Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “physics”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

The Materials Plasma Exposure eXperiment: Status of the Physics Basis Together With the Conceptual Design and Plans Forward

The Materials Plasma Exposure eXperiment (MPEX) is a linear plasma device that will address the plasma–material interaction (PMI) science for future fusion reactors and will enable testing of plasma-facing components (PFCs). It is designed as a steady-state device eventually delivering an ion fluence of up to 10 31 m -2 to the target. The device will be designed to handle neutron-activated materials. These capabilities should push the technical readiness level of PFCs up to six for some end-of-life aspects. In order to achieve the relevant plasma conditions, as they are expected in future fusion reactor divertors, MPEX will utilize a novel plasma source concept. This plasma source concept uses a high-power helicon (200 kW, 13.56 MHz), an electron cyclotron heating (ECH) system, which will heat electrons via electron Bernstein wave (EBW) heating (about 400 kW, 70 GHz), and an ion cyclotron heating (ICH) system (400 kW, 6–9 MHz). The physics basis for this plasma source concept including the heating physics and transport is based on experiments on Proto-MPEX. An overview of the experimental results and the physics basis will be given. The physics basis directly translates into functional requirements of MPEX and the conceptual design. The status of the conceptual design of MPEX will be shown.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Exascale models of stellar explosions: Quintessential multi-physics simulation

The ExaStar project aims to deliver an efficient, versatile, and portable software ecosystem for multi-physics astrophysics simulations run on exascale machines. The code suite is a component-based multi-physics toolkit, built on the capabilities of current simulation codes (in particular Flash-X and Castro), and based on the massively parallel adaptive mesh refinement framework AMReX. It includes modules for hydrodynamics, advanced radiation transport, thermonuclear kinetics, and nuclear microphysics. The code will reach exascale efficiency by building upon current multi- and many-core packages integrated into an orchestration system that uses a combination of configuration tools, code translators, and a domain-specific asynchronous runtime to manage performance across a range of platform architectures. The target science includes multi-physics simulations of astrophysical explosions (such as supernovae and neutron star mergers) to understand the cosmic origin of the elements and the fundamental physics of matter and neutrinos under extreme conditions.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Basic Research Needs for High Energy Physics Detector Research & Development: Report of the Office of Science Workshop on Basic Research Needs for HEP Detector Research and Development: December 11-14, 2019

Transformative discovery in science is driven by innovation in technology. Our boldest undertakings in particle physics have at their foundation precision instrumentation. To reveal the profound connections underlying everything we see from the smallest scales to the largest distances in the Universe, to understand its fundamental constituents, and to reveal what is still unknown, we must invent, develop, and deploy advanced instrumentation. Investments in High Energy Physics (HEP) enabled by instrumentation have been richly rewarded with discoveries of the tiny masses of the neutrinos, the origin of mass itself: the enigmatic Higgs boson, and the surprising accelerating expansion of the Universe. What we have learned is remarkable, unexpected, exciting and mysterious; raising many new questions waiting to be answered. The quest to answer them drives innovation that improves the nation's health, wealth, and security, inspiring the public and drawing young people to science. Excellence and innovation come most effectively from diverse teams of people. Success, therefore, depends critically on attracting, engaging, and supporting a diverse cadre of young people to the field, and ensuring an inclusive environment at all levels. The program laid out in the 2014 Particle Physics Projects Prioritization Panel (P5) report "Building for Discovery - A Strategic Plan for U.S. Particle Physics in a Global Context" guides current and near future experiments to exploit these and other discoveries, and the instrumentation innovation they require, to push the frontiers of science into new territory. To explore this territory HEP will soon embark on planning the next generation of experiments. Realizing these experiments will require giant leaps in capabilities beyond the instrumentation of today. Accordingly, now is a pivotal moment to invest in the accelerated development of cost-effective instrumentation with greatly improved sensitivity and performance that will make measurable the unmeasurable, enabling a tool-driven revolution to open the door to future discoveries. Historic scientific opportunities await us, enabled by executing the instrumentation research plan outlined here.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Final Reports of the 2019 Los Alamos National Laboratory Computational Physics Student Summer Workshop

The Los Alamos National Laboratory Computational Physics Workshop is intended to educate select students in problems of computational physics, while exciting them about problems of particular interest to LANL. The long term goal is to train a cadre of future researchers with strong connections to LANL and both interest and expertise in the problems LANL faces. This year’s workshop ran from June 10 – August 16, 2019 and once again attracted a phenomenal group of students, whose work is presented in the following pages. The students worked with LANL staff mentors in teams of two, doing original research. In addition, they attended a lecture series focusing on both the basics and the cutting edge challenges of computational physics (Table 1). A new item this year was a modification to our Meet LANL series of brown-bag lunch session, moving from focusing on individual researchers to panel presentations from the different groups within XCP. As always, the mentors were drawn from across disciplines and divisions. Projects looked physics ranging from quantum interactions, up to solar system development.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

"Experimental Research at the Energy Frontier in High Energy Physics" (Final Technical Report)

Recent developments in particle physics and cosmology raise the exciting prospect that we are on the threshold of a major step forward in our understanding of the universe. This proposal describes a research program at the University of Illinois in experimental high-energy physics on the ATLAS experiment at CERN’s Large Hadron Collider (LHC). Our group actively collaborates with the Theory Group and other experimental efforts within the Office of Science frontiers (energy, intensity, and cosmic) to search broadly in the ATLAS data to maximize our potential for scientifc discovery. The thrust of this proposal is to search for new physics using ATLAS data. Emphasis is placed on novel use of the Higgs boson as a tool to search for new particles and interactions and development of fast particle tracking systems for use in triggering. We propose to continue our strong role in FTK commissioning and operations. To maximize the impact and vitality of the proposed physics program through the HL-LHC running, we will lead firmware efforts in the HWTT Phase-II TDAQ upgrade, leveraging our expertise acquired through development of fast tracking hardware and firmware for the FTK system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Advanced Physics Models for Particle-Particle Interactions (UCD final report)

The LDRD "Advanced Physics Models for Particle-Particle Interactions” collaborative research with Professor Jennifer Sinclair Curtis and PhD student Chanh Nguyen at the University of California, Davis involved meso-scale, discrete element method (DEM) modeling of micron-sized particles impacting a substrate. Using DEM, physical insight into the details of particle response upon impact can be obtained. Such details provide the type of information necessary to enhance existing continuum or macro-scale simulation tools so that modeling for material strength and particle breakage models is based on particle physics versus empirical relationships. While molecular dynamics simulations of atoms can also provide physical insight into the particle response to impact, the scale of particles that are treated is on the order of angstroms, not microns. This study gives evidence to the ability of the DEM simulation technique to provide such fundamental, detailed insight for micron-sized particles as a function of particle properties and impact velocity.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Nuclear Power Plant Physical Protection Recommendation Document

This document is aimed at providing guidance to the National Nuclear Security Administration’s (NNSA) Office of International Nuclear Security’s (INS) country and regional teams for implementing effective physical protection systems (PPSs) for nuclear power plants (NPPs) to prevent the radiological consequences of sabotage. This recommendation document includes input from the Physical Protection Functional Team (PPFT), the Response Functional Team (RFT), and the Sabotage Functional Team (SFT) under INS. Specifically, this document provides insights into increasing and sustaining physical protection capabilities at INS partner countries’ NPP sites. Nuclear power plants should consider that the intent of this document is to provide a historical context as well as technologies and methodologies that may be applied to improve physical protection capabilities. It also refers to relevant guidance from the International Atomic Energy Agency (IAEA) and the U.S. Nuclear Regulatory Commission (NRC).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Coherent Neutrino-Nucleus Scattering: A Tool to Search for New Physics

The Neutrino is a well-understood particle in the models of particle physics. Even so, it is electrically neutral, and has a very small mass. As such, it’s interaction probability is extremely small. Detecting neutrinos has always been a challenge for particle physics. Over 40 years ago, Daniel Freedman predicted a new type of interaction that neutrinos could undergo that would be much more frequent. This has since been called Coherent Elastic Neutrino Nucleus Scattering (CEvNS). While this process occurred more frequently than the traditionally used interaction process (called inverse beta decay) it was more challenging to detect. Interestingly, though, if you could detect the CEvNS process, you would be sensitive to many new studies of fundamental particle physics. A researcher may also be able to use this process to study neutrinos in more applied settings, for instance by more easily monitoring the neutrinos given off during the nuclear reaction processes in nuclear reactors. The aim of this project was to observe coherent neutrino scattering for the first time, and begin to use it as a tool both to study new types of physics models, and to study the feasibility of this concept for applied situations.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Application of Quantum Machine Learning to High Energy Physics Analysis at LHC using IBM Quantum Computer Simulators and IBM Quantum Computer Hardware

Our group pioneers the use of Quantum Machine Learning (QML) on High Energy Physics analysis at LHC. We have successfully employed several QML classification algorithms in the ttH (Higgs production in association with a top quark pair) and Higgs to two muons (Higgs coupling to second generation fermions), two recent LHC flagship physics analysis, on gate-model quantum computer simulators and hardware. The simulation studies have been performed with the IBM Quantum Framework, Google Tensorflow Quantum Framework, and Amazon Braket Framework, and we have achieved good classification performance that is similar to the performances of the classical machine learning methods currently used in LHC physics analyses, classical SVM, classical BDT, and classical deep neural network for example. We have also performed our studies using IBM superconducting quantum computer hardware and the performance is promising and is approaching the performance from IBM quantum simulators. Moreover, we extend our studies to other QML areas such as quantum anomaly detection and quantum generative adversarial, and some preliminary results have been obtained. Also, we have overcome the challenges of intensive computing resources in the cases of large qubits (25 qubits or more) and large numbers of events using NVIDIA cuQuantum with NERSC Perlmutter HPC. Our studies give an example that Quantum Machine Learning performs as well as its classical counterpart for realistic High Energy Physics analysis datasets. Furthermore, our result on noisy quantum hardware provides important validation for the result on noiseless quantum simulators.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Nb SRF cavities applied to searches for beyond the standard model physics

The SRF technology developed for accelerators can be successfully applied to new applications, including quantum computing, dark matter searches and beyond the standard model physics. The ultra-high quality factor of SRF cavities can al-low to achieve unprecedented sensitivity in fields outside of the usual accelerator applications, for examples fundamental and beyond the standard model physics. Applications of SRF cavities for gravitational waves searches are also being investigated. The SQMS Physics and Sensing thrust is working on this effort as it strives to combine SRF cavities with QIS, with a focus on BSM and fundamental physics. Using Nb SRF cavities, we run a proof of principle search for the dark photon dark matter candidate at fixed frequency demonstrating the deepest sensitivity to wavelike dark mater [1]. The Dark SRF experiment uses two 1.3GHz SRF cavities to conduct a light shining through wall (LSW) experiment to attempt producing and detecting dark photons in the laboratory. The results of this first superconducting LSW search were recently published [2] and we are now working on the second phase of the experiment, which will be run in a dilution refrigerator using 2.6GHz SRF cavities. In addition to the limits we have achieved, we will also present the experiments we are currently developing, such as the heterodyne axion DM search [3, 4], the SERAPH series of experiments, and gravitational waves searches based on SRF cavities as proposed in [5-8].

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Physics-assisted Latent Space Dynamics Learning for Stiff Collisional-radiative Models

Collisional-radiative (CR) models describe the atomic processes in a plasma by tracking the population density in the ground and excited states for each charge state of the atom/ion. These models predict important plasma properties such as charge state distributions and radiative emissivity and opacity. Accurate descriptions of the CR balance of the plasma are essential in fusion whole device modeling, especially when significant impurities are introduced into the plasmas. In an integrated fusion plasma and CR simulation, a CR model, which is a high-dimensional stiff ODE, needs to be solved on each grid point in the configuration space, and can overwhelm the overall computational cost. In this work, we propose a machine-learning-based method that discovers a latent space and learns its corresponding latent dynamics, which can capture the essential physics to make accurate predictions at much lower online computational cost. The proposed approach is physics-assisted, due to its combination of a physical latent space and a data-driven latent space. It has been demonstrated that the proposed architecture can predict both the full-order CR dynamics and a physical quantity of interest accurately.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Center for Applied Physics and Superconducting Technologies (CAPST)

Fermi Research Alliance, LLC, as operator of Fermi National Accelerator Laboratory (Fermilab) and Northwestern University (Northwestern) have a broad overlap in scientific interests and complementary state-of-the-art facilities in the area of applied physics and technology, in particular physics of superconductivity, materials science, and detector technology. The Center for Applied Physics and Superconducting Technology (CAPST) will provide a mechanism for cross-utilizing capabilities of the two institutions for advances in understanding key scientific problems of mutual interest and opening up broader educational opportunities for students and postdoctoral candidates (postdocs). Northwestern undergraduate students, graduate students and postdocs will be able to perform cutting edge science and technology research in accelerator and detector science and the physics of superconductivity at Fermilab and Northwestern under the joint direction of Fermilab researchers and Northwestern professors. Research program covers three thrusts below, with subtopics detailed in the CRADA: • Vortex dynamics in superconductors • Superconducting devices • New frontiers in microwave instrumentation/techniques.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Physics-Informed Machine Learning Model for Ceramic Matrix Composite Creep

A physics-informed recurrent neural network (RNN) based surrogate model is developed to emulate the nonlinear, time-dependent constitutive behavior of ceramic matrix composites (CMCs) driven by matrix damage and constituent creep at the microscale. Physics-informed constraints are introduced into the surrogate model through regularization to ground the prediction in physics and improve its predictive capabilities. Training data is generated using the high-fidelity generalized method of cells (HFGMC) approach which calls appropriate creep and damage models for each of the constituents. This coupling permits simulating the nonlinear behavior of CMCs based on constituent response at the microscale along with microstructural features such as fiber and porosity volume fraction and fiber radius. The microscale repeating unit cell is loaded under creep fatigue conditions to replicate the material loading experienced in a turbine engine. Therefore, the RNN-based surrogate model is tasked with predicting, as a function of variable input stress sequence, temperature, and microstructural features, the resulting strain history response while satisfying physical constraints related to creep rate, isochoric inelastic deformation, and strain energy density. The trained surrogate model is shown to effectively match the strain history over quantified distributions of microstructural features and relevant loading regimes and temperatures. Neural network based surrogate models can offer efficient alternatives to running computationally intensive multiscale material models to simulate the nonlinear response of large structural models. Therefore, the presented work provides evidence towards the feasibility of developing, training, and running such models for CMCs with complex microstructures, nonlinear time-dependent material response, and under non-monotonic loading conditions.

ceramic matrix composites↗
DOE OSTI2022

Physics-Informed Neural Network Solution of Point Kinetics Equations for a Nuclear Reactor Digital Twin

A digital twin (DT) for nuclear reactor monitoring can be implemented using either a differential equations-based physics model or a data-driven machine learning model. The challenge of a physics-model-based DT consists of achieving sufficient model fidelity to represent a complex experimental system, whereas the challenge of a data-driven DT consists of extensive training requirements and a potential lack of predictive ability. We investigate the performance of a hybrid approach, which is based on physics-informed neural networks (PINNs) that encode fundamental physical laws into the loss function of the neural network. We develop a PINN model to solve the point kinetic equations (PKEs), which are time-dependent, stiff, nonlinear, ordinary differential equations that constitute a nuclear reactor reduced-order model under the approximation of ignoring spatial dependence of the neutron flux. The PINN model solution of PKEs is developed to monitor the start-up transient of Purdue University Reactor Number One (PUR-1) using experimental parameters for the reactivity feedback schedule and the neutron source. The results demonstrate strong agreement between the PINN solution and finite difference numerical solution of PKEs. We investigate PINNs performance in both data interpolation and extrapolation. For the test cases considered, the extrapolation errors are comparable to those of interpolation predictions. Extrapolation accuracy decreases with increasing time interval.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Bayesian Estimation of Oscillator Parameters: Toward Anomaly Detection and Cyber-Physical System Security

Cyber-physical system security presents unique challenges to conventional measurement science and technology. Anomaly detection in software-assisted physical systems, such as those employed in additive manufacturing or in DNA synthesis, is often hampered by the limited available parameter space of the underlying mechanism that is transducing the anomaly. As a result, the formulation of anomaly detection for such systems often leads to inverse or ill-posed problems, requiring statistical treatments. Here, we present Bayesian inference of unknown parameters associated with a generic actuator considered as a representative vital element of a cyber-physical system. Via a series of experimental input-output measurements, a transfer function for the actuator is obtained numerically, which serves as our model for the proposed method. Linear, nonlinear, and delayed dynamics may be assumed for the actuator response. By devising a code-based malicious signal, we study the efficacy of Bayesian inference for its potential to produce a detection, including uncertainty quantification, with a remarkably small number of input data points. Our approach should be adaptable to a variety of real-time cyber-physical anomaly detection scenarios.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Forward Physics Facility - Snowmass 2021 Letter of Interest

A rich physics program remains unexplored in the far-forward region at the LHC. The Forward Physics Facility (FPF) is a proposal to enlarge an existing cavern in the far-forward region of ATLAS to house a suite of experiments with groundbreaking new capabilities for neutrinos, long-lived particle searches, milli-charged particle searches, QCD, dark matter, dark sectors, and cosmic rays. The FPF will be located 500 m from the ATLAS interaction point. It is shielded from the ATLAS interaction point by 100 m of concrete and rock, creating an extremely low-background environment, ideal for many standard model studies and new physics searches. In this Letter of Interest, we describe the FPF’s location and general features, its physics potential in the HL-LHC era, and topics for further study.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS2002

Use of international space station for fundamental physics research

NASA's research plans aboard the International Space Station (ISS) are discussed. Experiments in low temperature physics and atomic physics are planned to commence in late 2005. Experiments in gravitational physics are planned to begin in 2007. A low temperature microgravity physics facility is under development for the low temperature and gravitation experiments.

space station fundamental physics↗
NASA NTRSDate not supplied

Physics Informed Neural Nets for Systems Health Management

To facilitate and solve the prediction problem, awareness of the current health state of the system is key, since it is necessary to perform condition-based predictions. To accurately predict the future state of any system, it is required to possess knowledge of its current health state and future operational conditions. Development in data-driven algorithms in regression of complex nonlinear functions and classification tasks have generated a growing interest in artificial intelligence for industrial applications. Complex multi-physics models as well as digital twins, once purely built on physics and corresponding simplified lumped parameter iterations, can now benefit from machine learning algorithms to mitigate the lack of understanding of some complex behavior. The research work presents application of physics-informed neural nets application to a representative electric powertrain for unmanned aerial vehicles. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage.

Physics Informed↗