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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

GP Cosmology Surrogate v1.0

GP Cosmology Surrogate is a Python library for building and training a generalized multi-output Gaussian process (GP) framework of @takhtaganov2021cosmic. In this approach, the surrogate is constructed sequentially, guided by a Bayesian optimization acquisition function that targets reduction of emulation error in the regions most consistent with the observational data. This adaptive design concentrates computational resources where they have the greatest impact on inference accuracy. The library supports efficient training for separable GP kernels, which allows the use of Kronecker algebra to handle high-dimensional input spaces and large numbers of correlated outputs. This makes it well suited for applications such as modeling cosmological power spectra, large-scale physical simulations, and multi-output hyperparameter tuning. By combining scalable multi-output GP modeling with data-driven adaptive sampling, GPsurrogate enables parameter inference and optimization with substantially fewer simulations than conventional space-filling designs.

Lukic, Zarija [Lawrence Berkeley National Laborato↗
DOE OSTI2023

Single-pixel reconstructive mid-infrared micro-spectrometer

Miniaturized spectrometers in the mid-infrared (MIR) are critical in developing next-generation portable electronics for advanced sensing and analysis. The bulky gratings or detector/filter arrays in conventional micro-spectrometers set a physical limitation to their miniaturization. In this work, we demonstrate a single-pixel MIR micro-spectrometer that reconstructs the sample transmission spectrum by a spectrally dispersed light source instead of spatially grated light beams. The spectrally tunable MIR light source is realized based on the thermal emissivity engineered via the metal-insulator phase transition of vanadium dioxide (VO 2 ). We validate the performance by showing that the transmission spectrum of a magnesium fluoride (MgF 2 ) sample can be computationally reconstructed from sensor responses at varied light source temperatures. With potentially minimum footprint due to the array-free design, our work opens the possibility where compact MIR spectrometers are integrated into portable electronic systems for versatile applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

JSPEC: A Program for IBS and Electron Cooling Simulation

JSPEC (JLab Simulation Package on Electron Cooling) is an open-source C++ program developed at Jefferson Lab to simulate the evolution of the ion beam under the intrabeam scattering effect and/or the electron cooling effect. JSPEC includes various models of the ion beam, the electron beam, and the friction force, aiming to reflect the latest advances in the field and to provide a useful tool to the community. JSPEC has been benchmarked against other cooling simulation codes and experimental data. It has been used to support the cooler design for JLEIC, an earlier JLab design for the Electron-Ion Collider. A Python wrapper of the C++ code, pyJSPEC, for Python 3.x environment has also been developed and released. It allows users to run JSPEC simulations in a Python environment and makes it possible for JSPEC to collaborate with other accelerator and beam modeling programs, as well as plentiful Python tools in data visualization, optimization, machine learning, etc. In this report, we introduce the features of JSPEC, with a focus on the latest development, and demonstrate how to use JSPEC and pyJSPEC with sample codes and numerical examples.

Accelerator Physics↗
DOE OSTI2023

Use of Electronic Laboratory Notebooks (ELN) in a Laboratory Information Management System (LIMS) [Slides]

The Actinide Analytical Chemistry (CAAC) group at Los Alamos National Laboratory (LANL) uses the LabWare LIMS (LabWare 7) system to manage our samples and material at risk. The LIMS system also provides other functionality which the Plasma Spectroscopy (PS) team has developed along with our LIMS team to provide streamlining and automation of all sample related actions required by PS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Available DT fusion neutron sources

For consideration for diagnostic and technology development for a deuterium and tritium (DT) fusion system, we briefly list here the available DT fusion systems, their characteristic information, such as yields, flux, the pulse width, rep rate and approximate spectrum. We also briefly list other neutron sources for comparison. This is not meant to be exhaustive but a sampling of recent and available sources.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Utilizing Bayesian Optimization for Efficient Dispersion Curve Feature Acquisition [Slides]

Locate features and densely sample near those features. This helps us calculate T 1 which is the magnitude of the deviation from the otherwise smooth monotonically increasing function. Get a generally good idea of the rest of the curve. Potentially incorporate the higher cost of sampling at higher frequencies and the fact that the features at higher frequencies are more valuable to identify. Create a useful physics informed mean function.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

LBNF Material Activation Study

This poster presents a study on material activation at the Fermilab Long-Baseline Neutrino Facility (LBNF), focusing on understanding how high-energy beams interact with surrounding materials to produce radioactive isotopes. Using a simplified model of the LBNF-20 bunker and the FLUKA Monte Carlo simulation tool, the project quantifies isotope production at key locations inside and outside the shielding structures. The study employs a two-step simulation process, tracking primary and secondary particles, and explores the impact of different geometric and material configurations on activation rates. Results include preliminary comparisons of simulated activation rates and suggest methods for refining geometry, improving accuracy, and efficiently simulating future configurations. This work informs the design and operation of the LBNF facility, aiding in radiation safety and shielding optimization. Future efforts will focus on sample iteration, automated simulations, and detailed residual isotope analyses to further enhance the understanding of material activation in high-energy physics environments.

Pietraszek, Jacob [Unlisted, US]↗
DOE OSTI2021

Advancements of On-Line Monitoring and Real-Time Characterization of Actual Low-Level Hanford Tank-Waste Samples

Waste and process stream characterization is essential to the safe, efficient, and cost-effective processing of Hanford Site materials. The demand for materials characterization including chemical, radionuclide, and physical attributes during waste management, transfer, and staging operations in the Hanford tank farms is expected to significantly increase as the U.S. Department of Energy River Protection Project progresses toward Hanford waste treatment and immobilization. Our past work has applied Raman on-line monitoring and chemometric modeling to the supernate of Hanford tank 241-AP-105 and has demonstrated the quantitative measurement of nine analytes within this waste. To demonstrate broader applicability, this method has now been expanded to waste from two additional Hanford tanks, AW-102 and AP-107, and the offgas condensate from lab-scale melter runs of actual-tank waste feeds. To enhance the performance of the Raman method, instruments with various excitation wavelengths were compared, since it is known that the Raman response is enhanced by shorter wavelength excitation. The laser excitations for the three systems tested were 404 nm, 532 nm, and 671 nm. In addition to laser wavelength selection, the laser power, measurement integration times, and signal averaging techniques were also investigated to determine their effect on detection limits for oxy-anion analytes within tank wastes. These advancements in Raman capability are compared with past standards and will be presented.

Bryan, Samuel A.↗
DOE OSTI2026

Reducing systematic bias in machine learning applications to J/ψ signal extraction in high-energy nuclear physics

Machine learning techniques are increasingly used in high-energy nuclear physics because they can exploit multivariate correlations more efficiently than conventional cut-based analyses. A central challenge is the construction of training samples that faithfully reproduce the detector response observed in data. Signal samples are usually derived from detector simulations; therefore, mismatches between simulation and data can degrade classifier performance and introduce systematic biases. This work presents two practical correction procedures, namely cumulative distribution function (CDF) mapping and a shift-and-scale transformation, to align simulated signal features with those measured in data. Their performance is demonstrated with $J$/$\psi$ yield measurements in $\sqrt{s_{nn}}$ = 200 GeV Ru+Ru and Zr+Zr collisions recorded by STAR. A set of self-consistency tests shows that these procedures substantially suppress the systematic bias associated with data-simulation discrepancies in machine-learning-based signal extraction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

i- flow: High-dimensional integration and sampling with normalizing flows

In many fields of science, high-dimensional integration is required. Numerical methods have been developed to evaluate these complex integrals. We introduce the code i-flow, a python package that performs high-dimensional numerical integration utilizing normalizing flows. Normalizing flows are machine-learned, bijective mappings between two distributions. i-flow can also be used to sample random points according to complicated distributions in high dimensions. We compare i-flow to other algorithms for high-dimensional numerical integration and show that i-flow outperforms them for high dimensional correlated integrals. The i-flow code is publicly available on gitlab at https://gitlab.com/i-flow/i-flow.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Reactivity of CO 2 with Utica, Marcellus, Barnett, and Eagle Ford Shales and Impact on Permeability

We report that in order to reduce greenhouse gas emissions while recovering hydrocarbons from unconventional shale formations, processes that make use of carbon dioxide to enhance oil recovery while storing carbon dioxide (CO 2 ) should be considered. Here, we examine samples from three shale basins across the United States (Utica and Marcellus Shales in the Appalachian Basin, Barnett Shale in the Bend Arch-Ft. Worth Basin, and Eagle Ford in the Western Gulf Basin) to address the following questions: (1) do changes from reaction with CO 2 and fluids at the micrometer and nanometer scale alter flow pathways and, in turn, impact hydrocarbon production, CO 2 storage, and seal integrity and (2) can CO 2 or fluid reactivity be predicted based on physical or chemical properties of shale formations? Experiments were conducted at 40 °C and 10.3 MPa to characterize the interaction between CO 2 and shale using X-ray diffraction (XRD), carbon and sulfur analysis, in situ Fourier transform infrared spectroscopy (FT-IR), feature relocation scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), mercury (Hg) intrusion porosimetry, and Brunauer-Emmett-Teller (BET) surface area and pore size analysis coupled with density functional theory (DFT) methods. Changes in mechanical, physical, and flow properties of shale cores due to CO 2 exposure were addressed using a New England Research Autolab 1500 and Xenon X-ray computed tomography (CT) scanning. Results showed that CO 2 did not promote significant reactivity with the shale if water was not present; only shales with swelling clays or residual interstitial pore water reacted with dry CO 2 to promote reactivity in shale. When water was added as a reactant, CO 2 formed carbonic acid and reacted with the shale to dissolve carbonate pockets, etched and pitted the shale matrix surfaces, and increased the microporosity and decreased nanoporosity. Porosity and permeability increased appreciably in core shale samples after exposure to CO 2 saturated fluid due to dissolution of carbonate. Shale mechanical properties were not altered. Trends were not observed that could tie CO 2 or fluid reactivity to physical or chemical properties of the shale formations at the basin scale from the samples we examined. However, if the shale contained significant amounts of carbonate and water was available to react with the CO 2 , pore sizes were altered in the matrix and permeability and porosity increased.

04 OIL SHALES AND TAR SANDS↗
DOE OSTI2025

Strength, deformation, and the fcc–hcp phase transition in condensed Kr and Xe to the 100 GPa pressure range

The rare gas solids exhibit systematic differences in crystal structure, phase transition conditions, bond strength, and other physical properties. The physical properties of heavy rare gas solids krypton and xenon are modified by the martensitic phase transition from face-centered cubic to hexagonal close packed structure over a broad pressure range. Crystal structure, strength, and plastic deformation of krypton and xenon have been investigated at 300 K using compression in the diamond-anvil cell with synchrotron angle-dispersive x-ray diffraction and complementary ruby fluorescence spectroscopy for Xe. Stacking faults indicative of the fcc–hcp phase transition are observed at pressures at and above 1.23 ± 0.05 and 1.9 ± 0.6 GPa in Kr and Xe, respectively. The transition remains incomplete in both solids to pressures greater than 100 GPa. Strength determined from stress measurements in Pt and ruby standards at pressures up to 111 GPa and complemented by observations of strain and texture measurements obtained by x-ray diffraction in the radial geometry to 100 GPa indicates similar or higher strength than Ar at all conditions, with significant stiffening at 15–20 GPa. Radial diffraction data reveal the persistence of broad highly textured fcc diffraction lines to 101 GPa in Xe, suggesting that the axial measurements may underestimate the metastable persistence of the fcc phase due to biased sampling of hcp crystallites resulting from preferred crystallite orientation. Kr and Xe are compared with He, Ne, and Ar for a systematic understanding of physical properties and phase equilibria of rare gas solids.

Compressive stress↗
DOE OSTI2020

Crossing the Streams – Sampler and the TemplateEngine [Slides]

This presentation discusses Sampler, which is a versatile UQ and parametric study tool that can be applied to any SCALE Sequence. Sampler can perturb any quantity in any SCALE input. Recent work at ORNL has developed new types of covariance data that allow Sampler UQ to be applied to nearly all SCALE applications, including reactor depletion, UNF fuel characterization, source term analysis, and decay heat calculation. In SCALE 6.2 releases, CE data in transport cannot be perturbed. Sampler was originally designed for stochastic sampling with any sequence within SCALE and Parametric capability added in SCALE 6.2.2. Sampler can be used for uncertainty quantification, including sample data in static or depletion calculations and sample inputs for uncertainties in compositions and dimensions. The SCALE TemplateEngine allows for expanding templates to full inputs and the combination provides a powerful UQ tool.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Quantum many-body physics from a gravitational lens

The past two decades have seen the emergence of remarkable interconnections among previously remotely related disciplines, such as condensed matter, nuclear physics, gravity and quantum information, fuelled both by experimental advances and by the new powerful theoretical methods offered by holographic duality. In this Review, we sample some recent developments in holographic duality in connection with quantum many-body dynamics. These include insights into strongly correlated phases without quasiparticles and their transport properties, quantum many-body chaos and the scrambling of quantum information. We also discuss recent progress in understanding the structure of holographic duality itself using quantum information, including a ‘local’ version of the duality, as well as the quantum error-correction interpretation of quantum many-body states with a gravity dual, and how such notions help to demonstrate the unitarity of black hole evaporation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Atomic layer epitaxy of kagome magnet Fe 3 Sn 2 and Sn-modulated heterostructures

Magnetic materials with kagome crystal structure exhibit rich physics, such as frustrated magnetism, skyrmion formation, topological flat bands, and Dirac/Weyl points. Until recently, most studies on kagome magnets have been performed on bulk crystals or polycrystalline films. Here, we report the atomic layer molecular beam epitaxy synthesis of high-quality thin films of topological kagome magnet Fe 3 Sn 2 . The structural and magnetic characterization of Fe 3 Sn 2 on epitaxial Pt(111) identifies highly ordered films with c-plane orientation and an in-plane magnetic easy axis. Studies on the local magnetic structure by anomalous Nernst effect imaging reveal in-plane oriented micrometer size domains. Superlattice structures consisting of Fe 3 Sn 2 and Fe 3 Sn are also synthesized by atomic layer molecular beam epitaxy, demonstrating the ability to modulate the sample structure at the atomic level. The realization of high-quality films by atomic layer molecular beam epitaxy opens the door to explore the rich physics of this system and investigate novel spintronic phenomena by interfacing Fe 3 Sn 2 with other materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Model-free estimation of completeness, uncertainties, and outliers in atomistic machine learning using information theory

Abstract An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification (UQ), or extracting physical insights from large datasets. However, atomistic ML often relies on unsupervised learning or model predictions to analyze information contents from simulation or training data. Here, we introduce a theoretical framework that provides a rigorous, model-free tool to quantify information contents in atomistic simulations. We demonstrate that the information entropy of a distribution of atom-centered environments explains known heuristics in ML potential developments, from training set sizes to dataset optimality. Using this tool, we propose a model-free UQ method that reliably predicts epistemic uncertainty and detects out-of-distribution samples, including rare events in systems such as nucleation. This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Lithium-ion battery physics and statistics-based state of health model

A pseudo-2d model using COMSOL Multiphysics® software is developed to simulate performance and performance degradation of Li-ion batteries consisting of layered and olivine cathodes with graphite anode when subjected to peak shaving grid service. Multiple degradation pathways are considered, including solid electrolyte interphase (SEI) formation and breakdown at the anode, cathode dissolution and its synergistic effect on SEI formation at the anode. The model is validated by simulating commercial cylindrical cell performance. A global model is developed to simulate performance across all chemistries, along with individual chemistry models using global model parameters as initial values. There is good agreement between these models for various optimization parameters such as SEI equilibrium potential, cathode dissolution exchange current density, solvent diffusivity in the SEI and SEI ionic conductivity. To circumvent time constraints related to the COMSOL model, a 0d global model is developed which fits data well and provides more clarity on differences in cathode dissolution exchange current density. Again, good agreement for various optimization parameters is obtained among the COMSOL global & individual chemistry models and the 0-d model. The lessons learned from the physics-based model is used to develop a top down statistics-based model using current, voltage and anode volumetric change per mole lithium intercalated, along with their interactions as degradation predictors. This model predicts out of sample degradation for multiple grid services and electric vehicle drive cycle with high accuracy and provides the pathway to develop an efficient battery management system combining machine learning and findings from physics-based computationally intensive algorithms.

Crawford, Aladsair J.↗