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 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2017

Method for Determining the Sensitivity of a Physical Security System.

Modern systems, such as physical security systems, are often designed to involve complex interactions of technological and human elements. Evaluation of the performance of these systems often overlooks the human element. A method is proposed here to expand the concept of sensitivity—as denoted by d’—from signal detection theory (Green & Swets 1966; Macmillan & Creelman 2005), which came out of the field of psychophysics, to cover not only human threat detection but also other human functions plus the performance of technical systems in a physical security system, thereby including humans in the overall evaluation of system performance. New in this method is the idea that probabilities of hits (accurate identification of threats) and false alarms (saying “threat” when there is not one), which are used to calculate d’ of the system, can be applied to technologies and, furthermore, to different functions in the system beyond simple yes-no threat detection. At the most succinct level, the method returns a single number that represents the effectiveness of a physical security system; specifically, the balance between the handling of actual threats and the distraction of false alarms. The method can be automated, and the constituent parts revealed, such that given an interaction graph that indicates the functional associations of system elements and the individual probabilities of hits and false alarms for those elements, it will return the d’ of the entire system as well as d’ values for individual parts. The method can also return a measure of the response bias* of the system. One finding of this work is that the d’ for a physical security system can be relatively poor in spite of having excellent d’s for each of its individual functional elements.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2023

IRIS-HEP Strategic Plan for the Next Phase of Software Upgrades for HL-LHC Physics

The quest to understand the fundamental building blocks of nature and their interactions is one of the oldest and most ambitious of human scientific endeavors. CERN's Large Hadron Collider (LHC) represents a huge step forward in this quest. The discovery of the Higgs boson, the observation of exceedingly rare decays of $B$ mesons, and stringent constraints on many viable theories of physics beyond the Standard Model (SM) demonstrate the great scientific value of the LHC physics program. The next phase of this global scientific project will be the High-Luminosity LHC (HL-LHC) which will collect data starting circa 2029 and continue through the 2030s. The primary science goal is to search for physics beyond the SM and, should it be discovered, to study its implications. In the HL-LHC era, the ATLAS and CMS experiments will record around 100 times as many collisions as were used to discover the Higgs boson (and at twice the energy). Both NSF and DOE are making large detector upgrade investments so the HL-LHC can operate in this high-rate environment. Similar investment in software R&D for acquiring, managing, processing and analyzing HL-LHC data is critical to maximize the return-on-investment in the upgraded accelerator and detectors. This report presents a strategic plan for a possible second 5-year funded phase (2023 through 2028) for the Institute for Research and Innovation in Software for High Energy Physics (IRIS-HEP) which will close remaining software and computing gaps to deliver HL-LHC science.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

DEMONSTRATION OF A DATA-DRIVEN PHYSICS-BASED APPROACH FOR COMPUTATIONALLY EFFICIENT CFD PREDICTION OF TWO-PHASE BUBBLY FLOW

To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale physics-guided data-driven approach, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.

42 ENGINEERING↗
DOE OSTI2013

Research on acceleration method of reactor physics based on FPGA platforms

The physical designs of the new concept reactors which have complex structure, various materials and neutronic energy spectrum, have greatly improved the requirements to the calculation methods and the corresponding computing hardware. Along with the widely used parallel algorithm, heterogeneous platforms architecture has been introduced into numerical computations in reactor physics. Because of the natural parallel characteristics, the CPU-FPGA architecture is often used to accelerate numerical computation. This paper studies the application and features of this kind of heterogeneous platforms used in numerical calculation of reactor physics through practical examples. After the designed neutron diffusion module based on CPU-FPGA architecture achieves a 11.2 speed up factor, it is proved to be feasible to apply this kind of heterogeneous platform into reactor physics. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Benchmark Comparisons of Lattice Physics Calculations for Thorium-Based Fuels in Pressure-Tube Heavy Water Reactors

Thorium is a fertile nuclear fuel resource that is nearly three times as abundant as uranium in the earth's crust and that can be exploited and used to extend uranium resources. The pressure tube heavy water reactor (PTHWR) is an excellent existing technology for utilizing thorium-based fuels, due to its high neutron economy achieved by using heavy water as a moderator and coolant, the ability to perform online refueling, and its flexibility in fuel management. Reactor physics studies are underway at the Canadian Nuclear Laboratories (CNL) to evaluate several types of thorium-based fuels that could be implemented in PT-HWRs in the near-term and intermediate future, building upon past experience. For preliminary, conceptual studies, deterministic lattice physics and core physics codes such as WIMS-AECL and RFSP are used to provide quick and more approximate estimates of performance and safety characteristics. The objective of this study was to perform 2D lattice physics benchmark comparisons between WIMS-AECL and MCNP5 for several types of thorium-based fuel bundle lattices for potential use in PT-HWRs. Benchmark comparisons will help confirm the validity of deterministic calculation results, and provide estimates of the potential magnitude of calculation errors and uncertainties. Areas for potential improvements in the WIMS-AECL calculations may also be identified. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗