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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Concept of circular-linear energy recovery accelerator to probe the energy frontier

Energy-frontier accelerators provide powerful tools performing high precision measurements confirming the fundamentals of the physics and broadening new research horizons. Such machines are either driven by circular or linear accelerators. The circular machines, having the centre-of-mass (CM) energy values reaching 200 GeV (for leptons) and above, experience beam energy loss and quality dilution, for example, due to synchrotron radiation, limiting the overall CM energy achievable and requiring a constant energy top-up to compensate the loss and the beam quality dilution. Linear colliders overcome these limitations, while the finite capabilities of generating high average current beams limits the luminosity. This is partially compensated by the quality of the colliding beams. In this work, we suggest a novel design of circular-linear accelerator based on the merging of the “non-emitting”, low-energy storage rings and energy recovery linear accelerators. We suggest using the recently considered dual-axis asymmetric cavities to enable the operation of such a system, and in particular the energy recovery from spent, high-intensity beams. The machine considered, under the scope of the SNOWMASS-2021 initiative, can be potentially used to reach ultimate energy frontiers in high-energy physics as well as to drive next generation light sources. The merging of circular and linear systems, and applications of dual axes cavities, should allow the maintaining of high beam quality, high luminosity, and high energy efficiency, while offering a flexible energy management and opening clear opportunity for reducing the running cost. We note that the numbers shown in the paper are for illustration purpose and can be improved further.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Parametrizations of coupled betatron motion for strongly coupled lattices

The coupling of transverse motion is a natural occurrence in particle accelerators, either in the form of a residual coupling arising from imperfections or originating by design from strong systematic coupling fields. While the first can be treated perturbatively, the latter requires a robust approach adapted to strongly coupled optics, and a parametrization of the linear optics must be performed to explore beam dynamics in such peculiar lattices. This work highlights the key physical interpretations of the main parametrization formalisms to describe linear coupled optics, along with explicit links and comparisons of these parametrizations. Concepts rarely illustrated in other works, such as forced mode flips and local coupling, are explored in detail, clarifying some anomalies that can arise in lattice functions. The analytical methods have been implemented in a reference Python package and connected with ray-tracing and integration codes to explore examples of strongly coupled lattices, which are discussed in detail to highlight the key physical interpretations of the parametrizations and characteristics of the lattices.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Background studies and normalization of signal events in the Mu2e experiment

The Mu2e experiment is currently being constructed at Fermilab to search for the neutrino-less conversion of negative muons into electrons in the field of an aluminum nucleus. The experiment aims at a sensitivity of four orders of magnitude higher than previous related experiments, which implies highly demanding accuracy requirements both in the design and during the operation. To achieve such a goal, two important tasks should be accomplished. First, it is essential to estimate precisely the particle yields and all the backgrounds that could mimic the monoenergetic conversion electron signal. Second, it is necessary to normalize the signal events accurately. The normalization of the signal events is planned to be done using a detector system made of an HPGe detector and a Lanthanum Bromide detector, which will measure the rate of muons stopped on the aluminum target by looking at the emitted characteristic X-and γ-rays of energies up to 1809 keV. Therefore, it is essential to evaluate the detector system's performance before the start of the actual experiment. In this study, the first task was addressed by an extensive campaign of Monte Carlo simulations to investigate the relevant parameters and their impact on the experiment's sensitivity. The second task was handled by taking advantage of the Helmholtz-Zentrum Dresden-Rossendorf (HZDR) pulsed Bremsstrahlung photon beam at the ELBE facility. The detector system was tested at the ELBE facility under timing and background conditions similar to the ones expected at the Mu2e experiment. The study presents and discusses the simulation results and the detector system testing campaign.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Editorial announcements and information — ICFA Beam Dynamics Newsletter No. 85

This is a general information article of the ICFA Beam Dynamics Newsletter No. 85. It contains foreword from the Editor-in-Chief and the issue editor, a workshop and conference report section, a recent Ph.D. thesis section, a forthcoming beam dynamics events section, and a section of announcements from the beam dynamics panel.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Beam-induced backgrounds measured in the ATLAS detector during local gas injection into the LHC beam vacuum

Inelastic beam-gas collisions at the Large Hadron Collider (LHC), within a few hundred metres of the ATLAS experiment, are known to give the dominant contribution to beam backgrounds. These are monitored by ATLAS with a dedicated Beam Conditions Monitor (BCM) and with the rate of fake jets in the calorimeters. These two methods are complementary since the BCM probes backgrounds just around the beam pipe while fake jets are observed at radii of up to several metres. In order to quantify the correlation between the residual gas density in the LHC beam vacuum and the experimental backgrounds recorded by ATLAS, several dedicated tests were performed during LHC Run 2. Local pressure bumps, with a gas density several orders of magnitude higher than during normal operation, were introduced at different locations. The changes of beam-related backgrounds, seen in ATLAS, are correlated with the local pressure variation. In addition the rates of beam-gas events are estimated from the pressure measurements and pressure bump profiles obtained from calculations. Using these rates, the efficiency of the ATLAS beam background monitors to detect beam-gas events is derived as a function of distance from the interaction point. These efficiencies and characteristic distributions of fake jets from the beam backgrounds are found to be in good agreement with results of beam-gas simulations performed with theFluka Monte Carlo programme.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

Here, we develop a computational database, website applications (web-apps), and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D) heterostructures. Using density functional theory (DFT) based lattice parameters and electronic band energies for 674 nonmetallic exfoliable 2D materials, we generate 226 779 possible bilayer heterostructures. We classify these heterostructures into type-I, -II, and -III systems according to Anderson’s rule, which is based on the relative band alignments of the noninteracting monolayers. We find that type II is the most common and type III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the Periodic Table of constituent elements. The band alignment data can also be used for identifying photocatalysts and high-work-function 2D metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS 2 /WSe 2 , MoS 2 /h-BN, and MoSe 2 /CrI 3 ), to compare the band-alignment description with the predictions from Anderson’s rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools, and associated data will be distributed through the JARVIS-HETEROSTRUCTURE website. Our analysis, results, and the developed web-apps can be applied to the screening and design applications, such as finding alternative photocatalysts, photodetectors, and high-work-function (WF) 2D-metal contacts.

2-dimensional systems↗
DOE OSTI2025

Photon-Counting Interferometry to Detect Geontropic Space-Time Fluctuations with GQuEST

The gravity from the quantum entanglement of space-time (GQuEST) experiment uses tabletop-scale Michelson laser interferometers to probe for fluctuations in space-time. We present a practicable interferometer design featuring a novel photon-counting readout method that provides unprecedented sensitivity, as it is not subject to the interferometric standard quantum limit. We evaluate the potential of this design to measure space-time fluctuations motivated by recent “geontropic” quantum gravity models. The accelerated accrual of Fisher information offered by the photon-counting readout enables GQuEST to detect the predicted quantum gravity phenomena within measurement times at least 100 times shorter than equivalent conventional interferometers. The GQuEST design, thus, enables a fast and sensitive search for signatures of quantum gravity in a laboratory-scale experiment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

BraggNN : fast X-ray Bragg peak analysis using deep learning

X-ray diffraction based microscopy techniques such as high-energy diffraction microscopy (HEDM) rely on knowledge of the position of diffraction peaks with high precision. These positions are typically computed by fitting the observed intensities in detector data to a theoretical peak shape such as pseudo-Voigt. As experiments become more complex and detector technologies evolve, the computational cost of such peak-shape fitting becomes the biggest hurdle to the rapid analysis required for real-time feedback in experiments. To this end, we propose BraggNN, a deep-learning based method that can determine peak positions much more rapidly than conventional pseudo-Voigt peak fitting. When applied to a test dataset, peak center-of-mass positions obtained from BraggNN deviate less than 0.29 and 0.57 pixels for 75 and 95% of the peaks, respectively, from positions obtained using conventional pseudo-Voigt fitting (Euclidean distance). When applied to a real experimental dataset and using grain positions from near-field HEDM reconstruction as ground-truth, grain positions using BraggNN result in 15% smaller errors compared with those calculated using pseudo-Voigt. Recent advances in deep-learning method implementations and special-purpose model inference accelerators allow BraggNN to deliver enormous performance improvements relative to the conventional method, running, for example, more than 200 times faster on a consumer-class GPU card with out-of-the-box software.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Analysis of Validating and Verifying OpenACC Compilers 3.0 and Above

OpenACC is a high-level directive-based parallel programming model that can manage the sophistication of heterogeneity in architectures and abstract it from the users. The portability of the model across CPUs and accelerators has gained the model a wide variety of users. This means it is also crucial to analyze the reliability of the compilers’ implementations. To address this challenge, the OpenACC Validation and Verification team has proposed a validation testsuite to verify the OpenACC implementations across various compilers with an infrastructure for a more streamlined execution. This paper will cover the following aspects: (a) the new developments since the last publication on the testsuite, (b) outline the use of the infrastructure, (c) discuss tests that highlight our workflow process, (d) analyze the results from executing the testsuite on various systems, and (e) outline future developments.

Jarmusch, Aaron↗
DOE OSTI2020

Thermal Image Processing for Feature Extraction from Encapsulated Phase Change Materials

Encapsulated inorganic particles with high melting points (>300 °C) are desired as high-temperature Phase Change Materials (PCMs) for next-generation Latent Heat Thermal Energy Storage (LHTES) systems. One of the many challenges during the development of PCMs is to achieve a high throughput that in turn depends on accurately modeling the relation between process parameters and geometric & thermal properties of the PCMs particle. During the production of the PCMs, a high-speed infrared camera is used to acquire images of the encapsulated material under controlled illumination conditions. This research article focuses on the development of image processing techniques for both geometric and thermal feature extraction during the development of the PCMs. A user-friendly GUI has been designed in MATLAB and preliminary experimental results have demonstrated that the method is fast, accurate and reliable for a high throughput production. The extracted features will be used to develop Machine Learning (ML) models to predict the geometric and thermal properties of the PCM based on the process parameter settings. The ML model will accelerate the search for the optimized process settings to boost the throughput of the production.

25 ENERGY STORAGE↗
DOE OSTI2024

Axially symmetric e-lens based on McMillan map

In this work, we investigate the transverse dynamics of a single particle in a model integrable accelerator lattice, based on a McMillan axially symmetric electron lens. Although the McMillan e-lens has been considered as a device potentially capable of mitigating collective space charge forces, some of its fundamental properties have not been described yet. The main goal of our work is to close this gap and understand the limitations and potential of this device. We classify possible regimes with stable trajectories and provide set of canonical action-angle variables, along with an evaluation of the dynamical aperture, Poincar\'e rotation numbers as functions of amplitudes, and spread in nonlinear tunes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

FY2020 Energy Efficient Mobility Systems Annual Progress Report

EEMS Program activities during FY 2020 focused on analytical research and large-scale modeling and simulation to understand the impacts that new mobility technologies and services will have at the vehicle-, traveler-, and overall transportation system-level. This research included the development of a multi-fidelity, end-to-end transportation system models and tools to evaluate the complex interactions among the various actors within the mobility landscape, analysis of empirical data to characterize which solutions may provide the largest benefits, and development of new control systems and algorithms that use vehicle connectivity and automation to improve the performance and efficiency of individual vehicles as well as the overall traffic system. This document presents a brief overview of the EEMS Program and documents progress and results from projects within each of the EEMS activity areas. The Computational Modeling and Simulation key activity area summarizes work within the sub-areas of (1) the SMART (Systems and Modeling for Accelerated Research in Transportation) Mobility Lab Consortium, (2) Artificial Intelligence, High-Performance Computing, and Data Analytics, and (3) Core Simulation and Evaluation Tools. Additionally, the program’s advanced R&D projects are summarized within (4) the Connectivity and Automation Technology key activity area. Each of the individual progress reports provide a project overview and highlights of the technical results.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Energy conserving quadrature based dimensionality reduction for nonlinear hydrodynamics problems

Projection based dimensionality reduction is used to approximate the full discretiza tion of a PDE problem in order to reduce the cost of its numerical simulation while staying faithful to the full dynamics. For nonlinear problems, hyperreduction methods are additionally needed to remove the dependence of the nonlinear terms on the full problem’s size. In this project, we develop an energy conserving hyperreduction method and apply it to Eulerian-Lagrangian hydrodynamics problems, demonstrating that the reduced model successfully accelerates the numerical simulations while conserving the problem’s total energy with high accuracy.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Axially Symmetric E-Lens Based on McMillan Map

In this work, we investigate the transverse dynamics of a single particle in a model integrable accelerator lattice, based on a McMillan axially symmetric electron lens. Although the McMillan e-lens has been considered as a device potentially capable of mitigating collective space charge forces, some of its fundamental properties have not been described yet. The main goal of our work is to close this gap and understand the limitations and potential of this device. We classify possible regimes with stable trajectories and provide set of canonical action-angle variables, along with an evaluation of the dynamical aperture, Poincar\'e rotation numbers as functions of amplitudes, and spread in nonlinear tunes.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition

Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits—amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.

54 ENVIRONMENTAL SCIENCES↗