectures that solve complex partial differential equations (PDEs). Several (nearly data free) approaches have been recently reported that successfully solve PDEs, with examples including deep feed forward networks, generative networks, and deep encoder-decoder networks. However, practical adoption of these approaches is limited by the difficulty in training these models, especially to make predictions at large output resolutions (≥1024×1024). Here we report on a software framework for data parallel distributed deep learning that resolves the twin challenges of training these large SciML models - training in reasonable time as well as distributing the storage requirements. Our framework provides several out of the box functionality including (a) loss integrity independent of number of processes, (b) synchronized batch normalization, and (c) distributed higher-order optimization methods. We show excellent scalability of this framework on both cloud as well as HPC clusters, and report on the interplay between bandwidth, network topology and bare metal vs cloud. We deploy this approach to train generative models of sizes hitherto not possible, showing that neural PDE solvers can be viably trained for practical applications. We also demonstrate that distributed higher-order optimization methods are 2-3× faster than stochastic gradient-based methods and provide minimal convergence drift with higher batch-size.

PDEs↗
DOE OSTI2022

Explainable Bayesian Neural Network for Probabilistic Transient Stability Analysis Considering Wind Energy

While several data-driven models have been developed for transient stability assessment, how to consider the uncertainties from load and renewable generations and provide interpretation of data-driven assessment results are still open. This paper proposes an explainable Bayesian Neural Network (BNN) for probabilistic transient stability assessment (TSA). By extracting the uncertainties from loads and wind farms, the BNN model can make a reliable prediction and quantify the prediction uncertainties. We also develop the Gradient Shap algorithm to make the global and local explanations for the probabilistic TSA model, a significant advantage over existing black-box data-driven methods. Numerical results on the modified IEEE 39-bus system show that the proposed method outperforms the existing methods in terms of prediction accuracy and uncertainty quantification capabilities. The explainability of the proposed method allows system operators to design preventive controls for enhancing system stability.

Bayesian Neural Network↗
DOE OSTI2021

Accelerated, scalable and reproducible AI-driven gravitational wave detection

The development of reusable artificial intelligence (AI) models for wider use and rigorous validation by the community promises to unlock new opportunities in multi-messenger astrophysics. Here we develop a workflow that connects the Data and Learning Hub for Science, a repository for publishing AI models, with the Hardware-Accelerated Learning (HAL) cluster, using funcX as a universal distributed computing service. Using this workflow, an ensemble of four openly available AI models can be run on HAL to process an entire month's worth (August 2017) of advanced Laser Interferometer Gravitational-Wave Observatory data in just seven minutes, identifying all four binary black hole mergers previously identified in this dataset and reporting no misclassifications. This approach combines advances in AI, distributed computing and scientific data infrastructure to open new pathways to conduct reproducible, accelerated, data-driven discovery. By combining a repository for artificial intelligence models and a supercomputing cluster, an entire month's worth of advanced LIGO data is analysed in just 7 min, finding all binary black hole mergers previously identified in this dataset and reporting no misclassifications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Motion Tracking Above and Below Water Dataset

This data set includes one trial of above and under water motion tracking measurements from a Qualysis motion tracking system. The Qualisys native .qtm file can be opened by the Qualisys Track Manager software. Data from this file has been exported to a tab separated value (.tsv) file which is a generic ASCII file format that can be read by a text editor, MATLAB, Excel, etc... Also exported is a native MATLAB formatted file (.mat) which can be loaded directly into MATLAB. The trial is from a three body wave energy converter device, using the underwater system for the central nacelle data, and above water system for the fore and aft floats.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Snekmer: a scalable pipeline for protein sequence fingerprinting based on amino acid recoding

Abstract Motivation The vast expansion of sequence data generated from single organisms and microbiomes has precipitated the need for faster and more sensitive methods to assess evolutionary and functional relationships between proteins. Representing proteins as sets of short peptide sequences (kmers) has been used for rapid, accurate classification of proteins into functional categories; however, this approach employs an exact-match methodology and thus may be limited in terms of sensitivity and coverage. We have previously used similarity groupings, based on the chemical properties of amino acids, to form reduced character sets and recode proteins. This amino acid recoding (AAR) approach simplifies the construction of protein representations in the form of kmer vectors, which can link sequences with distant sequence similarity and provide accurate classification of problematic protein families. Results Here, we describe Snekmer, a software tool for recoding proteins into AAR kmer vectors and performing either (i) construction of supervised classification models trained on input protein families or (ii) clustering for de novo determination of protein families. We provide examples of the operation of the tool against a set of nitrogen cycling families originally collected using both standard hidden Markov models and a larger set of proteins from Uniprot and demonstrate that our method accurately differentiates these sequences in both operation modes. Availability and implementation Snekmer is written in Python using Snakemake. Code and data used in this article, along with tutorial notebooks, are available at http://github.com/PNNL-CompBio/Snekmer under an open-source BSD-3 license. Supplementary information Supplementary data are available at Bioinformatics Advances online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Sustainable Data Evolution Technology (SDET) for Power Grid Optimization (Final Report)

This project develops an innovative sustainable data evolution technology (SDET) to create open-access power grid datasets and facilitate updates to these datasets by the power grid community. The lack of open-access, realistic large-scale datasets significantly limits the ability of researchers, developers and ultimately end users to develop, benchmark and compare new methods and tools for optimizing the operation and planning of the grid, which leads to slow adoption by end users. Available open datasets include the IEEE sponsored 14-300 bus transmission test cases and distribution test feeders, the Edinburgh Power Systems Test Case Archive (39-2224 buses), and the Polish cases (2383-3375 buses) available with MATPOWER. These datasets serve certain purposes, but for power grid optimization, they are insufficient in one or more of these aspects: 1) they are too small; 2) they are incomplete in both models and scenarios for optimization purposes; and 3) they are static and are not keeping up with grid needs. The SDET technology delivers large-scale realistic datasets and data-creation tools capable of generating new datasets. We work closely with the Category-2 data repository project awarded by the same GRID DATA program. The generated datasets and data creation tools are compatible with and available through the data repository. The objective is to make this a sustained effort within and beyond the ARPA-E GRID DATA program so that the datasets can evolve over time and meet the current and future needs for power grid optimization and potentially other applications in power grid operation and planning. The novel and disruptive features of SDET differentiate itself from prior work in the following aspects: 1) SDET derives features and metrics for both transmission and distribution (T&D) systems by analyzing many public and private datasets provided by our industry partners National Rural Electric Cooperative Association (NRECA) and PJM Interconnection (PJM). 2) SDET develops data-creation tools and use these tools to generate large-scale open-access realistic datasets that comply with the metrics for both T&D systems. 3) SDET validates the created datasets using industry tools provided by our vendor partner GE Energy Solution. The data creation tools, as well as the created datasets, are integrated into the data repository (DR. POWER) funded by ARPA-E GRID DATA program in Category 2. Users can use the datasets and access these data creation tools to create and update datasets, thus datasets can evolve per user requirements and power grid development. The SDET is a novel and disruptive technology compared to current ad hoc and static dataset generation. It is expected the SDET technology will fundamentally enhance the development, assessment, and adoption of new methods and tools for power grid optimization and other applications, which in turn would significantly improve the reliability, resiliency, and efficiency of the power grid – a major ARPA-E mission objective.

24 POWER TRANSMISSION AND DISTRIBUTION↗