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At least 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Griffin: A MOOSE-based reactor physics application for multiphysics simulation of advanced nuclear reactors

Griffin is a Multiphysics Object-Oriented Simulation Environment (MOOSE) based reactor physics application for multiphysics simulations of advanced reactor designs jointly developed by Idaho National Laboratory and Argonne National Laboratory. This paper summarizes the motivation, significance, architecture, design, and features of Griffin. Griffin offers flexible and extensible features to address the challenges associated with advanced reactor designs. These features range from fundamental particle transport to specific reactor physics tasks. The features cover a wide range including on-the-fly and traditional two-step cross-section generation methods, steady-state and transient transport solvers suitable for both heterogeneous and homogeneous models, high-fidelity depletion where thousands of isotopes can be tracked and low-fidelity depletion characterized by burnup, etc. The most fundamental aspect that sets Griffin apart from other reactor analysis codes is that it is developed based on the MOOSE framework. A modular development approach is strongly enforced, with multiphysics being an essential element considered since the beginning of Griffin’s development. Griffin links various MOOSE physics modules and couples to other MOOSE-based applications and non-MOOSE-based applications for multiphyiscs simulations. Griffin includes three modules: ISOXML for preparing and managing multigroup cross sections, radiation transport for solving the neutron transport equation, and reactor analysis for user-oriented reactor physics analysis functionalities. Griffin uses various finite element methods for spatial discretization, multigroup approximation for energy discretization and discrete ordinates method, spherical harmonics expansion method, and diffusion approximation for streaming direction discretization to solve the neutron transport equation. Griffin’s flexibility is evidenced through Griffin’s various applications to fast reactor, high-temperature reactor, pebble bed reactor, molten salt reactor, and microreactor designs. Griffin development follows the software quality assurance procedure for MOOSE-based applications and with software requirements consistent with the ASME NQA-1 standard. Griffin has been adopted into the reactor analysis system for the U.S. NRC and is in use at U.S. companies, universities and national laboratories.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Improving the Quasi‐Biennial Oscillation via a Surrogate‐Accelerated Multi‐Objective Optimization

Accurate simulation of the quasi-biennial oscillation (QBO) is challenging due to uncertainties in representing convectively generated gravity waves. We develop an end-to-end uncertainty quantification workflow that calibrates these gravity wave processes in E3SM for a realistic QBO. Central to our approach is a domain knowledge-informed, compressed representation of high-dimensional spatio-temporal wind fields. By employing a parsimonious statistical model that learns the fundamental frequency from complex observations, we extract interpretable and physically meaningful quantities capturing key attributes. Building on this, we train a probabilistic surrogate model that approximates the fundamental characteristics of the QBO as functions of critical physics parameters governing gravity wave generation. Leveraging the Karhunen–Loève decomposition, our surrogate efficiently represents these characteristics as a set of orthogonal features, capturing cross-correlations among multiple physics quantities evaluated at different pressure levels and enabling rapid surrogate-based inference at a fraction of the computational cost of full-scale simulations. Finally, we analyze the inverse problem using a multi-objective approach. Our study reveals a tension between amplitude and period that constrains the QBO representation, precluding a single optimal solution. To navigate this, we quantify the bi-criteria trade-off and generate a set of Pareto optimal parameter values that balance the conflicting objectives. This integrated workflow improves the fidelity of QBO simulations and offers a versatile template for uncertainty quantification in complex geophysical models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Soft X-ray measurements of transport and MHD activity in the core and edge NSTX plasma (Final Technical Report)

A comprehensive suite of innovative diagnostic instrumentation and techniques has been developed by the Johns Hopkins collaboration for measurements of transport and MHD activity in the core and edge of the National Spherical Torus Experiment (NSTX) at the Princeton Plasma Physics Laboratory (PPPL). These include ultrafast multi-energy poloidal soft X-ray arrays, high spatial resolution multi-energy tangential soft X-ray arrays, high spectral resolution transmission grating imaging VUV spectrometers and ultrafast bolometric arrays, which measure over a broad range of spatial, spectral, and temporal scales the emission from ohmic, RF, and beam driven NSTX plasmas. The instrumentation was designed to fulfill the specific measurement needs of the Spherical Torus (ST) magnetic fusion concept and is accompanied by a comprehensive set of numerical tools for data analysis and modeling, aimed at determining as many as possible plasma parameters and at extracting as much as possible physics information from NSTX experiments. The Johns Hopkins diagnostics have been and continue to be instrumental for essentially all the NSTX experiments. In addition, our own research and experiments using these diagnostics has led over the years to fundamental conclusions for the ST fusion physics, such as the establishment of near neoclassical impurity transport in the NSTX core and edge plasma, and the critical role of the Global Alfven Eigenmode (GAE) MHD activity for the ST core electron thermal transport. The Johns Hopkins Diagnostics continue to be operational on the upgraded NSTX-U and will provide key plasma information in its upcoming high field, current and power experimental campaigns. The present report summarizes the main technical characteristics and measurement capabilities of the diagnostic instrumentation and methods developed within our research and describes a key physics result achieved during this research. Success in our research was made possible also by the excellent support and cooperation of the NSTX research team and of PPPL researchers, who are warmly acknowledged here.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Developing Capabilities in Physical and Computational Sciences

The Physical and Computational Sciences Directorate (PCSD) performs fundamental research in support of the science missions of Offices of Basic Energy Sciences (BES), Advanced Scientific Computing Research (ASCR), High Energy Physics (HEP), Nuclear Physics (NP), and Fusion Energy Sciences (FES), and others within the domains of the chemical, materials, computational sciences, mathematics, and physics. This LDRD project aims to provide funding to develop/demonstrate research capabilities for proposals and publications to support these science missions. Staff will propose small research tasks/projects to be performed under this overall project.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Accelerator and Beam Physics: Grand Challenges and Research Opportunities

Accelerator and beam physics (ABP) is the science of the motion, generation, acceleration, manipulation, prediction, observation and use of charged particle beams. It focuses on fundamental long-term accelerator and beam physics research and development. Accelerator and beam physics research has resulted in important advances in accelerator science, yet support for this research is declining. NSF has terminated its program in accelerator Science and funding by DOE through GARD and Accelerator Stewardship has been steady or declining. The declining support for accelerator research will slow advances and threaten student training and work-force development in accelerator science. We propose a robust and scientifically challenging program in accelerator and beam physics, which will position the field of US High Energy Physics to be productive and competitive for decades to come.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Machine Learning and Data Science to Advance Laboratory Earthquake Prediction and Illuminate the Mechanics of Precursors to Failure

Earthquakes represent one of our greatest natural hazards and in recent years human induced seismicity is adding to the threat. Even a modest improvement in the ability to forecast devastating large earthquakes or smaller shallow events associated with fluid injection could save thousands of lives and billions of dollars. Current efforts to forecast earthquakes are limited by knowledge of earthquake physics and hampered by a lack of reliable lab or field observations. However, recent work has provided a critical opportunity for advancement. We have found: 1) clear and consistent precursors prior to earthquake-like failure in the laboratory and 2) that lab earthquakes can be predicted using machine learning (ML). These works show that stick-slip failure events –the lab equivalent of earthquakes– are preceded by a cascade of micro-failure events that radiate elastic energy in a manner that foretells catastrophic failure. Remarkably, ML predicts the fault zone stress state, the failure time and in some cases the magnitude of lab earthquakes. In addition, the observations include clear precursors to failure in the form of changes in fault zone properties prior to lab earthquakes. Precursors have been observed in previous laboratory studies but their origin is poorly understood and their possible connection to ML based earthquake prediction is unknown. The work conducted under our project has dramatically expanded these efforts. We have developed an integrated data science approach to illuminate the physics of earthquake precursors and lab earthquake prediction. Our work has accelerated the development of ML, artificial intelligence (AI), and related data science approaches by providing massive data sets that are tightly connected to critical scientific problems and by bringing together leading subject matter experts and data scientists. Earthquake physics involves phenomena that are far from equilibrium. Our work has leveraged data science methods to illuminate these phenomena and investigate how they relate to earthquake prediction. In addition to a large database with many types of labeled events that is available to everyone, our work has advanced the fundamental understanding of seismic forecasting, earthquake physics, and fault rheology

58 GEOSCIENCES↗
DOE OSTI2023

Search for Rare Processes: Mu2e and KOTO

The Standard Model (SM) of particle physics has been very successful in describing the fundamental forces, the properties of elementary particles, and predicting particles prior to their discovery, for example the top quark and the Higgs boson. Yet the SM does not explain all phenomena, and discoveries of physics processes beyond the SM will allow progress in our understanding of the fundamental nature of matter and forces. The search for rare processes is one of the paths to look for new physics. Two of these searches are for muon to electron conversion and the decay $\text{K}_\text{L}\rightarrow \pi^0 \nu \bar{\nu}$. Our main effort was in the Mu2e experiment under construction at Fermilab, which has the goal of searching for the neutrinoless conversion of a muon into an electron at a single event sensitivity of 2.87 x 10-17 and, in the process, provide evidence of physics processes beyond the Standard Model. We developed the firmware to be used in the Mu2e tracker readout, assisted in the construction and operation of the Mu2e Vertical Slice Test, and provided input to studies of cosmic event reconstructions in Mu2e using simulated data. Under this reward we also concluded our effort in two KOTO experiment in J-PARC, Japan and set a limit on the rate of the rare decay, $\text{K}_\text{L}\rightarrow \pi^0 \nu \bar{\nu}$ using the data collected in the 2016-2018 period. We also maintained and operated the DAQ system used for data collection thru 2021.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Photovoltaic Solar Engineering and System Overview

This report depicts local photovoltaic components, interdependencies with the grid, and local two-way communication pathways. It identifies physical and virtual risks and delineates between photovoltaic and grid scale components. It provides readers with a fundamental overview of the most relevant and critical physical components and serves as a basic educational resource for readers.

14 SOLAR ENERGY↗
DOE OSTI2020

Long-baseline Sterile Neutrino Searches in the NOvA Experiment

Experimental high energy physics constitutes the experimental investigation of the fundamental building blocks of matter and radiation and their interactions. These fundamental building blocks are classified as bosons and fermions. Neutrinos are fermions that come under the lepton category. According to the Standard Model of particle physics, they are massless, neutral particles that interacts with matter only through weak force and comes in three different flavors. Neutrinos exhibit a characteristic property, known as the neutrino oscillation. Neutrino oscillation is the the change of neutrino from one flavor to another flavor as they propagates in space. This phenomenon is only possible if the neutrinos are massive. The reported anomalous result from LSND and MiniBooNE experiments demanded the existence of a fourth kind of neutrino, at the same time, LEP experimental result confirms that only three active neutrino can exist. So if they exists, this fourth kind of neutrino can’t interact through weak force or they can be called as sterile neutrino. Some of the experiments favored the existence of sterile neutrinos, at the same time, there are some other experiments that disfavors their existence. So the search for the sterile neutrinos is very active in the field of experimental neutrino physics. NOvA is a long-baseline experiment with two functionally identical detectors, a Near Detector (ND) at Fermilab and a Far Detector at Ash River, Minnesota which is 810 km far away from the ND. They are offaxis detectors that lie 14.6 mrad off from the beam axis. This enables the detector to receive a neutrino beam with energy peak at 2 GeV. The NOvA experiment is designed to run with both the neutrino beam and antineutrino beam and NOvA has the potential to search for the existence of sterile neutrinos. This thesis is based on the sterile neutrino search by looking at the depletion of the neutral-current (NC) energy spectrum of neutrinos at the FD. Since NC events are not sensitive to three flavor oscillation, any depletion of NC events at the FD indicates the existence of sterile neutrinos.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Addressing GPU memory limitations for Graph Neural Networks in High-Energy Physics applications

Introduction Reconstructing low-level particle tracks in neutrino physics can address some of the most fundamental questions about the universe. However, processing petabytes of raw data using deep learning techniques poses a challenging problem in the field of High Energy Physics (HEP). In the Exa.TrkX Project, an illustrative HEP application, preprocessed simulation data is fed into a state-of-art Graph Neural Network (GNN) model, accelerated by GPUs. However, limited GPU memory often leads to Out-of-Memory (OOM) exceptions during training, due to the large size of models and datasets. This problem is exacerbated when deploying models on High-Performance Computing (HPC) systems designed for large-scale applications. Methods We observe a high workload imbalance issue during GNN model training caused by the irregular sizes of input graph samples in HEP datasets, contributing to OOM exceptions. We aim to scale GNNs on HPC systems, by prioritizing workload balance in graph inputs while maintaining model accuracy. Our paper introduces diverse balancing strategies aimed at decreasing the maximum GPU memory footprint and avoiding the OOM exception, across various datasets. Results Our experiments showcase memory reduction of up to 32.14% compared to the baseline. We also demonstrate the proposed strategies can avoid OOM in application. Additionally, we create a distributed multi-GPU implementation using these samplers to demonstrate the scalability of these techniques on the HEP dataset. Discussion By assessing the performance of these strategies as data loading samplers across multiple datasets, we can gauge their effectiveness in both single-GPU and distributed environments. Our experiments, conducted on datasets of varying sizes and across multiple GPUs, broaden the applicability of our work to various GNN applications that handle input datasets with irregular graph sizes.

Lee, Claire Songhyun↗
DOE OSTI2022

Predictive models enhance feedstock quality of corn stover via air classification

Feedstock heterogeneity is a fundamental obstacle to cost-competitive biobased products. Agricultural products like corn stover have anatomical components that vary in their chemical composition, mechanical properties, structure, and response to chemical and biological treatments. A technique that can enrich streams in select anatomical fractions would allow a tailored deconstruction approach to increase overall process efficiency. Air classification can be leveraged for such refining, however, fundamental characterization and understanding of the particle properties that underly the physics of air classification are only modestly documented. Here, we determine fundamental particle properties including mass-to-area ratio, drag coefficient, and partition velocity that describe how anatomical tissues of corn stover behave during air classification. In this work, mass-to-area ratios of anatomical tissues vary by nearly two orders of magnitude from 2.3 mg/mm 2 for cob to 0.04 mg/mm 2 for leaf. Drag coefficients of longer, fibrous materials (i.e., rind, husk, and sheath) are shown to correlate with particle area (p-value < 0.001) whereas granular tissues (i.e., cob, pith, and leaf) correlate better with mass-to-area ratio (p-values < 0.001). When compared to experimental observations, a simulated two-stage air classification and size reduction scenario predicts the overall partitioning of anatomical tissues within 15% for pith, husk, rind, and cob tissues. The model predicts an air-classified fraction preferentially enriched in cob (purity = 20%), rind (purity = 74%), and pith (purity = 4.5%) with a mass yield of 47%. Empirical relations for these properties can be used to predict the partitioning of corn stover during air classification based on anatomical type and size.

09 BIOMASS FUELS↗
DOE OSTI2025

First Direct-Detection Results on Sub-GeV Dark Matter Using the SENSEI Detector at SNOLAB

Here, we present the first results from a dark matter search using six Skipper-CCDs in the SENSEI detector operating at SNOLAB. We employ a bias-mitigation technique of hiding approximately 46% of our total data and aggressively mask images to remove backgrounds. Given a total exposure after masking of 100.72 gram-days from well-performing sensors, we observe 55 two-electron events, 4 three-electron events, and no events containing 4–10 electrons. The two-electron events are consistent with pileup from one-electron events. Among the 4 three-electron events, 2 appear in pixels that are likely impacted by detector defects, although not strongly enough to trigger our “hot-pixel” mask. We use these data to set world-leading constraints on sub-GeV dark matter interacting with electrons and nuclei.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Low energy backgrounds and excess noise in a two-channel low-threshold calorimeter

Here, we describe observations of low energy excess (LEE) events, background events observed in all light dark matter direct detection calorimeters, and noise in a transition edge sensor based two-channel silicon athermal phonon detector with 375 meV baseline energy resolution. We measure two distinct LEE populations: “shared” multichannel events with a pulse shape consistent with substrate athermal phonon events and sub-eV events that couple nearly exclusively to a single channel with a significantly faster pulse shape. These “singles” are consistent with events occurring within the aluminum athermal phonon collection fins. Similarly, our measured detector noise is higher than the theoretical expectation. Measured noise can be split into an uncorrelated component, consistent with shot noise from small energy depositions within the athermal phonon sensor itself, and a correlated component, consistent with shot noise from energy depositions within the silicon substrate's phonon system.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Snowmass 2021 White Paper: Electron Ion Collider for High Energy Physics

Electron Ion Collider (EIC) is a particle accelerator facility planned for construction at Brookhaven National Laboratory on Long Island, New York by the United States Department of Energy. EIC will provide capabilities of colliding beams of polarized electrons with polarized beams of proton and light ions. EIC will be one of the largest and most sophisticated new accelerator facilities worldwide, and the only new large-scale accelerator facility planned for construction in the United States in the next few decades. The versatility, resolving power and intensity of EIC will present many new opportunities to address some of the crucial and fundamental open scientific questions in particle physics. This document provides an overview of the science case of EIC from the perspective of the high energy physics community.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

A muon–ion collider at BNL: The future QCD frontier and path to a new energy frontier of μ + μ – colliders

In this work, we propose the development and construction of a novel muon–ion collider (MuIC) at Brookhaven National Laboratory (BNL) in the USA as an upgrade to succeed the electron–ion collider (EIC) that is scheduled to commence in the early 2030s, by a joint effort of the nuclear and particle physics communities. The BNL facility could accommodate a muon storage beam with an energy up to about 1 TeV with existing magnet technology. When collided with a 275 GeV hadron beam, the MuIC center-of-mass energy of about 1 TeV will extend the kinematic coverage of deep inelastic scattering physics at the EIC (with polarized beams) by more than an order of magnitude in Q 2 and x, opening a new QCD frontier to address many fundamental scientific questions in nuclear and particle physics. This coverage is comparable to that of the proposed Large Hadron–Electron Collider (LHeC) at CERN, but with complementary lepton and hadron kinematics, ion species, and beam polarization. Additionally, the development of a MuIC at BNL will focus the worldwide R&D efforts on muon collider technology and serve as a demonstrator toward a future muon–antimuon collider at O(10) TeV energy, which is an attractive option to reach the next high energy frontier in particle physics at an affordable cost and a smaller footprint than a future circular hadron collider. We discuss here the possible design parameters of the MuIC, kinematic coverage, science cases, and detector design considerations including resolution estimates on DIS kinematic variables. A possible road map toward the future MuIC and muon–antimuon colliders is also presented.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Superconducting detectors for rare event searches in experimental astroparticle physics

Superconducting detectors have become an important tool in experimental astroparticle physics, which seeks to provide a fundamental understanding of the Universe. In particular, such detectors have demonstrated excellent potential in two challenging research areas involving rare event search experiments, namely, the direct detection of dark matter (DM) and the search for neutrinoless double beta decay. Here, we review the superconducting detectors that have been and are planned to be used in these two categories of experiments. We first provide brief histories of the two research areas and outline their significance and challenges in astroparticle physics. Then, we present an extensive overview of various types of superconducting detectors with a focus on sensor technologies and detector physics, which are based on calorimetric measurements and heat flow in the detector components. Finally, we introduce leading experiments and discuss their future prospects for the detection of DM and the search for neutrinoless double beta decay employing superconducting detectors.

transition edge sensors↗
DOE OSTI2022

Test of lepton universality in beauty-quark decays

The standard model of particle physics currently provides our best description of fundamental particles and their interactions. The theory predicts that the different charged leptons, the electron, muon and tau, have identical electroweak interaction strengths. Previous measurements have shown that a wide range of particle decays are consistent with this principle of lepton universality. This article presents evidence for the breaking of lepton universality in beauty-quark decays, with a significance of 3.1 standard deviations, based on proton–proton collision data collected with the LHCb detector at CERN’s Large Hadron Collider. The measurements are of processes in which a beauty meson transforms into a strange meson with the emission of either an electron and a positron, or a muon and an antimuon. If confirmed by future measurements, this violation of lepton universality would imply physics beyond the standard model, such as a new fundamental interaction between quarks and leptons.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Unnuclear physics: Conformal symmetry in nuclear reactions

Significance Symmetry plays a key role in modern physics as a guiding principle for fundamental theories of nature. We use the nonrelativistic conformal symmetry to predict universal energy spectra of final states in reactions of quantum particles at vastly different length scales. Our work extends Georgi’s “unparticle” proposal to nonrelativistic spin-1/2 fermions in the unitary regime of strong interactions. These systems form so-called unparticles, which behave characteristically different from normal particles. They can be engineered with ultracold atoms and occur naturally in nuclear reactions with multiple neutrons—a situation we dub “unnuclear physics.” We present the general scenario of unnuclear physics, apply it to identify unnuclei in nuclear reactions, and highlight the opportunity to measure unnuclei at radioactive beam facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗