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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

GeSn alloys with ∼21% Sn grown by effusion cell molecular beam epitaxy

We report the epitaxial growth of high quality GeSn alloys with Sn compositions up to 21.25 ± 1% by effusion-cell molecular beam epitaxy (MBE). Achieving such a high Sn content with effusion cells is particularly significant, since these sources typically impose stronger radiative heating on the substrate, which is a factor long considered a major obstacle to high-Sn GeSn epitaxy. Contrary to the prevailing assumption that high growth rates are good for suppressing Sn segregation, we demonstrate that carefully controlled low fluxes, combined with stable ultra-low substrate temperatures, enable significant Sn incorporation even under effusion-cell conditions. Structural analyses by high-resolution x-ray diffraction, reciprocal space mapping, atomic force microscopy, and transmission electron microscopy confirm sharp interfaces, high crystallinity, and smooth surfaces at high Sn contents. Furthermore, these findings establish the feasibility of MBE-grown high-Sn GeSn even under diffusion cells, thereby broadening opportunities for Si-compatible photonic and quantum devices.

Alloys↗
DOE OSTI2023

Measurements of neutrino oscillation parameters from the T2K experiment using $$3.6\times 10^{21}$$ protons on target

Abstract The T2K experiment presents new measurements of neutrino oscillation parameters using $$19.7(16.3)\times 10^{20}$$ 19.7 ( 16.3 ) × 10 20 protons on target (POT) in (anti-)neutrino mode at the far detector (FD). Compared to the previous analysis, an additional $$4.7\times 10^{20}$$ 4.7 × 10 20 POT neutrino data was collected at the FD. Significant improvements were made to the analysis methodology, with the near-detector analysis introducing new selections and using more than double the data. Additionally, this is the first T2K oscillation analysis to use NA61/SHINE data on a replica of the T2K target to tune the neutrino flux model, and the neutrino interaction model was improved to include new nuclear effects and calculations. Frequentist and Bayesian analyses are presented, including results on $$\sin ^2\theta _{13}$$ sin 2 θ 13 and the impact of priors on the $$\delta _{\textrm{CP}}$$ δ CP measurement. Both analyses prefer the normal mass ordering and upper octant of $$\sin ^2\theta _{23}$$ sin 2 θ 23 with a nearly maximally CP-violating phase. Assuming the normal ordering and using the constraint on $$\sin ^2\theta _{13}$$ sin 2 θ 13 from reactors, $$\sin ^2\theta _{23}=0.561^{+0.021}_{-0.032}$$ sin 2 θ 23 = 0 . 561 - 0.032 + 0.021 using Feldman–Cousins corrected intervals, and $$\varDelta {}m^2_{32}=2.494_{-0.058}^{+0.041}\times 10^{-3}~\text {eV}^2$$ Δ m 32 2 = 2 . 494 - 0.058 + 0.041 × 10 - 3 eV 2 using constant $$\varDelta \chi ^{2}$$ Δ χ 2 intervals. The CP-violating phase is constrained to $$\delta _{\textrm{CP}}=-1.97_{-0.70}^{+0.97}$$ δ CP = - 1 . 97 - 0.70 + 0.97 using Feldman–Cousins corrected intervals, and $$\delta _{\textrm{CP}}=0,\pi $$ δ CP = 0 , π is excluded at more than 90% confidence level. A Jarlskog invariant of zero is excluded at more than $$2\sigma $$ 2 σ credible level using a flat prior in $$\delta _{\textrm{CP}},$$ δ CP , and just below $$2\sigma $$ 2 σ using a flat prior in $$\sin \delta _{\textrm{CP}}.$$ sin δ CP . When the external constraint on $$\sin ^2\theta _{13}$$ sin 2 θ 13 is removed, $$\sin ^2\theta _{13}=28.0^{+2.8}_{-6.5}\times 10^{-3},$$ sin 2 θ 13 = 28 . 0 - 6.5 + 2.8 × 10 - 3 , in agreement with measurements from reactor experiments. These results are consistent with previous T2K analyses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

BCL Gem [SWR 21-64]

This repository contains all the methods to build, test, and release the gem available as rake tasks via bundler/gem_tasks.

Long, Nicholas↗
DOE OSTI2021

DEPRECATED AI-Batt-OS (Autonomous Identification of Battery Life Models - Open Source) [SWR 21-17]

DEPRECATED. This repository was archived by the owner on Jun 30, 2026. It is now read-only. Open source implementation of some of the methods utilized by AI-Batt, a battery lifetime modeling and analysis toolkit provided by the National Laboratory of the Rockies (NLR). This software demonstrates the use of bi-level optimization and symbolic regression techniques to semi-autonomously identify algebraic models predicting the capacity fade of lithium-ion batteries during calendar aging. Modeling the degradation of batteries is a complex task, due to the difficulty in separating the time-dependent and time-independent factors impacting cell level degradation, across multiple data series with different numbers of measurements and/or data quality. Bi-level optimization enables model parameters to be optimized to either the entire data set or to individual data series, allowing statistical disambiguation of global behaviors (data series independent) and local behaviors (data series dependent). Symbolic regression is used to automatically search for optimal low-dimesional models predicting the variation of locally optimized parameters versus time-independent experimental variables from millions of possible models, resulting in a more accurate and repeatable model identification process than is possible by a manual search. The provided tools also implement cross-validation and bootstrap resampling schemes, empowering statistical model comparison/selection and quantification of model uncertainties. An example script replicates the results from the manuscript "Challenging Practices of Algebraic Battery Life Models through Statistical Validation and Model Identification via Machine-Learning", submitted to ECS. All code is written in MATLAB. Requires the Statistics and Machine Learning Toolbox. Contact Dr. Paul Gasper at Paul.Gasper@nlr.gov for any questions.

Gasper, Paul↗
DOE OSTI2021

AI-Batt (Autonomous Identification of Battery Life Models) [SWR 21-36]

Autonomous Identification of Battery Life Models (AI-Batt) AI-Batt is a MATLAB code base for developing lifetime models for batteries from accelerated aging data. The code base provides many functions for processing, visualizing, and modeling battery aging data, making the data processing, exploration, and modeling workflow substantially faster. These tools are tailored for working with battery aging data sets, which usually consist of many separate time-series for each cell, with many test conditions and possible replicates at each condition, which makes it difficult to simply process or visualize the data set. Complex modeling tasks, such as cross-validation, sensitivity analysis, and uncertainty quantification have been implemented to enable thorough statistical investigation of model predictions. Additionally, several machine-learning algorithms are implemented to autonomously identify suitable models via symbolic regression. Data processing functions automatically cast data from the struct data type, which is commonly used to store experimental data, but is not an acceptable input for most algorithms, to the table data type, which can be easily used as input to any optimization algorithm. Also, the data can be separated into time-invariant and time-variant data tables, which is helpful for exploring the data set as well as developing separate models for time-variant and time-invariant aging mechanisms. For example, in aging tests with constant temperature, temperature is a time-invariant experimental condition. Visualization tools enable plotting of data, model fits, and model simulations possible with single-line function calls, empowering data exploration of complex data sets with both time-varying and time-invariant trends. Plots can be automatically generated for the whole data set, or separated by data group (groups of test replicates) or individual data series. Data points or data series can be automatically colored by the value of a variable with a variety of color maps, and model predictions can also be colored by the value of a fit statistic. Comparisons between data sets and the predictions/simulations of different models on the same data set can be easily plotted as well. Distributions of parameter values from bootstrap resampling can be plotted to visualize the reliability of parameter estimation, or determine any correlations between parameters. Modeling tools handle the complex task of creating and parsing symbolic equations for modeling battery lifetime. Equations are parsed to grab relevant data variables, parameter values, or specified sub-models for input into optimization, evaluation, or simulation functions. Models can be optimized locally (one set of parameters for each data series), bi-level (some parameters shared across the data set), or globally (single set of parameters for all data). Functions implementing symbolic regression algorithms help users to discover effective model equations, even in poorly sampled, high-dimensional data.

Smith, Kandler↗
DOE OSTI2021

Human Performance Improvement Task Group Task 21-1 Best Practice: Using virtual capabilities or options for HPI application (to reduce errors, strengthening defenses, strengthening the organization, and/or increasing capacity)

In 2020 the world was impacted by COVID-19. How work is done was dramatically changed for a large portion of the workforce. Many have made the abrupt shift to working from their home while others have adapted to COVID protocols (such as social distancing and wearing facemasks). Fundamentally, Human and Organizational Performance Improvement concepts and science did not change – only the delivery method changed (in some cases). The primary effect has been to accelerate dynamics already present in society – from e-commerce to online education to remote healthcare. Change is never easy for humans. However, remote work should be viewed as an opportunity; not a fix to a problem. In this Best Practice paper, you’ll get practical suggestions to help you reduce errors and improve Human Performance in remote, socially-distanced “work-from-home” environments.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Nominal oxygen parameters for RHIC Run 21

In addition to gold ions, oxygen ions have been provided for oxygen-oxygen collisions in RHIC during the FY21 running period. The relevant parameters are summarized in this document.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Summary of Assessments Performed in FY 21 by SNL QA POC for FY 20 SNL NFCSC Milestone Deliverables

The Sandia National Laboratories (SNL) staff is meeting the requirements of the Nuclear Fuel Cycle and Supply Chain (NFCSC) Quality Assurance Program Document (QAPD). Each of the 5 NFCSC FY 20 packages for SNL were reviewed. None of the 5 packages had incorrect QRL categories. No major corrective actions are assigned. Additional and minor PICS:NE checkbox errors may exist, but none were identified in this assessment. Training is now also geared to encourage WPMs to take credit for optional technical reviews by considering setting the QRL level to 3. This is because when the level is set to 4, the PICS:NE system does not provide fields for review information. Finally, training in the NFCSC QAPD has been delivered as always to WPMs.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Experimental Searches for Muon to Electron Conversion in a Nucleus: COMET, DeeMe, and Mu2e (A Contributed paper for Snowmass 21)

Searches for charged lepton flavor violation (CLFV) offer deep probes for a range of new physics scenarios, such as super-symmetric models, theories involving scalar leptoquarks or additional Higgs doublets, and models explaining the neutrino mass hierarchy and the matter-antimatter asymmetry of the universe via leptogenesis. The coherent, neutrinoless conversion of a muon to an electron in the field of a nucleus, $\mu^{-}+N\rightarrow e^{-} +N$, is one example of a muonic CLFV process which has sensitivity to this new physics. This paper details three experiments: COMET, DeeMe and Mu2e which will search for $\mu^{-}+N\rightarrow e^{-} +N$ in the coming decade. These experiments offer sensitivity up to an effective new physics mass scale of $\mathcal{O}(10^{4}$ TeV/c$^{2}$), going far beyond what can be achieved in direct, collider-based, searches. The theoretical motivation, designs and anticipated timelines for these three experiments are presented. These experiments are a crucial part of a global search for CLFV. Continued support for all planned experimental searches for muonic CLFV is strongly encouraged.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Niowave Flow Testing of the High Power Converter - FY 21

Design work for a 100 kW converter to produce a neutron flux via electron impact on a flowing stream of liquid metal lead-bismuth (LBE) was conducted in FY20. In FY21, the focus shifted to a 20 kW converter which was designed in collaboration with Niowave engineers. A flow visualization model is needed to validate model results for the 20 kW converter and ensure adequate coolant flow over containment structures. This work highlights the mockup that was made with quartz side plates by LANL.

42 ENGINEERING↗
DOE OSTI2022

NorthStar Facility Target Fab and Flow Testing - FY 21

The facility target fabrication and flow testing activity will consist of flow tests using the blower system at LANL to test the disk holder and target, both redesigned by the NorthStar prime contractor. The housing and transition system delivering helium cooling to the disc holder and target will be fabricated and tested at LANL facilities. The tests will include pressure drop versus flow measurements through the target and housing, optical flow diagnostics and fiber optic imaging of the disc stack. The pressure drop measurements will be compared to CFD simulations to verify the latter, the optical flow diagnostics will be used to determine whether modifications made to the housing and target holder allow sufficient flow through the bottom of the housing to adequately cool it and the fiber optics will be used to image the discs during operation as a proof of concept of the diagnostic, to be used at NorthStar facilities during preproduction testing. The work is a Partial Fulfillment of the Deliverable Requirement for NorthStar Facility Target Fab and Flow Testing and Production Target Testing Support by LANL

42 ENGINEERING↗
DOE OSTI2022

Niowave Design and Analysis of a High Power Converter - FY 21

LANL provided support to Niowave Inc. on the design and analysis of the Mk. 4 and Mk. 5, 20 kW converter. The Mk. 4 converter design consisted of lead bismuth eutectic (LBE) flowing over a divider plate to create a waterfall. Niowave, with feedback from LANL, improved on the Mk.4 divider plate and “horn” region of the converter to create the Mk. 5 version of the converter.

42 ENGINEERING↗