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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Neural networks for large eddy simulations of wall-bounded turbulence: numerical experiments and challenges

Here, we examine the application of neural network-based methods to improve the accuracy of large eddy simulations of incompressible turbulent flows. The networks are trained to learn a mapping between flow features and the subgrid scales, and applied locally and instantaneously—in the same way as traditional physics-based subgrid closures. Models that use only the local resolved strain rate are poorly correlated with the actual subgrid forces obtained from filtering direct numerical simulation data. We see that highly accurate models in a priori testing are inaccurate in forward calculations, owing to the preponderance of numerical errors in implicitly filtered large eddy simulations. A network that accounts for the discretization errors is trained and found to be unstable in a posteriori testing. We identify a number of challenges that the approach faces, including a distribution shift that affects networks that fail to account for numerical errors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Concentric Spherical GNN for 3D Representation Learning

Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry. We propose a novel multi-resolution convolutional architecture for learning over concentric spherical feature maps, of which the single sphere representation is a special case. Our hierarchical architecture is based on alternatively learning to incorporate both intra-sphere and inter-sphere information. We show the applicability of our method for two different types of 3D inputs, mesh objects, which can be regularly sampled, and point clouds, which are irregularly distributed. We also propose an efficient mapping of point clouds to concentric spherical images, thereby bridging spherical convolutions on grids with general point clouds. We demonstrate the effectiveness of our approach in improving state-of-the-art performance on 3D classification tasks with rotated data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

VERAView User's Manual

VERAView has been developed as an interactive graphical interface for the visualization and engineering analyses of output data from VERA. The Python-based software is easy to install, intuitive to use, and provides instantaneous 2D and 3D images, 1D plots, and alpha-numeric data from VERA multi-physics simulations. This document provides a brief overview of the software and some description of the major features of the application, including examples of each of the encapsulated “widgets” that have been implemented thus far. VERAView is still under major development and large changes in the software and this document are still anticipated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Predicting the High-Temperature Oxidation Response of Nickel Superalloys Using CALPHAD-Enhanced Machine Learning

Structural materials such as Ni-based superalloys used in high-temperature power cycles are routinely exposed to toxic environments including high temperature and pressure, aqueous and gas corrosion, etc. Here, we present a physics-informed machine learning approach to predict the oxidation response of diverse Ni-superalloys. First, a high-fidelity experimental dataset is curated from typical oxidation mass-change experiments in air, covering 25+ elements and different physical behavior such as parabolic growth, non-parabolic growth, and oxide spallation. Second, the dataset is featurized using thermophysical, chemical, and mechanical properties obtained from high-throughput CALPHAD calculations. Third, several machine learning models are developed to identify key features related to mass-change characteristics and model the mass-change curve for various alloys. Finally, the model is deployed to rapidly screen over a new composition space and down-select candidate alloys with high oxidation resistance for experimental validation.

CALPHAD↗
DOE OSTI2021

The Atacama Cosmology Telescope: A Catalog of >4000 Sunyaev–Zel’dovich Galaxy Clusters

Here, we present a catalog of 4195 optically confirmed Sunyaev–Zel’dovich (SZ) selected galaxy clusters detected with signal-to-noise ratio >4 in 13,211 deg 2 of sky surveyed by the Atacama Cosmology Telescope (ACT). Cluster candidates were selected by applying a multifrequency matched filter to 98 and 150 GHz maps constructed from ACT observations obtained from 2008 to 2018 and confirmed using deep, wide-area optical surveys. The clusters span the redshift range 0.04 < z < 1.91 (median z = 0.52). The catalog contains 222 z > 1 clusters, and a total of 868 systems are new discoveries. Assuming an SZ signal versus mass-scaling relation calibrated from X-ray observations, the sample has a 90% completeness mass limit of M 500 c > 3.8 × 10 14 M ⊙ , evaluated at z = 0.5, for clusters detected at signal-to-noise ratio >5 in maps filtered at an angular scale of 2.'4. The survey has a large overlap with deep optical weak-lensing surveys that are being used to calibrate the SZ signal mass-scaling relation, such as the Dark Energy Survey (4566 deg 2 ), the Hyper Suprime-Cam Subaru Strategic Program (469 deg 2 ), and the Kilo Degree Survey (825 deg 2 ). We highlight some noteworthy objects in the sample, including potentially projected systems, clusters with strong lensing features, clusters with active central galaxies or star formation, and systems of multiple clusters that may be physically associated. The cluster catalog will be a useful resource for future cosmological analyses and studying the evolution of the intracluster medium and galaxies in massive clusters over the past 10 Gyr.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Comparison of ozone formation attribution techniques in the northeastern United States

The Integrated Source Apportionment Method (ISAM) has been revised in the Community Multiscale Air Quality (CMAQ) model. This work updates ISAM to maximize its flexibility, particularly for ozone (O 3 ) modeling, by providing multiple attribution options, including products inheriting attribution fully from nitrogen oxide reactants, fully from volatile organic compound (VOC) reactants, equally from all reactants, or dynamically from NO x or VOC reactants based on the indicator gross production ratio of hydrogen peroxide (H 2 O 2 ) to nitric acid (HNO 3 ). The updated ISAM has been incorporated into the most recent publicly accessible versions of CMAQ (v5.3.2 and beyond). This study's primary objective is to document these ISAM updates and demonstrate their impacts on source apportionment results for O 3 and its precursors. Additionally, the ISAM results are compared with the Ozone Source Apportionment Technology (OSAT) in the Comprehensive Air-quality Model with Extensions (CAMx) and the brute-force method (BF). All comparisons are performed for a 4 km horizontal grid resolution application over the northeastern US for a selected 2 d summer case study (9 and 10 August 2018). General similarities among ISAM, OSAT, and BF results add credibility to the new ISAM algorithms. However, some discrepancies in magnitude or relative proportions among tracked sources illustrate the distinct features of each approach, while others may be related to differences in model formulation of chemical and physical processes. Despite these differences, OSAT and ISAM still provide useful apportionment data by identifying the geographical and temporal contributions of O 3 and its precursors. Both OSAT and ISAM attribute the majority of O 3 and NO x contributions to boundary, mobile, and biogenic sources, whereas the top three contributors to VOCs are found to be biogenic, boundary, and area sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Fast quantum interferometry at the nanometer and attosecond scales with energy-entangled photons

In classical optical interferometry, loss and background complicate achieving fast nanometer-resolution measurements with illumination at low light levels. Conversely, quantum two-photon interference is unaffected by loss and background, but nanometer-scale resolution is physically difficult to realize. As a solution, we enhance two-photon interference with highly nondegenerate energy entanglement featuring photon frequencies separated by 177 THz. We observe measurement resolution at the nanometer (attosecond) scale with only O(10 4 ) photon pairs, despite the presence of background and loss. Our nondestructive thickness measurement of a metallic thin film agrees with atomic force microscopy, which often achieves better resolution via destructive means. With contactless, nondestructive measurements in seconds or faster, our instrument enables metrological studies in optically challenging contexts where background, loss, or photosensitivity are factors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

GIF Supercritical Water Cooled Reactor: Proliferation Resistance and Physical Protection White Paper

This report is part of a series of six white papers, prepared jointly by the Proliferation Resistance and Physical Protection Working Group (PRPPWG) and the six System Steering Committees (SSCs) and provisional System Steering Committees (pSSCs). This publication is an update to a similar series published in 2011 presenting the status of Proliferation Resistance & Physical Protection (PR&PP) characteristics for each of the six systems selected by the Generation IV International Forum (GIF) for further research and development, namely: the Sodium-cooled fast Reactor (SFR), the Very high temperature reactor (VHTR), the gas-cooled fast reactor (GFR), the Molten salt reactor (MSR) and the Supercritical water–cooled reactor (SCWR). This white paper represents the status of Proliferation Resistance and Physical Protection (PR&PP) characteristics for the Supercritical Water-cooled Fast reactor (SCFR) reference designs selected by the Generation IV International Forum (GIF) SCWR System Steering Committee (SSC). The intent is to generate preliminary information about the PR&PP features of the SCWR reactor technology and to provide insights for optimizing their PR&PP performance for the benefit of SCWR system designers. It updates the SCWR analysis published in the 2011 report “Proliferation Resistance and Physical Protection of the Six Generation IV Nuclear Energy Systems”, prepared Jointly by the Proliferation Resistance and Physical Protection Working Group (PRPPWG) and the System Steering Committees and provisional System Steering Committees of the Generation IV International Forum, taking into account the evolution of both the systems, the GIF R&D activities, and an increased understanding of the PR&PP features. The white paper, prepared jointly by the GIF PRPPWG and the GIF SCWR SSC, follows the highlevel paradigm of the GIF PR&PP Evaluation Methodology to investigate the PR&PP features of the eight proposed GIF SCWR designs. Two small modular reactors, the Canadian SSR and the Canada/China/Europe ECC-SMART are also mentioned. An overview of the fuel cycles for the GIF designs are provided. For PR, the document analyses and discusses the proliferation resistance aspects in terms of robustness against State-based threats associated with diversion of materials, misuse of facilities, breakout scenarios, and production in clandestine facilities. Similarly, for PP, the document discusses the robustness against theft of material and sabotage by non-State actors. The document follows a common template adopted by all the white papers in the updated series.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Analysis of nonlinear features associated with radio-frequency sheaths

An analytic study is conducted of nonlinear features that arise when radio-frequency (RF) sheath boundary conditions are solved self-consistently with the electromagnetic fields in a magnetized plasma. An instability associated with RF sheath–plasma interactions is identified and shown to be closely linked with sheath–plasma resonance and the occurrence of multiple solutions. It is proposed that this instability serves as a physical mechanism that enables jumping from one solution branch to another, providing a physical basis for the prediction of an RF sheath hysteresis in the time domain. It is further shown that this instability can lead to non-intuitive features such as spontaneous symmetry breaking in an otherwise completely symmetric problem. In plane-stratified geometry, two analytically solvable examples are considered for the cases of an unbounded half-space and a bounded domain

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Three interaction energy scales in the single-layer high-$T_c$ cuprate HgBa 2 CuO 4+δ

The lamellar cuprate superconductors exhibit the highest ambient-pressure superconducting transition temperatures ($T_c$), and after more than three decades of extraordinary research activity, continue to pose formidable scientific challenges. A major experimental obstacle has been to distinguish universal phenomena from materials- or technique-dependent ones. Angle-resolved photoemission spectroscopy (ARPES) measures momentum-dependent single-particle electronic excitations and has been invaluable in the endeavor to determine the anisotropic momentum-space properties of the cuprates. HgBa 2 CuO 4+δ (Hg1201) is a single-CuO 2 -layer cuprate with a particularly high optimal Tc and a simple crystal structure, yet there exists little information from ARPES about the electronic properties of this model system. In this paper we present an ARPES study of doping-, temperature-, and momentum-dependent systematics of near-nodal dispersion anomalies in Hg1201. The data reveal a hierarchy of three distinct energy scales: a subgap low-energy kink, an intermediate-energy kink near 55 meV, and a peak-dip-hump structure. The first two features are attributed to the coupling of electrons to Ba-derived optical phonons and in-plane bond-stretching phonons, respectively. The nodal peak-dip-hump structure appears to have a common doping dependence in several single-layer cuprates and is interpreted as a manifestation of pseudogap physics at the node. These results establish several universal phenomena, both in terms of connecting multiple experimental techniques for a single material and in terms of connecting comparable spectral features in multiple structurally similar cuprates.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A data-driven method to estimate the antiproton background in the Mu2e experiment

The Mu2e experiment at Fermilab will search for the Charged Lepton Flavour Violating (CLFV) process of coherent, neutrinoless µ− → e − conversion in the field of an aluminum nucleus. The expected signal is a monochromatic electron with the energy of 104.97 MeV, slightly below the muon rest mass. Observation of a CLFV process would provide unambiguous evidence for Beyond the Standard Model (BSM) physics. Mu2e is sensitive to a wide range of BSM models and has the capability to distinguish between them, guiding us towards the most accurate models. The key features of the Mu2e experiment are: (1) a high intensity pulsed negative muon beam with about 1010 stopped µ −/s, and (2) a sophisticated superconducting solenoid system with a gradient magnetic field to form and guide the intense muon beam to the target. The Mu2e physics data taking is expected to begin in 2027. For Run I, the expected 5σ discovery sensitivity is Rµe = 1.2 × 10−15, with a total expected background of 0.11 ± 0.03 events. In the absence of a signal, the expected upper limit is Rµe < 6.2 × 10−16 at 90% CL. The success of this experiment hinges on the accurate estimation of the background from various SM processes that could provide signal-like electrons. One of the background processes is antiprotons annihilating in the stopping target to produce signal like electrons through π0 → γγ decays followed by γ conversions, and π− → µ−ν¯ decays followed by µ− decay. It is a relatively small background with large uncertainty (100%) due to the lack of antiproton production cross section information for the Mu2e proton beam energy of 8 GeV. We have developed a novel methodology to estimate the antiproton background in-situ. This forms the main theme of the thesis. We observed that at Mu2e energies, antiproton annihilation in the stopping target is the only source of events with multiple, simultaneous particle trajectories. From Geant4 simulations, only about 0.2% of the simulated antiproton annihilation events have a signal-like electron. Meanwhile, ∼ 5% of events have multiple reconstructible particle tracks per event. Therefore, we have devised a methodology to reconstruct the multi-track events and estimate the antiproton background by exploiting the large ratio of the production rates of the two final states.

Chithirasreemadam, Namitha [Pisa U.] (ORCID:000000↗
DOE OSTI2023

Search for dark matter produced in association with a Higgs boson decaying to tau leptons at $\sqrt{s}$ = 13 TeV with the ATLAS detector

A search for dark matter produced in association with a Higgs boson in final states with two hadronically decaying τ-leptons and missing transverse momentum is presented. The analysis uses 139 fb -1 of proton-proton collision data at $\sqrt{s}$ = 13 TeV collected by the ATLAS experiment at the Large Hadron Collider between 2015 and 2018. No evidence of physics beyond the Standard Model is found. The results are interpreted in terms of a 2HDM+a model featuring two scalar Higgs doublets and a pseudoscalar singlet field. Exclusion limits on the parameters of the model in selected benchmark scenarios are derived at 95% confidence level. Model-independent limits are also set on the visible cross-section for processes beyond the Standard Model producing missing transverse momentum in association with a Higgs boson decaying into τ-leptons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

A Novel Spatio-Temporal Regime Tracking Method for Impact Simulations

In this proposal, we present a novel method of tracking the rheological regimes activated during impact cratering events that will allow researchers to gain new insights into cratering mechanics. Rheology describes the stress-strain response of rocks to different conditions. Planetary impact cratering events often occur on too large of a scale to be feasibly captured in controlled experiments. Instead, these dynamic events are primarily studied using multi-physics codes equipped with complex material models that enable calculations of the impact event at scale. However, determining which physical processes are required for the problem of interest is challenging. Because the dominant rheological regimes change with space and time during crater formation, it is difficult to link numerical simulations with observable features of craters at the end of the event. The basis of this work is the implementation of numerical flags that track the activation of each rheological regime throughout impact simulations. We demonstrate this with the ‘Rock Model’ implemented in the CTH shock-physics code. We will use this rheology tracking method to ’zoom in’ on a specific region within an event and track the conditions the rock experiences over time. This work will develop community benchmarks to validate and distribute our implemented rheological models. We will focus on improving the melt models used by the planetary impact modeling community by developing an EOS-aware rheological transition from solid to melt. Through analysis of cell and tracer-particle based tracking data, we will study the effects of different rheologies on modeled outcomes, particularly on the volume and distributions of impacts melts. We will also use this method to link observable features with the rheological mechanisms responsible for them. The deliverables (peer-reviewed papers) from the proposed work are (i) tests of the implemented rheologic processes and demonstrations of the tracking flags; (ii) the first calculations of the spatio-temporal evolution of the dominant rheologies during impact cratering events; and (iii) application to delivery of impactor iron during basin-scale impacts.

58 GEOSCIENCES↗
DOE OSTI2024

Propyne: Determination of Physical Properties and Unit Cell Parameters under Titan-Relevant Conditions

With its large size, dense atmosphere, methane-based hydrological-like cycle, and diverse surface features, the Saturnian moon Titan is one of the most unique of the outer Solar System satellites. Study of the photochemically produced molecules in Titan’s atmosphere is critical in order to understand the mechanics of the atmosphere and, by extension, the interactions between atmosphere, surface, and subsurface water ocean. One example is propyne vapor, a photochemically produced species in Titan’s upper atmosphere expected to condense in Titan’s stratosphere at lower altitudes. Propyne may also be a trace species in Titan’s stratospheric co-condensed ice clouds detected by the Cassini Composite InfraRed Spectrometer. Bulk structural characterization of propyne ice is currently incomplete and is lacking in published laboratory Raman spectra and X-ray diffraction data. Here, we present a laboratory characterization of propyne ice, including the first published X-ray diffraction and Raman spectroscopy results for propyne ice.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Intermetallic Reactivity Database: Compiling Chemical Pressure and Electronic Metrics toward Materials Design and Discovery

Here, the advent of high-throughput Density Functional Theory (DFT) calculations has supported the creation of large databases containing the quantitative output necessary for constructing theoretical phase diagrams and predicting physical properties. In this Article, we present a complementary resource, the Intermetallic Reactivity Database (IRD), focused on the chemical bonding features of solid-state structures and indicators of potential structural transformations. Each IRD entry augments common features, such as band structures and density of states (DOS) distributions, with chemically motivated information including DFT-Chemical Pressure (CP) schemes and visualizable representations of the atomic charges. Together, these data types enable the rationalization and prediction of potential structural phenomena encountered in intermetallic chemistry, as we illustrate with four examples: the origins of the Y 2 Ni 2 Mg structure in terms of CP features of its parent structures, the anticipation of intergrowth phases from the net atomic CPs collected in Al-containing binary phases, the correlation between trends in the CP schemes of CaCu 5 -type phases and experimentally observed structural variations, and finally, the development of theoretical methodology with the testing of a streamlined method generating DFT-CP schemes. Altogether, these examples highlight how the IRD supports the creation of models of structural chemistry that extend beyond the bounds of its entries.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A robust deep learning workflow to predict multiphase flow behavior during geological C O 2 sequestration injection and Post-Injection periods

Simulation of multiphase flow in porous media is essential to manage the geologic CO 2 sequestration (GCS) process, and physics-based simulation approaches usually take prohibitively high computational cost due to the nonlinearity of the coupled physics. This paper contributes to the development and evaluation of a deep learning workflow that accurately and efficiently predicts the temporal-spatial evolution of pressure and CO 2 plumes during injection and post-injection periods of GCS operations. Based on a Fourier Neural Operator, the deep learning workflow takes input variables or features including rock properties, well operational controls and time steps, and predicts the state variables of pressure and CO 2 saturation. To further improve the predictive fidelity, separate deep learning models are trained for CO 2 injection and post-injection periods due to the difference in primary driving force of fluid flow and transport during these two phases. We also explore different combinations of features to predict the state variables. We use a realistic example of CO 2 injection and storage in a 3D heterogeneous saline aquifer, and apply the deep learning workflow that is trained from physics-based simulation data and emulate the physics process. Through this numerical experiment, we demonstrate that using two separate deep learning models to distinguish post-injection from injection period generates the most accurate prediction of pressure, and a single deep learning model of the whole GCS process including the cumulative injection volume of CO 2 as a deep learning feature, leads to the most accurate prediction of CO 2 saturation. For the post-injection period, it is key to use cumulative CO 2 injection volume to inform the deep learning models about the total carbon storage when predicting either pressure or saturation. The deep learning workflow not only provides high predictive fidelity across temporal and spatial scales, but also offers a speedup of 250 times compared to full physics reservoir simulation, and thus will be a significant predictive tool for engineers to manage the long-term process of GCS.

58 GEOSCIENCES↗
DOE OSTI2021

The First Neptunium Dioxide Neutron Diffraction Experiment at HFIR

Here we present the first neutron diffraction experiment of a 50 mg sample of neptunium(IV) dioxide (NpO 2 ) powder at the High Flux Isotope Reactor (HFIR) using the wide-angle neutron diffractometer (WAND 2 ) HB-2C. We also provide a brief literature survey to pinpoint gaps in the crystal, electronic, and magnetic structure of NpO 2 to determine the optimum experiment to be pursued in the next HFIR user proposal cycle. Characterizing actinide materials with neutron diffraction, inelastic scattering, neutron imaging, polarized neutron studies, and small-angle neutron scattering techniques will increase our understanding of 5f electron behavior and elucidate atomic and magnetic structural properties. This strategy will help fill gaps in the literature and provide theoretical physicists with the data needed to build physical and chemical models to guide and validate computational methods. These methods may be used for predicting structural and chemical features of actinide-based materials. This proof-of-principle effort begins to uncover how to advance actinide science and technology at Oak Ridge National Laboratory (ORNL) using neutron capabilities. This effort has already sparked collaborative discussions between researchers outside the Radioisotope Science and Technology Division, including theoretical physicists from the Nuclear Nonproliferation Division and beamline scientists in the Neutron Scattering Division. We intend to continue pursuing neutron experiments and develop a scientific strategy focused on studying transuranium elements (e.g., Np, Pu, Am, and Cm) using the advanced neutron capabilities at ORNL.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Agentic artificial intelligence for multistage physics experiments at a large-scale user facility particle accelerator

We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.

Accelerator/storage ring control systems↗