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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Optode performance data associated with: Metabolic Multireactor: practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments

This data package is associated with the publication “Metabolic Multireactor: practical considerations for using simple oxygen sensing optodes for high-throughput batch reactor metabolism experiments”, submitted to PlosONE (Kaufman et al. 2023; 10.1101/2023.03.28.534656).We carried out many testing and calibration experiments on a system of small oxygen consumption batch reactors designed for use with water and sediment samples for environmental questions. The oxygen sensing system is based very directly on the work of Larsen, et al. [2011], and similar oxygen sensing technology is widely used in the literature. Our primary focus was on practical considerations, such as temperature effects, lighting angle effects, sterilization, and other similar situations that a user may find useful. Most of the tests required comparing “base” calibration curves to “treatment” calibration curves to determine the extent to which the treatment impacted the reported measurements. This data package contains the performance and calibration data collected for that purpose.This dataset is comprised of one data folder containing (1) file-level metadata; (2) data dictionary; (3) readme; (4) diffusion test result files; (5) limit of detection test result files; (6) temperature impact files; (7) a main data file that contains test results for all other tests; and (8) an R script that uses Kolmogorov-Smirnov tests to determine whether treatment calibrations are significantly different from their respective base calibrations. All files are .csv, .txt, .Rmd, or .pdf.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Public Data Set: The New Pegasus-III Experiment

This public data set contains openly-documented, machine readable digital research data corresponding to figures published in A.C. Sontag et al., 'The New Pegasus-III Experiment,' IEEE Transactions on Plasma Science 50, 4009 (2022).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Real-time data processing for serial crystallography experiments

We report the use of streaming data interfaces to perform fully online data processing for serial crystallography experiments, without storing intermediate data on disk. The system produces Bragg reflection intensity measurements suitable for scaling and merging, with a latency of less than 1 s per frame. Our system uses the CrystFEL software in combination with the ASAP::O data framework. In a series of user experiments at PETRA III, frames from a 16 megapixel Dectris EIGER2 X detector were searched for peaks, indexed and integrated at the maximum full-frame readout speed of 133 frames per second. The computational resources required depend on various factors, most significantly the fraction of non-blank frames ('hits'). The average single-thread processing time per frame was 242 ms for blank frames and 455 ms for hits, meaning that a single 96-core computing node was sufficient to keep up with the data, with ample headroom for unexpected throughput reductions. Further significant improvements are expected, for example by binning pixel intensities together to reduce the pixel count. We discuss the implications of real-time data processing on the `data deluge' problem from recent and future photon-science experiments, in particular on calibration requirements, computing access patterns and the need for the preservation of raw data.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Data Storage for HEP Experiments in the Era of High-Performance Computing

As particle physics experiments push their limits on both the energy and the intensity frontiers, the amount and complexity of the produced data are also expected to increase accordingly. With such large data volumes, next-generation efforts like the HL-LHC and DUNE will rely even more on both high-throughput (HTC) and high-performance (HPC) computing clusters. Full utilization of HPC resources requires scalable and efficient data-handling and I/O. For the last few decades, ROOT has been used by most HEP experiments to store data. However, other storage technologies like HDF5 may perform better in HPC environments. Initial explorations with HDF5 have begun using ATLAS, CMS and DUNE data; the DUNE experiment has also adopted HDF5 for its data-acquisition system. This paper presents the future outlook of the HEP computing and the role of HPC, and a summary of ongoing and future works to use HDF5 as a possible data storage technology for the HEP experiments to use in HPC environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models.

In this paper we study the efficacy of combining machine-learning methods with projection-based model reduction techniques for creating data-driven surrogate models of computationally expensive, high-fidelity physics models. Such surrogate models are essential for many-query applications e.g., engineering design optimization and parameter estimation, where it is necessary to invoke the high-fidelity model sequentially, many times. Surrogate models are usually constructed for individual scalar quantities. However there are scenarios where a spatially varying field needs to be modeled as a function of the model’s input parameters. Here we develop a method to do so, using projections to represent spatial variability while a machine-learned model captures the dependence of the model’s response on the inputs. The method is demonstrated on modeling the heat flux and pressure on the surface of the HIFiRE-1 geometry in a Mach 7.16 turbulent flow. The surrogate model is then used to perform Bayesian estimation of freestream conditions and parameters of the SST (Shear Stress Transport) turbulence model embedded in the high-fidelity (Reynolds-Averaged Navier–Stokes) flow simulator, using shock-tunnel data. The paper provides the first-ever Bayesian calibration of a turbulence model for complex hypersonic turbulent flows. We find that the primary issues in estimating the SST model parameters are the limited information content of the heat flux and pressure measurements and the large model-form error encountered in a certain part of the flow.

42 ENGINEERING↗
DOE OSTI2025

First Global Extraction of Generalized Parton Distributions from Experiment and Lattice Data with Next-to-Leading-Order Accuracy

We report the first global extraction of generalized parton distributions, GUMP 1.0, by combining deeply virtual Compton scattering and 𝜌-meson production data from Jefferson Lab and the Hadron-Electron Ring Accelerator with global fits of parton distribution functions, charge form factors, and lattice quantum chromodynamics simulations. Using a conformal moment space parametrization, we achieve a unified description across low- and high-𝑥 regions at next to leading order accuracy in perturbative corrections. The results provide state-of-the-art generalized parton distributions consistent with almost all known facts, enabling three-dimensional nucleon imaging in impact parameter space and, at the same time, establishing a benchmark for future theoretical and experimental studies of the nucleon structure.

Form factors↗
DOE OSTI2022

Improved Distributed-memory Triangle Counting by Exploiting the Graph Structure

Graphs are ubiquitous in modeling complex systems and representing interactions between entities to uncover structural information of the domain. Traditionally, graph analytics workloads are challenging to efficiently scale (both strong and weak cases) on distributed memory due to the irregular memory-access driven nature (with little or no computations) of the methods. The structure of graphs and their relative distribution over the processing elements poses another level of complexity, making it difficult to attain sustainable scalability across platforms. In this paper, we discuss enhancements to TriC, a distributed-memory implementation of graph triangle counting using Message Passing Interface (MPI), which was featured in the 2020 Graph Challenge competition. We have made some incremental enhancements to TriC, primarily adopting a user-defined buffering strategy to overcome the startup problem for large graphs (by fixing the memory for intermediate data), and experimenting with probabilistic data structures such as bloom filter to improve the query response time for assessing edge existence, at the expense of increasing the overall false positive rate. These adjustments have led to a modest improvements in most cases, as compared to the previous version.

Graph Analytics, HPC↗
DOE OSTI2020

Activity Estimations for HSA Co-60 targets for Cycles 168A, 168B, and 169A Memo

67 HSA cobalt targets have been irradiated in the ATR, starting back on 2/10/15. Individual HSA cobalt targets may have been moved to different positions and irradiated at different times in the ATR. A tool called the Isotope Production and Activation tool (IPA tool) was developed to capture the changes for different irradiation conditions. The IPA tool uses cross section and flux data, cycle history data, and HSA experiment positioning data to estimate a range of specific activities for each of the HSA cobalt targets to generate SCALE/ORIGENS inputs for each target. The IPA tool also post-processes the results for use in tables and figures. The IPA tool derived results that can be independently verified using an acceptable alternative method. The memo contains the specific activity for all 67 HSA cobalt targets.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

SoyFACE Fumigation Data Files

This data set is related to the SoyFACE experiments, which are open-air agricultural climate change experiments that have been conducted since 2001. The fumigation experiments take place at the SoyFACE farm and facility in Champaign County, Illinois during the growing season of each year, typically between June and October. - The "SoyFACE Plot Information 2001 to 2021" file contains information about each year of the SoyFACE experiments, including the fumigation treatment type (CO2, O3, or a combination treatment), the crop species, the plots (also referred to as 'rings' and labeled with numbers between 2 and 31) used in each experiment, important experiment dates, and the target concentration levels or 'setpoints' for CO2 and O3 in each experiment. - This data set includes files with minute readings of the fumigation levels ( "SoyFACE 1-Minute Fumigation Data Files" folder) from the SoyFACE experiments. The "Soyface 1-Minute Fumigation Data Files" folder contains sub-folders for each year of the experiments, each of which contains sub-folders for each ring used in that year's experiments. This data set also includes hourly data files for the fumigation experiments ( "SoyFACE Hourly Fumigation Data Files" folder) created from the 1-minute files, and hourly ambient/weather data files for each year of the experiments ( "Hourly Weather and Ambient Data Files" folder). The ambient CO2 and O3 data are collected at SoyFACE, and the weather data are collected from the SURFRAD and WARM weather stations located near the SoyFACE farm. - The "Fumigation Target Percentages" file shows how much of the time the CO2 and O3 fumigation levels are within a 10 or 20 percent margin of the target levels when the fumigation system is turned on. - The "Matlab Files" folder contains custom code (Aspray, E.K.) that was used to clean the "SoyFACE 1-Minute Fumigation Data" files and to generate the "SoyFACE Hourly Fumigation Data" and "Fumigation Target Percentages" files. Code information can be found in the "SoyFACE Hourly Fumigation Data Explanation" file. - Finally, the " * Explanation" files contain information about the column names, units of measurement, and other pertinent information for each data file. * NOTE: We have identified some files in the “SoyFACE 1-Minute Fumigation Data Files” folder in our SoyFACE data set submission that were not downloaded properly - the files were present in the folder, but the actual files were empty. V3 ensures that there are no longer any empty files in the data set.

agricultural↗
DOE OSTI2023

An intelligent Data Delivery Service for and beyond the ATLAS experiment

The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. It has been designed to intelligently orchestrate workflows and data management systems, decoupling data pre-processing, delivery, and primary processing in large scale workflows. It is an experiment-agnostic service that has been deployed to serve data carousel (orchestrating efficient processing of tape-resident data), machine learning hyperparameter optimization, active learning, and other complex multi-stage workflows defined via DAG (Directed Acyclic Graph), CWL (Common Workflow Language) and other descriptions, including a growing number of analysis workflows. We will at first introduce some deployed use cases in a summary. Then we will focus on new improvements and use cases under developments in ATLAS, Rubin Observatory and sPHENIX, together with future efforts.

97 MATHEMATICS AND COMPUTING↗