Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “measurement data”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Combination of inclusive top-quark pair production cross-section measurements using ATLAS and CMS data at $ \sqrt{s} $ = 7 and 8 TeV

A combination of measurements of the inclusive top-quark pair production cross-section performed by ATLAS and CMS in proton–proton collisions at centre-of-mass energies of 7 and 8 TeV at the LHC is presented. The cross-sections are obtained using top-quark pair decays with an opposite-charge electron–muon pair in the final state and with data corresponding to an integrated luminosity of about 5 fb$^{−1}$ at $ \sqrt{s} $ = 7 TeV and about 20 fb$^{−1}$ at $ \sqrt{s} $ = 8 TeV for each experiment. The combined cross-sections are determined to be 178.5 ± 4.7 pb at $ \sqrt{s} $ = 7 TeV and $ {243.3}_{-5.9}^{+6.0} $ pb at $ \sqrt{s} $ = 8 TeV with a correlation of 0.41, using a reference top-quark mass value of 172.5 GeV. The ratio of the combined cross-sections is determined to be R$_{8/7}$ = 1.363 ± 0.032. The combined measured cross-sections and their ratio agree well with theory calculations using several parton distribution function (PDF) sets. The values of the top-quark pole mass (with the strong coupling fixed at 0.118) and the strong coupling (with the top-quark pole mass fixed at 172.5 GeV) are extracted from the combined results by fitting a next-to-next-to-leading-order plus next-to-next-to-leading-log QCD prediction to the measurements. Using a version of the NNPDF3.1 PDF set containing no top-quark measurements, the results obtained are $ {m}_t^{\textrm{pole}}={173.4}_{-2.0}^{+1.8} $ GeV and $ {\alpha}_{\textrm{s}}\left({m}_Z\right)={0.1170}_{-0.0018}^{+0.0021} $.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

UNH TDP - IMU Raw Data and Processing Scripts - Fall 2021

This submission contains raw Yost IMU (Inertial Measurement Unit) data and processing scripts associated with MHKDR submission 394 (UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary, DOI: 10.15473/1973860) from the University of New Hampshire and Atlantic Marine Energy Center (AMEC) turbine deployment platform. The user is directed to the MHKDR submission 394 for relevant context and detail of this deployment; see link below. The 394_READ_ME file here provides the description from that submission for quick reference. The READ_ME file for this specific instrument from the 394 submission is also available here. This submission contains a zipped folder structure containing raw data in its original format and MATLAB (2019a) processing scripts used to process and manipulate the data into its final form. The final data products are submitted in the 394 submission.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Weather Data from BSEC Weather Stations

This dataset provides measurements of temperature, humidity, rainfall, wind, and sunlight at Ambient Weather and OttHydro stations across Baltimore city. These surface weather stations were deployed by the Baltimore Social-Environmental Collaborative (BSEC) Urban Integrated Field Laboratory (UIFL) project, funded by the Department of Energy (DOE). This dataset currently contains measurements from 2023 to June 2026 and will be periodically updated to include more stations and recent observations when available. Data File Information This dataset contains surface weather measurements data in comma-separated value (CSV) format and documents that describe the weather stations, locations, and measured parameters and units. data/[TIMEAVG]/[YEAR]/BSEC-[STATIONID]_[SENSORTYPE]_[TIMEAVG]_[YEAR].csv Surface weather measurements data in CSV format, where STATIONID indicates the weather station, SENSORTYPE is the type of weather station ('AWS' = Ambient Weather Station and 'OTT' = 'OttHydro Station'), TIMEAVG is time period for each entry (= daily, hourly, or 5min), and YEAR indicate the year in which the measurements were made. Example data file name: BSEC-AAC_AWS_hourly_2023.csv. documents/Station_Locations.csv This CSV file provides location information and measurement start date for each surface weather station. documents/Weather_Station_Descriptions.pdf This document provides detailed description of the instruments along with their setup and accuracy of measurement. documents/File_Contents.pdf This document describes the contents on the data files, including time notation, weather parameters and units of measurement. documents/site-metadata/[STATOINID]-metadata.pdf These PDF files provide information on weather station sites, including land cover characteristics, station mounting, and photographs. Each PDF file corresponds to one station, as indicated by STATIONID.

Ambient Weather Stations↗
DOE OSTI2025

Evaluation of New Additions to OLI Software in Predicting Mercuric and Mercurous Species in Liquid Waste Operations

Speciation of mercury during the pretreatment steps of tank waste processing is critical to successful mercury removal prior to vitrification during Liquid Waste Operations (LWO) at SRS. OLI software has been used to predict mercury speciation and activity throughout LWO. The OLI software operates based on a thermodynamic framework called the Mixed Solvent Electrolyte (MSE) framework. The MSE framework allows prediction in theoretically infinitely dilute to concentrated mixtures (e.g., purely solute solutions). Before modification to the MSE framework databanks, certain critical mercury species were missing in the MSE databank, and some thermodynamic data needed to be updated for the OLI software to accurately predict mercury chemical species in SRS waste tanks. To better reflect streams across LWO, new mercury species were integrated into the MSE database. To evaluate the changes to the OLI MSE framework per the Technical Task Request (TTR) and the Task Technical and Quality Assurance Plan (TTQAP), waste stream compositions from Tanks 38, 43, and Tank 50 decontaminated salt solution (DSS) were used as model inputs. Models were developed and executed using both the old and new databases. Compositional analyses from caustic Tank 50 DSS and caustic Tanks 38 and 43 were used as the input streams. These streams represent the most comprehensive chemical data sets where both mercury and tank constituents were measured together. Results for Tank 50 DSS predict HgO as the predominant species in both databases. Both methyl and dimethyl Hg species are present when the new database is ‘on’ and are not predicted with the new database turned ‘off’. The new database predicts a greater amount of HgO and a greater fraction of it in the solid phase. Pourbaix diagrams (potential vs. pH) generated for each Tank 50 DSS were identical regardless of which database was used. Elemental Hg and HgO were predicted in the water stable region under basic conditions. Tanks 38 and 43 follow similar trends as the Tank 50 DSS models. Unlike Tanks 38 and 50 DSS, the Tank 43 Pourbaix plot shows a region of stability for an aqueous HgOHCO3 - species between approximately pH 7-11. In all streams, when MeHg+ is included in the inputs, the new database predicts aqueous MeHgOH as the dominant species. If elemental or dimethyl mercury is in the waste stream, the new database model predicts they are unchanged and remain in those states and quantities. Additionally, the total mercury values are reported for both the measured input data and the OLI output data for all considered tanks. The summary indicates that the percentage error between the measured and calculated values is less than 1% in all cases The reconciliations and generation of the Pourbaix diagrams for Tank 50 DSS took approximately ten times longer with the new database ‘on’. In addition, over the course of that time, models with the new database ‘on’ were more likely to crash or display an error. Some modest performance improvements were noted when modeling with an i7 processor versus an i5. An example error is found in Appendix A. Furthermore, Appendix B provides V&V for two chemical systems analyzed with the OLI software, results were satisfactory. It is recommended to utilize the new databases (i.e., HCO.ddb and SR-Hg.ddb) in future Savannah River Mission Completion applications of OLI to represent pseudo steady-state. Furthermore, the integration and utilization of the new databases (i.e., HCO.ddb and SR-Hg.ddb) in modeling applications (e.g., Aspen) is also recommended.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2021

Optical and Electronic Losses Arising from Physically Mixed Interfacial Layers in Perovskite Solar Cells

Perovskite solar cell device performance is affected by optical and electronic losses. To minimize these losses in solar cells, it is important to identify their sources. Here, we report the optical and electronic losses arising from physically mixed interfacial layers between the adjacent component materials in highly efficient two terminal (2T) all-perovskite tandem, single junction wide-bandgap, and single junction narrow-bandgap perovskite-based solar cells. Physically mixed interfacial layers as the sources of optical and electronic losses are identified from spectroscopic ellipsometry measurements and data analysis followed by comparisons of simulated and measured external quantum efficiency spectra. Parasitic absorbance in the physically mixed regions between silver metal electrical contacts and electron transport layers (ETLs) near the back contact and a physical mixture of commercial indium tin oxide and hole transport layers (HTL) near the front electrical contact lead to substantial optical loss. Here, a lower-density void + perovskite nucleation layer formed during perovskite deposition at the interface between the perovskite absorber layer and the HTL causes electronic losses due to incomplete collection of photogenerated carriers likely originating from poor coverage and passivation of the initially nucleating grains.

14 SOLAR ENERGY↗
DOE OSTI2022

Do blue galaxy-clusters have hot intracluster gas?

Abstract We present herein a systematic X-ray analysis of blue galaxy clusters at z = 0.84 discovered by the Subaru telescope. The sample consisted of 43 clusters identified by combining red-sequence and blue-cloud surveys, covering a wide range of emitter fractions (i.e., 0.3–0.8). The spatial extent of the over-density region of emitter galaxies was approximately 1 Mpc in radius. The average cluster mass was estimated as 0.6(<1.5) × 1014 M⊙ from the stacked weak-lensing measurement. We analyzed the XMM-Newton archival data, and measured the X-ray luminosity of the hot intracluster medium. As a result, diffuse X-ray emission was marginally detected in 14 clusters, yielding an average luminosity of 5 × 1042 erg s−1. To the contrary, it was not significant in 29 clusters. The blue clusters were significantly fainter than the red-dominated clusters, and the X-ray luminosity did not show any meaningful correlation either with emitter fraction or richness. The X-ray surface brightness was low, but the amount of gas mass was estimated to be comparable to that observed in the 1013–1014 M⊙ cluster. Based on the results, we suggest that the blue clusters are at the early formation stage, and the gas is yet to be compressed and heated up to produce appreciable X-rays. Follow-up spectroscopic measurements are essential to clarify the dynamical status and co-evolution of galaxies and hot gas in the blue clusters.

Misato, Rana↗
DOE OSTI2024

Measurements of polarization and spin correlation and observation of entanglement in top quark pairs using lepton + jets events from proton-proton collisions at $\sqrt{𝑠}$ = 13 TeV

Measurements of the polarization and spin correlation in top quark pairs ($t\bar{t}$) are presented using events with a single electron or muon and jets in the final state. The measurements are based on proton-proton collision data from the LHC at $\sqrt{𝑠}$ =13 TeV collected by the CMS experiment, corresponding to an integrated luminosity of 138 fb −1 . All coefficients of the polarization vectors and the spin correlation matrix are extracted simultaneously by performing a binned likelihood fit to the data. The measurement is performed inclusively and in bins of additional observables, such as the mass of the $t\bar{t}$ system and the top quark scattering angle in the $t\bar{t}$ rest frame. The measured polarization and spin correlation are in agreement with the standard model. From the measured spin correlation, conclusions on the $t\bar{t}$ spin entanglement are drawn by applying the Peres-Horodecki criterion. The standard model predicts entangled spins for $t\bar{t}$ states at the production threshold and at high masses of the $t\bar{t}$ system. Entanglement is observed for the first time in events at high $t\bar{t}$ mass, where a large fraction of the $t\bar{t}$ decays are spacelike separated, with an expected and observed significance of above 5 standard deviations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Pantropical Tree Sapwood Hydraulic Properties, 1991 - 2014

This data package contains sapwood pressure-volume parameters including osmotic potential at full turgot, turgor loss point, bulk elastic modulus, and others, determined by curve fitting to raw isotherm data (paired measurements of water content and water potential). Raw data were digitized from published figures. Data are from natural tropical and subtropical forests or savannas and span rainfall gradients from deciduous dry tropical forests to everwet tropical forests, and come from the following countries: Argentina, Panama, Brazil, Costa Rica, and Madagascar. Included in the attached zip file are four CSV files. sapwood_PV_recal_headers.csv: description of variables in the two files below sapwood_PV_recal_corrected.csv: bias-corrected sapwood PV data (see Methods in the associated reference) sapwood_PV_recal_uncorrected.csv: uncorrected sapwood PV data (see Methods in the associated reference) sapwood_PV_references.csv: citation, bibliographic reference, and DOI for the published references from which data were extracted.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Verification, Validation, and Calibration Through a Causal Lens

While typical validation and verification approaches focus on identifying the associations between data elements using statistical and machine learning methods, the novel methods in this paper focus instead on identifying causal relationships between data elements. Statistical and machine-learning-based approaches are strictly data-driven, meaning that they provide quantitative comparison measures between data sets without explicitly considering the hypotheses behind them. This can lead to the erroneous conclusion that, if two data sets are close enough, the models that generated them are similar. In addition, when experimental and simulated data differ to an extent that fails to meet the acceptance criteria, calibration techniques are used to tweak simulation model parameters to reduce the gap between the two types of data. This produces the false expectation that a simulation model will match reality. The methods presented in this paper move away from these strictly data-driven methods for validation and calibration toward more robust, model-driven methods based on causal inference. Causal inference aims to identify the possible mechanisms that might have generated data. Thus, this analysis targets the prediction of the effects when one (or more) of the identified mechanisms are altered. There are many approaches to identify, quantify, and illustrate causal relationships. For the scope of this paper, directed graphs are employed as causal models. If the directed graph lacks cycles, it is known as a directed acyclic graph. A node in such a graph represents an observed data element while a directed edge connecting two nodes represents a causal relationship between two variables. The developed causal methods are designed to extract causal models from simulation models and experimental data. Causal models capture the causal relationships between data elements (e.g., simulated and experimental data). In this context, validation and verification are performed by comparing causal models. The proposed approach does not only inform system analysts on how a simulation model matches real-world data, but also identifies elements of the simulation model that should be revised when discrepancies between simulation and experimental data are observed. Through these causal methods, analysts can identify the portion of the model equation(s) that are behind an edge connecting two variables. Hence, once the structural differences between causal models have been determined, model calibration can occur by changing only those model parameters that impact the identified causal relationships.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Data Analytics Methods to Measure Plant Outage Resilience

Every 18 or 24 months nuclear power plants (depending on plant configuration, pressurized or boiling water reactor respectively) undergo a period of outage where the plant is taken offline and a large number of maintenance and surveillance activities (that cannot be performed while plant is running) are performed in typically 2–3 weeks. Planning of a plant outage is very challenging since all the activities are required to be performed in the shortest amount of time given available resources (typically contractor crews hired for the duration of the outage). Consequently, plant outages can be costly due the actual loss of power generation and crew costs and, because of it, there is a need to maximize resource usage in the outage planning phase and reduce the risk of outage delays. This paper is addressing these needs by providing a set of analytical methods designed to analyze plant outage schedule and identify critical elements based on available resources (time and crews). These methods are based on natural language processing and optimization algorithms. In this respect, two classes of methods have been developed: one that focuses on the time resource and how variability in the time to complete outage tasks may impact outage delays, and one that minimizes the risk of outage delays by integrating available resources to assess when daily activities should be performed.

97 - MATHEMATICS AND COMPUTING↗