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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 OSTI2013

Collective operations in a file system based execution model

A mechanism is provided for group communications using a MULTI-PIPE synthetic file system. A master application creates a multi-pipe synthetic file in the MULTI-PIPE synthetic file system, the master application indicating a multi-pipe operation to be performed. The master application then writes a header-control block of the multi-pipe synthetic file specifying at least one of a multi-pipe synthetic file system name, a message type, a message size, a specific destination, or a specification of the multi-pipe operation. Any other application participating in the group communications then opens the same multi-pipe synthetic file. A MULTI-PIPE file system module then implements the multi-pipe operation as identified by the master application. The master application and the other applications then either read or write operation messages to the multi-pipe synthetic file and the MULTI-PIPE synthetic file system module performs appropriate actions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

ParaText : scalable text analysis and visualization.

Automated analysis of unstructured text documents (e.g., web pages, newswire articles, research publications, business reports) is a key capability for solving important problems in areas including decision making, risk assessment, social network analysis, intelligence analysis, scholarly research and others. However, as data sizes continue to grow in these areas, scalable processing, modeling, and semantic analysis of text collections becomes essential. In this paper, we present the ParaText text analysis engine, a distributed memory software framework for processing, modeling, and analyzing collections of unstructured text documents. Results on several document collections using hundreds of processors are presented to illustrate the exibility, extensibility, and scalability of the the entire process of text modeling from raw data ingestion to application analysis.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRSDate not supplied

Evaluation of a Thermodynamic Ice Crystal Accretion Model using Experimental Data from NASA’s 2nd Fundamental Ice Crystal Icing Test

A thermodynamic model describing ice crystal icing is evaluated using experimental data collected at NASA Propulsion Systems Laboratory. The model makes the distinction between ice accretions that are "freeze-dominated" and "melt-dominated". Freeze-dominated icing occurs when partially melted ice crystals impact a surface and freeze. This type of icing is characterized by strong adhesion. This contrasts with melt-dominated icing, where un-melted ice crystals adhere to a surface. This type of icing is characterized by weak adhesion. In this paper, the thermodynamic model is used to analyze the ice growth rates and facility conditions to explain possible phenomena such as mass loss as well as the melting and freezing fractions. Previous analyses assumed a collection efficiency of unity at the leading edge of the experimental model. LEWICE is used to determine a better estimate of the collection efficiency at the model leading edge as ice accretion grows in time. The result shows that the sticking efficiency could change in a greater percent with time than previously reported.

Ice Crystal Icing↗
DOE OSTI2017

Movement and collision of Lagrangian particles in hydro-turbine intakes: a case study

Studies of the stress/survival of migratory fish during downstream passage through operating hydro-turbines are normally conducted to determine the fish-friendliness of units. One field approach consisting of recording extreme hydraulics with autonomous sensors is largely sensitive to the conditions of sensor release and the initial trajectories at the turbine intake. This study applies a modelling strategy based on flow simulations using computational fluid dynamics and Lagrangian particle tracking to represent the travel of live fish and autonomous sensor devices through hydro-turbine intakes. For the flow field calculation, the simulations were conducted with both a time-averaging turbulence model and an eddy-resolving technique. For the particle tracking calculation, different modelling assumptions for turbulence forcing, mass formulation, buoyancy, and release condition were tested. The modelling assumptions are evaluated with respect to data sets collected using a laboratory physical model and an autonomous sensor device deployed at Ice Harbor Dam (Snake River, State of Washington, U.S.A.) at the same discharge and release point as in the present computer simulations. We found an acceptable agreement between the simulated results and observed data and discuss relevant features of Lagrangian particle movement that are critical in turbine design and in the experimental design of field studies.

CFD↗
DOE OSTI2023

Towards Redefining the Reproducibility in Quantum Computing: A Data Analysis Approach on NISQ Devices

Although the building of quantum computers has kept making rapid progress in recent years, noise is still the main challenge for any application to leverage the power of quantum computing. Existing works addressing noise in quantum devices proposed noise reduction when deploying a quantum algorithm to a specified quantum computer. The reproducibility issue of quantum algorithms has been raised since the noise levels vary on different quantum computers. Importantly, existing works largely ignore the fact that the noise of quantum devices varies as time goes by. Therefore, reproducing the results on the same hardware will even become a problem. We analyze the reproducibility of quantum machine learning (QML) algorithms based on daily model training and execution data collection. Our analysis shows a correlation between our QML models’ test accuracy and quantum computer hardware’s calibration features. We also demonstrate that noisy simulators for quantum computers are not a reliable tool for quantum machine learning applications.

Senapati, Priyabrata↗
DOE OSTI2023

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Background: Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive performance of such forecasts if multiple models are combined into an ensemble. Here, we report on the performance of ensembles in predicting COVID-19 cases and deaths across Europe between 08 March 2021 and 07 March 2022. Methods: We used open-source tools to develop a public European COVID-19 Forecast Hub. We invited groups globally to contribute weekly forecasts for COVID-19 cases and deaths reported by a standardised source for 32 countries over the next 1–4 weeks. Teams submitted forecasts from March 2021 using standardised quantiles of the predictive distribution. Each week we created an ensemble forecast, where each predictive quantile was calculated as the equally-weighted average (initially the mean and then from 26th July the median) of all individual models’ predictive quantiles. We measured the performance of each model using the relative Weighted Interval Score (WIS), comparing models’ forecast accuracy relative to all other models. We retrospectively explored alternative methods for ensemble forecasts, including weighted averages based on models’ past predictive performance. Results: Over 52 weeks, we collected forecasts from 48 unique models. We evaluated 29 models’ forecast scores in comparison to the ensemble model. We found a weekly ensemble had a consistently strong performance across countries over time. Across all horizons and locations, the ensemble performed better on relative WIS than 83% of participating models’ forecasts of incident cases (with a total N=886 predictions from 23 unique models), and 91% of participating models’ forecasts of deaths (N=763 predictions from 20 models). Across a 1–4 week time horizon, ensemble performance declined with longer forecast periods when forecasting cases, but remained stable over 4 weeks for incident death forecasts. In every forecast across 32 countries, the ensemble outperformed most contributing models when forecasting either cases or deaths, frequently outperforming all of its individual component models. Among several choices of ensemble methods we found that the most influential and best choice was to use a median average of models instead of using the mean, regardless of methods of weighting component forecast models. Conclusions: Our results support the use of combining forecasts from individual models into an ensemble in order to improve predictive performance across epidemiological targets and populations during infectious disease epidemics. Our findings further suggest that median ensemble methods yield better predictive performance more than ones based on means. Our findings also highlight that forecast consumers should place more weight on incident death forecasts than incident case forecasts at forecast horizons greater than 2 weeks. Funding: AA, BH, BL, LWa, MMa, PP, SV funded by National Institutes of Health (NIH) Grant 1R01GM109718, NSF BIG DATA Grant IIS-1633028, NSF Grant No.: OAC-1916805, NSF Expeditions in Computing Grant CCF-1918656, CCF-1917819, NSF RAPID CNS-2028004, NSF RAPID OAC-2027541, US Centers for Disease Control and Prevention 75D30119C05935, a grant from Google, University of Virginia Strategic Investment Fund award number SIF160, Defense Threat Reduction Agency (DTRA) under Contract No. HDTRA1-19-D-0007, and respectively Virginia Dept of Health Grant VDH-21-501-0141, VDH-21-501-0143, VDH-21-501-0147, VDH-21-501-0145, VDH-21-501-0146, VDH-21-501-0142, VDH-21-501-0148. AF, AMa, GL funded by SMIGE - Modelli statistici inferenziali per governare l'epidemia, FISR 2020-Covid-19 I Fase, FISR2020IP-00156, Codice Progetto: PRJ-0695. AM, BK, FD, FR, JK, JN, JZ, KN, MG, MR, MS, RB funded by Ministry of Science and Higher Education of Poland with grant 28/WFSN/2021 to the University of Warsaw. BRe, CPe, JLAz funded by Ministerio de Sanidad/ISCIII. BT, PG funded by PERISCOPE European H2020 project, contract number 101016233. CP, DL, EA, MC, SA funded by European Commission - Directorate-General for Communications Networks, Content and Technology through the contract LC-01485746, and Ministerio de Ciencia, Innovacion y Universidades and FEDER, with the project PGC2018-095456-B-I00. DE., MGu funded by Spanish Ministry of Health / REACT-UE (FEDER). DO, GF, IMi, LC funded by Laboratory Directed Research and Development program of Los Alamos National Laboratory (LANL) under project number 20200700ER. DS, ELR, GG, NGR, NW, YW funded by National Institutes of General Medical Sciences (R35GM119582; the content is solely the responsibility of the authors and does not necessarily represent the official views of NIGMS or the National Institutes of Health). FB, FP funded by InPresa, Lombardy Region, Italy. HG, KS funded by European Centre for Disease Prevention and Control. IV funded by Agencia de Qualitat i Avaluacio Sanitaries de Catalunya (AQuAS) through contract 2021-021OE. JDe, SMo, VP funded by Netzwerk Universitatsmedizin (NUM) project egePan (01KX2021). JPB, SH, TH funded by Federal Ministry of Education and Research (BMBF; grant 05M18SIA). KH, MSc, YKh funded by Project SaxoCOV, funded by the German Free State of Saxony. Presentation of data, model results and simulations also funded by the NFDI4Health Task Force COVID-19 ( https://www.nfdi4health.de/task-force-covid-19-2 ) within the framework of a DFG-project (LO-342/17-1). LP, VE funded by Mathematical and Statistical modelling project (MUNI/A/1615/2020), Online platform for real-time monitoring, analysis and management of epidemic situations (MUNI/11/02202001/2020); VE also supported by RECETOX research infrastructure (Ministry of Education, Youth and Sports of the Czech Republic: LM2018121), the CETOCOEN EXCELLENCE (CZ.02.1.01/0.0/0.0/17-043/0009632), RECETOX RI project (CZ.02.1.01/0.0/0.0/16-013/0001761). NIB funded by Health Protection Research Unit (grant code NIHR200908). SAb, SF funded by Wellcome Trust (210758/Z/18/Z).

60 APPLIED LIFE SCIENCES↗
NASA NTRS1987

Geophysical parameters from the analysis of laser ranging to starlette

Starlette Satellite Laser Ranging (SLR) data were used, along with several other satellite data sets, for the solution of a preliminary gravity field model for TOPEX, PTGF1. A further improvement in the earth gravity model was accomplished using data collected by 12 satellites to solve another preliminary gravity model for TOPEX, designated PTGF2. The solution for the Earth Rotation Parameter (ERP) was derived from the analysis of SLR data to Starlette during the MERIT Campaign. Starlette orbits in 1976 and 1983 were analyzed for the mapping of the tidal response of the earth. Publications and conference presentations pertinent to research are listed.

Schutz, B. E.↗
NASA NTRS2015

A Fast Visible-Infrared Imaging Radiometer Suite Simulator for Cloudy Atmopheres

A fast instrument simulator is developed to simulate the observations made in cloudy atmospheres by the Visible Infrared Imaging Radiometer Suite (VIIRS). The correlated k-distribution (CKD) technique is used to compute the transmissivity of absorbing atmospheric gases. The bulk scattering properties of ice clouds used in this study are based on the ice model used for the MODIS Collection 6 ice cloud products. Two fast radiative transfer models based on pre-computed ice cloud look-up-tables are used for the VIIRS solar and infrared channels. The accuracy and efficiency of the fast simulator are quantify in comparison with a combination of the rigorous line-by-line (LBLRTM) and discrete ordinate radiative transfer (DISORT) models. Relative errors are less than 2 for simulated TOA reflectances for the solar channels and the brightness temperature differences for the infrared channels are less than 0.2 K. The simulator is over three orders of magnitude faster than the benchmark LBLRTM+DISORT model. Furthermore, the cloudy atmosphere reflectances and brightness temperatures from the fast VIIRS simulator compare favorably with those from VIIRS observations.

VIIRS Instrument Simulator↗
DOE OSTI2020

StrmtbFlow Fortran Program, Version 2

<span>This zip file contains files storing Fortran source code, compiled files, an executable file for Windows computers, and example input and output files for StmtbFlow. StrmtbFlow is a Fortran program that solves the multiphase flow equations within stream tubes and can be used to estimate the oil produced and CO2 stored from the application of CO2 EOR to an oil field. StrmtbFlow and StrmtbGen comprise the FE/NETL CO2 Prophet Model, which is a streamline/stream tube reservoir simulator for modeling CO2 EOR. StrmtbFlow's user&rsquo;s manual as well as StrmtbGen and its user's manual and a report on the mathematical basis for the FE/NETL CO2 Prophet Model are available on NETL's website under the Collection Name: FE/NETL CO2 Prophet Model.</span><span>&nbsp;</span>

Morgan, David↗
DOE OSTI2020

StrmtbGen Fortran Program, Version 2

This zip file contains files storing Fortran source code, compiled files, an executable file for Windows computers, and example input and output files for StrmtbGen. StrmtbGen is a Fortran program that generates streamlines and stream tubes. StrmtbGen and StrmtbFlow comprise the FE/NETL CO2 Prophet Model, which is a streamline/stream tube reservoir simulator for modeling CO2 EOR. StrmtbGen's user’s manual as well as StrmtbFlow and its user's manual and a report on the mathematical basis for the FE/NETL CO2 Prophet Model are available on NETL's website under the Collection Name: FE/NETL CO2 Prophet Model.

Morgan, David↗
DOE OSTI2016

Online Monitoring of Induction Motors

The online monitoring of active components project, under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program, researched diagnostic and prognostic models for alternating current induction motors (IM). Idaho National Laboratory (INL) worked with the Electric Power Research Institute (EPRI) to augment and revise the fault signatures previously implemented in the Asset Fault Signature Database of EPRI’s Fleet Wide Prognostic and Health Management (FW PHM) Suite software. Induction Motor diagnostic models were researched using the experimental data collected by Idaho State University. Prognostic models were explored in the set of literature and through a limited experiment with 40HP to seek the Remaining Useful Life Database of the FW PHM Suite.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Cycle-by-cycle Delay Estimation at Signalized Intersections by using Machine Learning and Simulated Video Detection Data

Accurate estimation of delay is crucial for efficient traffic signal operations. Estimation of delay in the real-time manner using traditional loop detectors requires advanced detectors (in addition to stop-bar detection). In cases when this detection layout is not in place, delay estimates are approximated with a lower accuracy. Video detection is one of the most frequently deployed detection systems at signalized intersections in recent years. In most cases video detection operates in the same way as traditional inductive loops. However, when coupled with computer vision algorithms, video detection systems could be used to retrieve additional information (e.g., vehicular arrivals and departures) that cannot be taken out from the conventional systems (e.g., long stop-bar loop detectors). Although present for several decades, video detection data were not frequently examined for delay estimation purposes. In this study, we proposed a novel delay estimation model which can be developed with only data from stop-bar video detectors. Relevant data were collected from a simulation model of 11 signalized intersections at downtown Chattanooga, TN and processed to create needed inputs for model development. With the use of multigene genetic programming the authors developed a delay model that outperforms accuracy of multi regression model. Furthermore, authors evaluated the developed model by comparison with the other benchmark delay models, such as HCM and approach delay model. It was found that the developed MGGP delay model outperforms benchmark models for a wide range of traffic and signal operation conditions.

Erdagi, Ismet Goksad↗
NASA NTRS2015

Development, Testing, and Validation of a Model-Based Tool to Predict Operator Responses in Unexpected Workload Transitions

One human factors challenge is predicting operator performance in novel situations. Approaches such as drawing on relevant previous experience, and developing computational models to predict operator performance in complex situations, offer potential methods to address this challenge. A few concerns with modeling operator performance are that models need to realistic, and they need to be tested empirically and validated. In addition, many existing human performance modeling tools are complex and require that an analyst gain significant experience to be able to develop models for meaningful data collection. This paper describes an effort to address these challenges by developing an easy to use model-based tool, using models that were developed from a review of existing human performance literature and targeted experimental studies, and performing an empirical validation of key model predictions.

model-based workload transition tool↗
DOE OSTI2023

Design and Operation of a Multi-Bed Catalytic Micro-Reactor for the Study of Co-Processing of Bio-Oils with VGO

An industry wide shift from fossil-based fuel to renewable fuel sources including biomass, municipal waste, and plastics will require new process monitoring methods to minimize transitional risks including off specification product formation and catalyst deactivation. This project aims to provide a machine learning based process monitoring tool composed of online, slipstream mass spectra for use in biomass refineries and co-processing in existing refineries allowing operators to monitor product qualities and adjust process conditions accordingly. In order to maximize the robustness of the tool, large volumes of data must be collected to fine tune model parameters which consists of both micro and pilot scale mass spectral data. Micro-scale data is collected with a multi-tube micro-reactor housing up to six catalysts in horizontal beds, coupled with a molecular beam mass spectrometer. A pyrolizer equipped with an auto-sampler streamlines the micro-scale data collection process. This type of pyrolizer/micro-reactor configuration does not exist on the market, and therefore had to be created for the purposes of this project. The design and commissioning of this reactor will be presented in detail. This reactor set-up is highly flexible and increases throughput of analysis. For catalyst testing, each bed can be individually selected simply by turning valves. For catalyst reduction and regeneration, simultaneous flow through all six beds is used. The reproducibility of the system was first assessed with whole biomass pyrolysis along with pyrolysis of calibration standards. Initial work on this system evaluated two FCC catalysts, equilibrium catalyst (E-cat), and a proprietary catalyst from Johnson Matthey specifically design for co-processing of bio-oil with vacuum gas oil (VGO). This work used model compounds and VGO which illuminated differences in products produced by the catalysts.

biomass↗
DOE OSTI2025

Enhancing Modeling and Simulation for Effective Protection Strategies

This report was created by Sandia National Laboratories (SNL) to document the principles and methodology of performance data collection and integration with modeling and simulation tools to better facilitate the performance evaluation of physical protection systems (PPS). Results and conclusions from the use of modeling and simulation tools are only as good as the data employed by the tools when conducting analysis. Acquiring the performance testing data necessary to ensure effective evaluation can be a complex and sometimes daunting process. It is the desire of the organization to provide guidance that eases the burdens associated with pursuit of these objectives. This document draws heavily upon longstanding principles of systems engineering that have been developed and employed by SNL in the discipline of security since the 1970s. The scope of this document is constrained to the testing of system components and integration of data that is applicable within the context of PPS performance analysis using two tools that have been developed by SNL, PathTrace© and Scribe3D©. For guidance related to testing and evaluation more broadly, the manuals and reports referenced by this document can be consulted.

42 ENGINEERING↗
DOE OSTI2015

Physics-Based Compact Model for CIGS and CdTe Solar Cells: From Voltage-Dependent Carrier Collection to Light-Enhanced Reverse Breakdown: Preprint

In this paper, we develop a physics-based compact model for copper indium gallium diselenide (CIGS) and cadmium telluride (CdTe) heterojunction solar cells that attributes the failure of superposition to voltage-dependent carrier collection in the absorber layer, and interprets light-enhanced reverse breakdown as a consequence of tunneling-assisted Poole-Frenkel conduction. The temperature dependence of the model is validated against both simulation and experimental data for the entire range of bias conditions. The model can be used to characterize device parameters, optimize new designs, and most importantly, predict performance and reliability of solar panels including the effects of self-heating and reverse breakdown due to partial-shading degradation.

14 SOLAR ENERGY↗
DOE OSTI2021

Analytical Bit-Error Model of NAND Flash Memories for Dosimetry Application

In this work, we provide an analytical model for the total ionizing dose (TID) effects on the bit error statistics of commercial flash memory chips. We have validated the model with experimental data collected by irradiating several commercial NAND flash memory chips from different technology nodes. We find that our analytical model can project bit errors at higher TID values (~ 20 krad(Si)) from measured data at lower TID values (<1 krad(Si)). Based on our model and the measured data, we have formulated basic design rules for using a commercial flash memory chip as a dosimeter. We discuss the impact of NAND chip-to-chip variability, noise margin and the intrinsic errors on the dosimeter design using detailed experimentation.

3-D NAND↗