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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 1,279 records · Page 71
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

"Hybrid fracture/matrix modeling for well completion options evaluation"

Orientation and completion for well pairs that have been subjected to multi-zonal stimulation play a critical role in the long-term performance of an Enhanced Geothermal Reservoir. Here we present the development of a methodology to rapidly and efficiently numerically simulate mixed fracture-matrix flow systems for evaluation of well design and completion options. The methodology is based on a loose coupling framework, allowing the fracture and matrix systems to be meshed separately. The fracture system includes the integration of fracture growth and aperture data from well stimulation simulations of stochastically generated fracture networks. Automatic mesh refinement is used in the matrix simulation to resolve heat transfer near the fracture network. This simulation framework is used to efficiently determine optimal production and injection well placement using adaptive sampling.

15 GEOTHERMAL ENERGY↗
NASA NTRS1993

Establishment of computerized numerical databases on thermophysical and other properties of molten as well as solid materials and data evaluation and validation for generating recommended reliable reference data

The Center for Information and Numerical Data Analysis and Synthesis, (CINDAS), measures and maintains databases on thermophysical, thermoradiative, mechanical, optical, electronic, ablation, and physical properties of materials. Emphasis is on aerospace structural materials especially composites and on infrared detector/sensor materials. Within CINDAS, the Department of Defense sponsors at Purdue several centers: the High Temperature Material Information Analysis Center (HTMIAC), the Ceramics Information Analysis Center (CIAC) and the Metals Information Analysis Center (MIAC). The responsibilities of CINDAS are extremely broad encompassing basic and applied research, measurement of the properties of thin wires and thin foils as well as bulk materials, acquisition and search of world-wide literature, critical evaluation of data, generation of estimated values to fill data voids, investigation of constitutive, structural, processing, environmental, and rapid heating and loading effects, and dissemination of data. Liquids, gases, molten materials and solids are all considered. The responsibility of maintaining widely used databases includes data evaluation, analysis, correlation, and synthesis. Material property data recorded on the literature are often conflicting, diverging, and subject to large uncertainties. It is admittedly difficult to accurately measure materials properties. Systematic and random errors both enter. Some errors result from lack of characterization of the material itself (impurity effects). In some cases assumed boundary conditions corresponding to a theoretical model are not obtained in the experiments. Stray heat flows and losses must be accounted for. Some experimental methods are inappropriate and in other cases appropriate methods are carried out with poor technique. Conflicts in data may be resolved by curve fitting of the data to theoretical or empirical models or correlation in terms of various affecting parameters. Reasons (e.g. phase transitions) must be found for unusual dependence or any anomaly. Such critical evaluation involves knowledge of theory, experience in measurement, familiarity with metallurgy (microstructural behavior) and not inconsiderable judgment. An examination of typical data compiled and analyzed by CINDAS shows that the thermal conductivity of a material reported in the literature may vary by a factor of two of more; the range of reported values increases as temperature increases reflecting the difficulty of high temperature measurements. Often only estimates of melt behavior are available, despite the importance of melt properties in modeling, welding, or other solidification processes. There may be only a few measurements available for properties such as kinematic viscosity, even for widely used materials such as stainless steel. In the face of such a paucity of existing data and in a national environment where too few new data are being generated it is nonetheless the responsibility of CINDAS to select and disseminate recommended values of a wide variety of thermophysical properties.

Ho, C. Y.↗
DOE OSTI2021

Accurate and confident prediction of electron beam longitudinal properties using spectral virtual diagnostics

Abstract Longitudinal phase space (LPS) provides a critical information about electron beam dynamics for various scientific applications. For example, it can give insight into the high-brightness X-ray radiation from a free electron laser. Existing diagnostics are invasive, and often times cannot operate at the required resolution. In this work we present a machine learning-based Virtual Diagnostic (VD) tool to accurately predict the LPS for every shot using spectral information collected non-destructively from the radiation of relativistic electron beam. We demonstrate the tool’s accuracy for three different case studies with experimental or simulated data. For each case, we introduce a method to increase the confidence in the VD tool. We anticipate that spectral VD would improve the setup and understanding of experimental configurations at DOE’s user facilities as well as data sorting and analysis. The spectral VD can provide confident knowledge of the longitudinal bunch properties at the next generation of high-repetition rate linear accelerators while reducing the load on data storage, readout and streaming requirements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

turbine-models

The (wind) turbine-models GitHub repo hosts an archive/database of turbine models already in the public domain. This includes mainly tabular power and thrust (when available) curve data in .csv files, other technical data, as well as documentation and references built with Sphinx/GitHub Pages. There is a script called "curve_parser.py" to assist with the parsing of power and thrust curves, but this repo is mostly a central location for turbine model data.

Duffy, Patrick↗
NASA NTRS1989

Improved performance in NASTRAN (R)

Three areas of improvement in COSMIC/NASTRAN, 1989 release, were incorporated recently that make the analysis program run faster on large problems. Actual log files and actual timings on a few test samples that were run on IBM, CDC, VAX, and CRAY computers were compiled. The speed improvement is proportional to the problem size and number of continuation cards. Vectorizing certain operations in BANDIT, makes BANDIT run twice as fast in some large problems using structural elements with many node points. BANDIT is a built-in NASTRAN processor that optimizes the structural matrix bandwidth. The VAX matrix packing routine BLDPK was modified so that it is now packing a column of a matrix 3 to 9 times faster. The denser and bigger the matrix, the greater is the speed improvement. This improvement makes a host of routines and modules that involve matrix operation run significantly faster, and saves disc space for dense matrices. A UNIX version, converted from 1988 COSMIC/NASTRAN, was tested successfully on a Silicon Graphics computer using the UNIX V Operating System, with Berkeley 4.3 Extensions. The Utility Modules INPUTT5 and OUTPUT5 were expanded to handle table data, as well as matrices. Both INPUTT5 and OUTPUT5 are general input/output modules that read and write FORTRAN files with or without format. More user informative messages are echoed from PARAMR, PARAMD, and SCALAR modules to ensure proper data values and data types being handled. Two new Utility Modules, GINOFILE and DATABASE, were written for the 1989 release. Seven rigid elements are added to COSMIC/NASTRAN. They are: CRROD, CRBAR, CRTRPLT, CRBE1, CRBE2, CRBE3, and CRSPLINE.

Chan, Gordon C.↗
NASA NTRS1990

Estimating short-period dynamics using an extended Kalman filter

An extended Kalman filter (EKF) is used to estimate the parameters of a low-order model from aircraft transient response data. The low-order model is a state space model derived from the short-period approximation of the longitudinal aircraft dynamics. The model corresponds to the pitch rate to stick force transfer function currently used in flying qualities analysis. Because of the model chosen, handling qualities information is also obtained. The parameters are estimated from flight data as well as from a six-degree-of-freedom, nonlinear simulation of the aircraft. These two estimates are then compared and the discrepancies noted. The low-order model is able to satisfactorily match both flight data and simulation data from a high-order computer simulation. The parameters obtained from the EKF analysis of flight data are compared to those obtained using frequency response analysis of the flight data. Time delays and damping ratios are compared and are in agreement. This technique demonstrates the potential to determine, in near real time, the extent of differences between computer models and the actual aircraft. Precise knowledge of these differences can help to determine the flying qualities of a test aircraft and lead to more efficient envelope expansion.

Bauer, Jeffrey E.↗
NASA NTRS2014

Exploring NASA OMI Level 2 Data With Visualization

Satellite data products are important for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if the satellite data are well utilized and interpreted, such as model inputs from satellite, or extreme events (such as volcano eruptions, dust storms, etc.).Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite data products provided by NASA and other organizations. Such obstacles may be avoided by allowing users to visualize satellite data as images, with accurate pixel-level (Level-2) information, including pixel coverage area delineation and science team recommended quality screening for individual geophysical parameters. We present a prototype service from the Goddard Earth Sciences Data and Information Services Center (GES DISC) supporting Aura OMI Level-2 Data with GIS-like capabilities. Functionality includes selecting data sources (e.g., multiple parameters under the same scene, like NO2 and SO2, or the same parameter with different aggregation methods, like NO2 in OMNO2G and OMNO2D products), user-defined area-of-interest and temporal extents, zooming, panning, overlaying, sliding, and data subsetting, reformatting, and reprojection. The system will allow any user-defined portal interface (front-end) to connect to our backend server with OGC standard-compliant Web Mapping Service (WMS) and Web Coverage Service (WCS) calls. This back-end service should greatly enhance its expandability to integrate additional outside data-map sources.

Level 2↗
NASA NTRS2014

Exploring NASA OMI Level 2 Data With Visualization

Satellite data products are important for a wide variety of applications that can bring far-reaching benefits to the science community and the broader society. These benefits can best be achieved if the satellite data are well utilized and interpreted, such as model inputs from satellite, or extreme events (such as volcano eruptions, dust storms,... etc.). Unfortunately, this is not always the case, despite the abundance and relative maturity of numerous satellite data products provided by NASA and other organizations. Such obstacles may be avoided by allowing users to visualize satellite data as "images", with accurate pixel-level (Level-2) information, including pixel coverage area delineation and science team recommended quality screening for individual geophysical parameters. We present a prototype service from the Goddard Earth Sciences Data and Information Services Center (GES DISC) supporting Aura OMI Level-2 Data with GIS-like capabilities. Functionality includes selecting data sources (e.g., multiple parameters under the same scene, like NO2 and SO2, or the same parameter with different aggregation methods, like NO2 in OMNO2G and OMNO2D products), user-defined area-of-interest and temporal extents, zooming, panning, overlaying, sliding, and data subsetting, reformatting, and reprojection. The system will allow any user-defined portal interface (front-end) to connect to our backend server with OGC standard-compliant Web Mapping Service (WMS) and Web Coverage Service (WCS) calls. This back-end service should greatly enhance its expandability to integrate additional outside data/map sources.

Aura↗
DOE OSTI2012

Raft River Geothermal Area Logical and Fact Data Models

This submission includes fact and logical data models for geothermal data concerning wells, fields, power plants and related analyses at Raft River, ID. The fact model is available in VizioModeler (native), html, UML, ORM-Specific, pdf, and as an XML Spy Project. An entity-relationship diagram is also included. Models are derived from tables, figures and other content in the following reports from the Raft River Geothermal Project: "Technical Report on the Raft River Geothermal Resource, Cassia County, Idaho," GeothermEx, Inc., August 2002. "Results from the Short-Term Well Testing Program at the Raft River Geothermal Field, Cassia County, Idaho," GeothermEx, Inc., October 2004.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

LinkML: an open data modeling framework

Background Scientific research relies on well-structured, standardized data; however, much of it is stored in formats such as free-text lab notebooks, nonstandardized spreadsheets, or data repositories. This lack of structure challenges interoperability, making data integration, validation, and reuse difficult. Findings LinkML (Linked Data Modeling Language) is an open framework that simplifies the process of authoring, validating, and sharing data. LinkML can describe a range of data structures, from flat, list-based models to complex, interrelated, and normalized models that utilize polymorphism and compound inheritance. It offers an approachable syntax that is not tied to any one technical architecture and can be integrated seamlessly with many existing frameworks. The LinkML syntax provides a standard way to describe schemas, classes, and relationships, allowing modelers to build well-defined, stable, and optionally ontology-aligned data structures. Once defined, LinkML schemas may be imported into other LinkML schemas. These key features make LinkML an accessible platform for interdisciplinary collaboration and a reliable way to define and share data semantics. Conclusions LinkML helps reduce heterogeneity, complexity, and the proliferation of single-use data models while simultaneously enabling compliance with FAIR (Findable, Accessible, Interoperable, and Reusable) data standards. LinkML has seen increasing adoption in various fields, including biology, chemistry, biomedicine, microbiome research, finance, electrical engineering, transportation, and commercial software development. In short, LinkML makes implicit models explicitly computable and allows data to be standardized at their origin. LinkML documentation and code are available at https://linkml.io/.

AI-ready data↗
DOE OSTI2019

Equilibrium, kinetic, and thermodynamic studies on the sorption of some heavy metal ions by the phosphorus-containing polymer sorbent

The sorption of Cu+{sup 2}, Ni+{sup 2}, Co+{sup 2}, and Zn+{sup 2} ions onto the phosphorus-containing sorbent were studied as a function of the contact time, temperature, and concentration of the initial metal ions. The equilibrium studies are presented by the Langmuir, Freundlich, and Temkin isotherm models. The sorption data are well described by the Langmuir isotherm model, and they also can be simulated by the Freundlich isotherm model. The pseudo-first-order, pseudo-second-order, and intraparticle diffusion models were applied to test the experimental data of the kinetic studies. The pseudo-second-order kinetic model provided the best correlation of the experimental data compared to the other kinetic models. The sorption capacity of the metal ions increased with temperature increasing. The positive value of enthalpy (ΔH°) and the negative value of Gibbs free energy (ΔG°) indicated that the sorption process was endothermic and spontaneous.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Thick target neutron yields from Beryllium, Carbon, Tungsten, and Lead targets irradiated by 26.7 MeV/nucleon 4 H e ions

The angular and energy distributions of secondary neutrons produced from 26.7 MeV/nucleon 4 He ions, stopping in thick Be, C, W and Pb targets are measured by the time of flight method. The GEANT4, PHITS, and FLUKA Monte Carlo simulation codes with different physics models are employed to simulate the neutron yields and the results are compared with the experimental data. For the thick Be and C targets, GEANT4 with INCL++ reproduces the experimental data reasonably well. Whereas PHITS with JQMD-2.0 overestimates the neutron yields at intermediate energies and FLUKA with PEANUT underestimated the neutron yields at intermediate energies. For W and Pb targets, FLUKA with PEANUT physics model agree with experimental data best. PHITS with JQMD-2.0 overestimates the neutron yield at intermediate energies at 0°, and GEANT4 with INCL++ underestimates the neutron yield at intermediate energies at three angles.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

SU-F-T-477: Investigation of DEFGEL Dosimetry Using MRI

Purpose: The DEFGEL dosimeter/phantom allows for the measurement of 3D dose distributions while maintaining tissue equivalence and deformability. Although DEFGEL is traditionally read out with optical CT, the use of MRI would permit the measurement of 3D dose distributions in optically interfering configurations, like while embedded in a phantom. To the knowledge of the authors, this work is the first investigation that uses MRI to measure dose distributions in DEFGEL dosimeters. Methods: The DEFGEL (6%T) formula was used to create 1 cm thick, 4.5 cm diameter cylindrical dosimeters. The dosimeters were irradiated using a Varian Clinac 21EX linac. The MRI based transverse relaxation rate (R2) of the gel was measured in a central slice of the dosimeter with a Spin-Echo (SE) pulse sequence on a 3T GE SIGNA PET/MR scanner. The R2 values were fit to a monoexponential dose response equation using in-house software (MATLAB). Results: The data was well fit using a monoexponential fit for R2 as a function of absorbed dose (R{sup 2} = 0.9997). The fitting parameters of the monoexponential fit resulted in a 0.1229 Gy{sub −1}s{sub −1} slope. The data also resulted in an average standard deviation of 1.8% for the R2 values within the evaluated ROI. Conclusion: The close fit for the dose response curve shows that a DEFGEL based dosimeter can be paired with a SE MRI acquisition. The Type A uncertainty of the MRI method shows adequate precision, while the slope of the fit curve is large enough that R2 differences between different gel doses are distinguishable. These results suggest that the gel could potentially be used in configurations where an optical readout is not viable, such as measurements with the gel dosimeter positioned inside larger or optically opaque phantoms. This work is partially funded by NIH grant R01CA190298.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

EJFAT: Towards Intelligent Compute Destination Load Balancing

To handle increased data flow, Jefferson Lab (JLab) is partnering with ESnet for development of an AI/ML directed compute work Load Balancer (LB) of UDP streamed data. The LB is FPGA based featuring dynamically configurable, low latency and high throughput destination address switching. The LB provides integration of edge and core computing to support JLab experimental programs, the Electron-Ion Collider, as well as data centers of the future. In the ESnet/JLab FPGA Accelerated Transport (EJFAT) initiative, the function of the LB Data Plane (DP) is to redirect data streams to selectable (but unknown to sender) destination hosts based on current worload and within that host to destination ports as a function of sub- stream id. This effects hierarchical scaling, first across compute machines for processing over a series of events and second, across ports so different data source sub-streams may be assigned to different processors for further parallelization. The LB Control Plane (CP) programs the DP using compute farm telemetry to direct and balance workloads across a compute cluster as the operating conditions require. While Proportional/Integrative/Derivative (PID) controllers are often seen in similar applications, here we investigate the feasibility of a Reinforcement Learning (RL) based schedule manager running in the CP to provide dynamic updates to the DP scheduling policy.

Lawrence, David↗
DOE OSTI2025

WELLS Interactive Application

The Wellbore Exploration and Location Logistic System (WELLS) Interactive Application is an interactive tool to enable easy exploration and visualization of the living national wellbore database (WELLS Database (https://edx.netl.doe.gov/dataset/wells_database)). The tool and underlying database were created and are maintained by the National Energy Technology Laboratory (NETL), providing visualization of the more than six million public wellbore records from more than 65 authoritative state, federal, and tribal resources. The WELLS Interactive Application serves up wellbore data from oil, gas, underground injection, research, geothermal, geotechnical, groundwater, and other types of wells in a single, standardized, unified system. In addition to the surface location of these wells, the underlying database combines select key attributes for features such as well age, depth, and operating status. The system also provides users with references back to the original sources used in this unified platform. The underlying data can be accessed through the WELLS Database: https://edx.netl.doe.gov/dataset/wells_database Additional Information: The WELLS Interactive Application (formerly titled CO2-Locate) enables visualization and access to the public wellbore records through an intuitive web-based mapping tool. The WELLS Interactive Application was designed to help users visualize, query, analyze, and download wellbore records. Public wellbore points are included as a layer in the Map page, called Public Wells. Additionally, a multivariate hexagon grid summarizing well density from proprietary well data, called Well Density, is included to identify data gaps between the public and proprietary well data. Filtering functionalities in the tool allow these two layers to be spatially filtered by state, county, or basin as well as by status, type, true vertical depth, and spud year. The WELLS Interactive Application also contains a Near Me tool can be used to search and explore wellbore data within a user-defined distance of a specified location on the map, which can also be downloaded. The Query tool allows users to query the selected or filtered wells in the Public Wells layer and export the data. For additional information on these tool functionalities, see the help documentation on the About page of the tool. Notes for Consideration: The Well Density layer provided in this application is derived from proprietary wellbore data, the records of which do not always contain values for key features (status, type, true vertical depth, or spud year). Therefore, data might not be available when layers are queried for all filter combinations. Additionally, visualizing layers and applying filters may take additional time to load (i.e., draw on the map) due to the large size of the data.

ccs↗
DOE OSTI2013

Analyses: Environmental Monitoring Well ER-20-5 #1 Pu data

This report updates the results of Pu measurements of groundwater samples from well ER-20- 5#1 collected on April 26, 2011. These analyses were not completed when the original well report was issued. Pu analytical results for this sample are compiled in Table 1. Laboratory analytical protocols are fully described in the LLNL Standard Operating Procedures written in support of the UGTA Project (LLNL, 2004).

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2017

Uncertainty quantification for optical model parameters

Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical potential can result in different cross sections, but these differences have not been systematically studied and quantified. The purpose of our work is to investigate the uncertainties in nuclear reactions that result from fitting a given model to elastic-scattering data, as well as to study how these uncertainties propagate to the inelastic and transfer channels. We use statistical methods to determine a best fit and create corresponding 95% confidence bands. A simple model of the process is fit to elastic-scattering data and used to predict either inelastic or transfer cross sections. In this initial work, we assume that our model is correct, and the only uncertainties come from the variation of the fit parameters. Here, we study a number of reactions involving neutron and deuteron projectiles with energies in the range of 5–25 MeV/u, on targets with mass A=12–208. We investigate the correlations between the parameters in the fit. The case of deuterons on 12 C is discussed in detail: the elastic-scattering fit and the prediction of 12 C(d,p) 13 C transfer angular distributions, using both uncorrelated and correlated χ 2 minimization functions. The general features for all cases are compiled in a systematic manner to identify trends. This work shows that, in many cases, the correlated χ 2 functions (in comparison to the uncorrelated χ 2 functions) provide a more natural parameterization of the process. These correlated functions do, however, produce broader confidence bands. Further optimization may require improvement in the models themselves and/or more information included in the fit.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗