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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 523 records · Page 29

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DOE OSTI2019

Radiative-capture cross sections for the La 139 ( n , γ ) reaction using thermal neutrons and structural properties of La 140

A set of prompt partial γ-ray production cross sections from thermal neutron capture were measured for the La 139 ( n , γ ) reaction using a guided beam of subthermal (thermal and cold) neutrons incident on a nat La 2 O 3 target at the Prompt Gamma Activation Analysis facility of the Budapest Research Reactor. Absolute La 140 cross sections were determined relative to the well-known comparator 35 Cl(n,γ) cross sections from the irradiation of a stoichiometric nat LaCl 3 sample. Here, the total cross section for radiative thermal neutron capture on La 139 from the sum of experimentally measured cross sections observed to directly feed the La 140 ground state was determined to be $σ$ $^{expt}_{0}$ = 8.58 (50) b.

139La(n, γ), E=thermal and cold↗

DOE OSTI2020

Splash-ML v0.1

The Splash database and portal, also under development (https://lbl.wellspringsoftware.net/kms/invention/detail/3980/), ingests experimental data and references to datasets from a variety of sources, primarily lightsource beamlines. After deployment, it will contain a great deal of metadata and references to data from which many insights can be extracted.

McReynolds, Dylan↗

DOE OSTI2015

A Data Analysis Center for Electromagnetic and Hadronic Interaction. Products of the DAC members

The Data Analysis Center (DAC) of the Center for Nuclear Studies (CNS) at the George Washington University (GW) has made significant progress in its program to enhance and expand the partial-wave (and multipole) analyses of fundamental two- and three-body reactions (such as pion-nucleon, photon-nucleon, and nucleon-nucleon scattering) by maintaining and augmenting the analysis codes and databases associated with these reactions. These efforts provide guidance to experimental groups at the international level, forming an important link between theory and experiment. A renaissance in light hadron spectroscopy is underway as a continuous stream of polarization data issues from existing precision electromagnetic facilities and the coming Jefferson Lab 12 GeV Upgrade. Our principal goals have been focused on supporting the national N* resonance physics program. We have also continued to study topics more generally related to the problems associated with partial-wave analysis. On the Experimental side of the CNS DAC. Its primary goal is the enhancement of the body of data necessary for our analyses of fundamental γ - N reactions. We perform experiments that study the dynamics responsible for the internal structure of the nucleon and its excitations. Our principal focus is on the N* programs at JLab and MAMI. At JLab we study spin-polarization observables using polarized photons, protons and neutrons and yielding charged final states. Similarly at MAMI we study neutral meson photoproduction off polarized protons and neutrons. We use the Crystal Ball and TAPS spectrometers (CBT) to detect photons and neutrons to measure the photoproduction of π0, η, 2π0, π0η, and K0 off the neutron. The CBT program complements our program at JLab, which studies reactions resulting in charged final states. We are also involved in a renewed effort to make neutral pion photoproduction measurements close to threshold at Mainz. In addition to the programs underway, we are contributing to the future by participation in preparations for the coming JLab 12 GeV Upgrade. GW students are involved in tests of the detectors proposed to be used with CLAS12, i.e., for the CentralTime-of-Flight Barrel (CTOF). WJB is heavily involved in the MUSE quest at PSI to solve the Proton Radius Puzzle.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS1996

A Green's function method for high charge and energy ion transport

A heavy-ion transport code using Green's function methods is developed. The low-order perturbation terms exhibiting the greatest energy variation are used as dominant energy-dependent terms, and the higher order collision terms are evaluated using nonperturbative methods. The recently revised NUCFRG database is used to evaluate the solution for comparison with experimental data for 625A MeV 20Ne and 517A MeV 40Ar ion beams. Improved agreements with the attenuation characteristics for neon ions are found, and reasonable agreement is obtained for the transport of argon ions in water.

Energy Transfer↗

DOE OSTI2025

DancePartner: Python Package to Mine Multiomics Relationship Networks from Literature and Databases

A goal of multi-omics experiments is to understand how mechanistic molecular biology is altered between conditions, typically a control group and experimental groups. Oftentimes this involves studying changes in biomolecule relationships (e.g. interactions, metabolic relationships) of several types of biomolecules (e.g. proteins, lipids, metabolites). Though several databases contain relationships between biomolecules, understudied species may have little to no relationship information in databases and thus must be mined from literature. There are several challenges to literature mining, including automated full-text extraction, duplicate biomolecule term collapsing, and implementing complex machine learning tools. To make relationship extraction more accessible to the community, a python package called DancePartner was developed to allow for the extraction of relationships from literature and databases, with functions to map biomolecule synonyms to standardized identifiers and visualize and characterize the resulting multi-omics network. Here, in this study, an example dataset involving Caenorhabditis elegans is presented, where relationships are mined from 1443 publications using DancePartner. These relationships are combined with relationships from KEGG, WikiPathways, UniProt, and LipidMaps, and visualized.

BERT↗

DOE OSTI2021

Computational scanning tunneling microscope image database

We introduce the systematic database of scanning tunneling microscope (STM) images obtained using density functional theory (DFT) for two-dimensional (2D) materials, calculated using the Tersoff-Hamann method. It currently contains data for 716 exfoliable 2D materials. Examples of the five possible Bravais lattice types for 2D materials and their Fourier-transforms are discussed. All the computational STM images generated in this work are made available on the JARVIS-STM website ( https://jarvis.nist.gov/jarvisstm ). We find excellent qualitative agreement between the computational and experimental STM images for selected materials. As a first example application of this database, we train a convolution neural network model to identify the Bravais lattice from the STM images. We believe the model can aid high-throughput experimental data analysis. These computational STM images can directly aid the identification of phases, analyzing defects and lattice-distortions in experimental STM images, as well as be incorporated in the autonomous experiment workflows.

47 OTHER INSTRUMENTATION↗

NASA NTRS1994

Sensitivity of lag-damping correlations to structural and aerodynamic approximations of isolated experimental rotors in forward flight

The predictions of regressive lag-mode damping levels are correlated with the database of an isolated, soft-inplane, three-blade rotor operated untrimmed. The database was generated at the Army Aeroflightdynamics Directorate at Ames. The correlation covers a broad range of data, from near-zero thrust conditions in hover to high-thrust and highly stalled conditions in forward flight with advance ratio as high as 0.55 and shaft angle as high as 20 degrees. In the experimental rotor, the airfoil or blade portion has essentially uniform mass and stiffness distributions, but the root flexure has highly nonuniform mass and stiffness distributions. Accordingly, the structural approximations refer to four models of root-flexure-blade assembly. They range from a rigid flap-lag model to three elastic flap-lag-torsion models, which differ in modeling the root flexure. The three models of root-flexure are: three root springs in which the bending-torsion couplings are fully accounted for; a finite-length beam element with some average mass and stiffness distributions such that the fundamental frequencies match those of the experimental model; and accurate modal representation in which the actual mass and stiffness distributions of the experimental root-flexure-blade assembly are used in calculating the nonrotating mode shapes. The four models of root-flexure-blade assembly are referred to as the rigid flap-lag model, spring model, modified model and modal model. For each of these four models of the root-flexure-blade assembly, the predictions are based on the following five aerodynamic theories: ear theory, which accounts for large angle-of-attack and reverse-flow effects on lift, and has constant drag and pitching moment; quasisteady stall theory, which includes quasisteady stall lift, drag and pitching moment characteristics of the airfoil section, dynamics stall theory, which uses the ONERA dynamic stall models of lift, drag and pitching moment; dynamic wake theory, which is based on a finite-state three-dimensional wake model and includes all wake effects including both shed and trailing vorticity; and dynamics and wake theory, which combines both dynamic stall theory and dynamic wake theory and is a relatively complete aerodynamic representation.

Gaonkar, G. H.↗

DOE OSTI2025

Recent Metallic Fuel Data Recovery in FIPD

The Metallic Fuels Irradiation and Physics Database (FIPD) [1] is an organized collection of metallic fuel test pin data (U-xPu-yZr, = 0 ~ 28; y = 2 ~ 10) and documentation available to industry. FIPD mainly contains three types of data: (1) Fuel pin fabrication data, including fuel slug diameter, fuel slug length, cladding diameter, smear density, etc. (2) Fuel pin operation conditions, including axial distributions for power, temperatures, fluences, burnup, and isotopic densities, etc. and (3) Fuel pin post-irradiation examination (PIE) data, including fission gas release and gas chemistry, profilometry, and neutron radiography, etc. The operating conditions for pins with PIE data available in FIPD span significant ranges across key parameters. The fuel peak burnup extends from less than 5% up to 20 at%. The cladding peak temperature varies from about 490°C to 660°C. Finally, the cladding peak DPA shows a wide range from less than 5 to 120. These broad ranges reflect the diverse testing conditions and operational parameters captured in the available PIE data. More detail about FIPD can be found in ref. [2]. The database development is an ongoing effort covering metallic fuel experiments from the Experimental Breeder Reactor II (EBR-II) and the Fast Flux Test Facility (FFTF). As reported in the ref. [3, 4], most of the PIE data generated during the IFR program [5] has been collected, reviewed, processed, and integrated into FIPD. The most recently added PIE data can be found in ref. [4], which shows the collection of over 95% of the PIE data by the time of this paper. The recent improvements to the database are summarized in this paper.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Artificial intelligence (AI)-based techniques for intelligent contingency management (ICM) require that intelligent agents learn various aspects of system dynamics to create and execute contingencies. For high assurance contingency management, agents achieve the most compelling results through supervised or semi-supervised machine learning, for which agents require large datasets to learn the dynamics of the system. Unfortunately, data collection in aerospace applications can be costly, due to both time and resources. Presented work describes a framework for data augmentation of ICM databases containing training data for machine learning models. This framework populates the database with the outputs of generative adversarial network (GAN) models that were trained on flight data. Methods for evaluating the suitability of these models based on the equations of motion, as well as other physical constraints, are discussed. The paper demonstrates the utility of this database for training intelligent agents on the NASA T2 generic transport aircraft model and experimental vertical takeoff and landing (VTOL) simulation model.

Generative Machine Learning↗

DOE OSTI2021

Development of a Reference Database for Beta-Delayed Neutron Emission

Beta-delayed neutron emission is important for nuclear structure and astrophysics as well as for reactor applications. Significant advances in nuclear experimental techniques in the past two decades have led to a wealth of new measurements that remain to be incorporated in the databases. In this work, we report on a coordinated effort to compile and evaluate all the available β-delayed neutron emission data. The different measurement techniques have been assessed and the data have been compared with semi-microscopic and microscopic-macroscopic models. The new microscopic database has been tested against aggregate total delayed neutron yields, time-dependent group parameters in 6-and 8-group re-presentation, and aggregate delayed neutron spectra. New recommendations of macroscopic delayed-neutron data for fissile materials of interest to applications are also presented. The new Reference Database for Beta-Delayed Neutron Emission Data is available online.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2021

High-throughput design of high-performance lightweight high-entropy alloys

Developing affordable and light high-temperature materials alternative to Ni-base superalloys has significantly increased the efforts in designing advanced ferritic superalloys. However, currently developed ferritic superalloys still exhibit low high-temperature strengths, which limits their usage. Here we use a CALPHAD-based high-throughput computational method to design light, strong, and low-cost high-entropy alloys for elevated-temperature applications. Through the high-throughput screening, precipitation-strengthened lightweight high-entropy alloys are discovered from thousands of initial compositions, which exhibit enhanced strengths compared to other counterparts at room and elevated temperatures. The experimental and theoretical understanding of both successful and failed cases in their strengthening mechanisms and order-disorder transitions further improves the accuracy of the thermodynamic database of the discovered alloy system. This study shows that integrating high-throughput screening, multiscale modeling, and experimental validation proves to be efficient and useful in accelerating the discovery of advanced precipitation-strengthened structural materials tuned by the high-entropy alloy concept.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Validating Molecular Dynamics Simulations against Experimental Observables in Light of Underlying Conformational Ensembles

Far from the static, idealized conformations deposited into structural databases, proteins are highly dynamic molecules that undergo conformational changes on temporal and spatial scales that may span several orders of magnitude. These conformational changes, often intimately connected to the functional roles that proteins play, may be obscured by traditional biophysical techniques. Over the past 40 years, molecular dynamics (MD) simulations have complemented these techniques by providing the “hidden” atomistic details that underlie protein dynamics. However, there are limitations of the degree to which molecular simulations accurately and quantitatively describe protein motions. Here we show that although four molecular dynamics simulation packages (AMBER, GROMACS, NAMD, and il mm) reproduced a variety of experimental observables for two different proteins (engrailed homeodomain and RNase H) equally well overall at room temperature, there were subtle differences in the underlying conformational distributions and the extent of conformational sampling obtained. This leads to ambiguity about which results are correct, as experiment cannot always provide the necessary detailed information to distinguish between the underlying conformational ensembles. Furthermore, the results with different packages diverged more when considering larger amplitude motion, for example, the thermal unfolding process and conformational states sampled, with some packages failing to allow the protein to unfold at high temperature or providing results at odds with experiment. While most differences between MD simulations performed with different packages are attributed to the force fields themselves, there are many other factors that influence the outcome, including the water model, algorithms that constrain motion, how atomic interactions are handled, and the simulation ensemble employed. Here four different MD packages were tested each using best practices as established by the developers, utilizing three different protein force fields and three different water models. Differences between the simulated protein behavior using two different packages but the same force field, as well as two different packages with different force fields but the same water models and approaches to restraining motion, show how other factors can influence the behavior, and it is incorrect to place all the blame for deviations and errors on force fields or to expect improvements in force fields alone to solve such problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Prediction of stability constants of metal–ligand complexes by machine learning for the design of ligands with optimal metal ion selectivity

The new LOGKPREDICT program integrates HostDesigner molecular design software with the machine learning (ML) program Chemprop. By supplying HostDesigner with predicted log K values, LOGKPREDICT enhances the computer-aided molecular design process by ranking ligands directly by metal–ligand binding strength. Harnessing reliable experimental data from a historic National Institute of Standards and Technology (NIST) database and data from the International Union of Pure and Applied Chemistry (IUPAC), we train message passing neural net algorithms. The multi-metal NIST-based ML model has a root mean square error (RMSE) of 0.629 ± 0.044 (R 2 of 0.960 ± 0.006), while two versions of lanthanide-only IUPAC-based ML models have, respectively, RMSE of 0.764 ± 0.073 (R 2 of 0.976 ± 0.005) and 0.757 ± 0.071 (R 2 of 0.959 ± 0.007). For relative log K predictions on an out-of-sample set of six ligands, demonstrating metal ion selectivity, the RMSE value reaches a commendably low 0.25. Here we showcase the use of LOGKPREDICT in identifying ligands with high selectivity for lanthanides in aqueous solutions, a finding supported by recent experimental evidence. We also predict new ligands yet to be verified experimentally. Therefore, our ML models implemented through LOGKPREDICT and interfaced with the ligand design software HostDesigner pave the way for designing new ligands with predetermined selectivity for competing metal ions in an aqueous solution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2008

Comparison of LEWICE and GlennICE in the SLD Regime

A research project is underway at the NASA Glenn Research Center (GRC) to produce computer software that can accurately predict ice growth under any meteorological conditions for any aircraft surface. This report will present results from two different computer programs. The first program, LEWICE version 3.2.2, has been reported on previously. The second program is GlennICE version 0.1. An extensive comparison of the results in a quantifiable manner against the database of ice shapes that have been generated in the GRC Icing Research Tunnel (IRT) has also been performed, including additional data taken to extend the database in the Super-cooled Large Drop (SLD) regime. This paper will show the differences in ice shape between LEWICE 3.2.2, GlennICE, and experimental data. This report will also provide a description of both programs. Comparisons are then made to recent additions to the SLD database and selected previous cases. Quantitative comparisons are shown for horn height, horn angle, icing limit, area, and leading edge thickness. The results show that the predicted results for both programs are within the accuracy limits of the experimental data for the majority of cases.

Wright, William B.↗

DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗

NASA NTRSDate not supplied

Data Augmentation for Intelligent Contingency Management Using Generative Adversarial Neural Networks

Generative Machine Learning↗

DOE OSTI2019

Examining the evidence for decoupling between photosynthesis and transpiration during heat extremes

Recent experimental evidence suggests that during heat extremes, wooded ecosystems may decouple photosynthesis and transpiration, reducing photosynthesis to near zero but increasing transpiration into the boundary layer. This feedback may act to dampen, rather than amplify, heat extremes in wooded ecosystems. We examined eddy covariance databases (OzFlux and FLUXNET2015) to identify whether there was field-based evidence to support these experimental findings. We focused on two types of heat extremes: (i) the 3 days leading up to a temperature extreme, defined as including a daily maximum temperature >37 ° C (similar to the widely used TXx metric), and (ii) heatwaves, defined as 3 or more consecutive days above 35 ° C. When focusing on (i), we found some evidence of reduced photosynthesis and sustained or increased latent heat fluxes at seven Australian evergreen wooded flux sites. However, when considering the role of vapour pressure deficit and focusing on (ii), we were unable to conclusively disentangle the decoupling between photosynthesis and latent heat flux from the effect of increasing the vapour pressure deficit. Outside of Australia, the Tier-1 FLUXNET2015 database provided limited scope to tackle this issue as it does not sample sufficient high temperature events with which to probe the physiological response of trees to extreme heat. Thus, further work is required to determine whether this photosynthetic decoupling occurs widely, ideally by matching experimental species with those found at eddy covariance tower sites. Such measurements would allow this decoupling mechanism to be probed experimentally and at the ecosystem scale. Transpiration during heatwaves remains a key issue to resolve, as no land surface model includes a decoupling mechanism, and any potential dampening of the land–atmosphere amplification is thus not included in climate model projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Experimental Wargaming with SIGNAL

Wargames are a common tool for investigating complex conflict scenarios and have a long history of informing military and strategic study. Historically, these games have often been one-offs, may not rigorously collect data, and have been built primarily for exploration rather than developing data-driven analytical conclusions. Experimental wargaming, a new wargaming approach that employs the basic principles of experimental design to facilitate an objective basis for exploring fundamental research questions around human behavior (such as understanding conflict escalation), is a potential tool that can be used in combination with existing wargaming approaches. The Project on Nuclear Gaming, a consortium involving the University of California, Berkeley, Sandia National Laboratories, and Lawrence Livermore National Laboratory, developed an experimental wargame, SIGNAL, to explore questions surrounding conflict escalation and strategic stability in the nuclear context. To date, the SIGNAL experimental wargame has been played hundreds of times by thousands of players from around the world, creating the largest database of wargame data for academic purposes known to the authors. Here, this paper discusses the design of SIGNAL, focusing on how the principles of experimental design influenced this design.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗