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At least 955 records · Page 53

Quantum Machine Learning in High Energy Physics

Machine learning has been used in high energy physics for a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to apply quantum machine learning to High Energy Physics. This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics and provide an outlook on future applications.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Advancement of hybrid fluid-kinetic modeling for HEDP and ICF science

We report on the development progress of a hybrid fluid-kinetic code for simulating fluids and plasmas in a wide range of environments, such as laser–matter interactions, inertial confinement fusion, magnetic confinement fusion, and pulsed power. The suite of numerical tools under development utilizes heterogeneous computer architectures and leverages the benefits of particle–based simulation techniques. By working to combine the kinetic particle-in-cell (PIC) model with a particle-based fluid simulation technique, such as smoothed particle hydrodynamics, we are developing a flexible framework capable of accurately modeling complex flows within and between kinetic and fluid regimes. The TriForce code is under development as a C++ framework for parallel, 3D, particle-based, hybrid fluid-kinetic plasma simulations. The fluid half of TriForce will be based upon the meshless smoothed-particle-hydrodynamics (SPH) approach, well-suited for shear, mixing, and turbulence, whereas the kinetic half resembles a traditional particle-in-cell (PIC) code; other particle-based approaches to fluid modeling that do use a mesh are also possible to use and are under investigation. Maxwell’s electromagnetic field equations are solved either via explicit or implicit algorithms or approximated via resistive magnetohydrodynamics (MHD) using an Ohm’s law and resulting induction equation (extended MHD is under development). A primary goal of enabling direct comparisons, from the same code, between results from the variants of MHD and implicit electromagnetic solutions is to improve our fundamental understanding of systems with magnetic fields. The code is under development to recover results from both radiation-MHD and fully kinetic codes in those limits, and is continuing to be developed from other follow-on grants to operate in between where both descriptions may co-exist and interact. For certain applications, it is desired for a simulation to contain fluid ions and electrons as well as kinetic ions and electrons. Typically, it is too computationally intensive to model a full-scale ICF or HEDP experiment fully kinetically since many cycles are expended with very small time steps on modeling the fluid part of a material that is well treated by the fluid approximation. In this case, many traditional PIC particles can be replaced with a single fluid particle representing the thermal part of the distribution function, and there are fewer needed kinetic particles, which describe the non-thermal part and can be sub-cycled relative to the fluid particle advance. Furthermore, a pure fluid code may, depending on the problem, simply lack many physically important details that are beyond the scope of its reduced approximations and assumptions. In this report, we summarize the objectives achieved in the development of the collisional and kinetic half of the code, and the physics problems to which the code has been applied in the areas of advanced and innovative fusion concepts, pulsed power, and magneto-inertial fusion.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Perspectives on the integration between first-principles and data-driven modeling

Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. Further the opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive comparison of the hybridization techniques with respect to their differences and similarities, as well as advantages and limitations and future perspectives. Finally, we apply and illustrate hybrid modeling, physics-informed ML and model calibration via a chemical reactor case study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Is the Narrow E-Peak Distribution of Gamma-Ray Bursts Real?

Over the performance period of the research grant, the authors conducted a study of the role that the detector response plays in the detection of gamma-ray bursts. The goal of the study was to determine whether the fact that the gamma-ray bursts observed by the BATSE instrument on the Compton Gamma-ray Observatory are characterized by approximately the same characteristic energy is a consequence of the instrument's characteristics, or whether the distribution is a physical attribute of gamma-ray bursts. The authors succeeded in showing that instrumental effects are mild, and that the observed characteristic energy is a physical attribute of bursts. In the course of this research, the authors ported the computer code for calculating the BATSE detector response matrices to the Sun Solaris platform, and created a version of the code that runs under any platform that supports a Fortran 77 compiler with DEC extensions. This code has already been used by other investigators to analyze BATSE data. The authors constructed a Monte Carlo simulation of the BATSE burst trigger, with which they determined the efficiency of detecting a burst as a function of characteristic burst spectral energy. The results were then applied to BATSE observations to determine the physical model for the distribution of burst characteristic energies.

Brainerd, Jerome J.↗

In Situ Resource Utilization Technology Research and Facilities Supporting the NASA's Human Systems Research and Technology Life Support Program

The NASA Microgravity Science program has transitioned research required in support of NASA s Vision for Space Exploration. Research disciplines including the Materials Science, Fluid Physics and Combustion Science are now being applied toward projects with application in the planetary utilization and transformation of space resources. The scientific and engineering competencies and infrastructure in these traditional fields developed at multiple NASA Centers and by external research partners provide essential capabilities to support the agency s new exploration thrusts including In-Situ Resource Utilization (ISRU). Among the technologies essential to human space exploration, the production of life support consumables, especially oxygen and; radiation shielding; and the harvesting of potentially available water are realistically achieved for long-duration crewed missions only through the use of ISRU. Ongoing research in the physical sciences have produced a body of knowledge relevant to the extraction of oxygen from lunar and planetary regolith and associated reduction of metals and silicon for use meeting manufacturing and repair requirements. Activities being conducted and facilities used in support of various ISRU projects at the Glenn Research Center and Marshall Space Flight Center will be described. The presentation will inform the community of these new research capabilities, opportunities, and challenges to utilize their materials, fluids and combustion science expertise and capabilities to support the vision for space exploration.

Schlagheck, Ronald A.↗

Physics of the primitive solar accretion disk

The theory of viscous accretion disks developed by Lynden-Bell and Pringle (1974) has been applied to the evolution of the primitive solar nebula. The additional physical input needed to determine the structure of the disk is described. A series of calculations was carried out using a steady flow approximation to explore the effects on the disk properties of variations in such parameters as the angular momentum and accretion rate of the infalling material from a collapsing interstellar cloud fragment. The more detailed evolutionary calculations involved five cases with various combinations of parameters. It was concluded that the late stages of evolution of the disks would be dominated by the effects of mass loss from the expansion of a hot disk corona into space, and the effects of this were included in the evolutionary calculations. A new theory of comet formation is formulated upon these results. The most important result is the conclusion that the primitive solar accretion disk was repeatedly unstable against axisymmetric perturbations, in which rings would form and collapse upon themselves, with the subsequent formation of giant gaseous protoplanets.

Cameron, A. G. W.↗

Characterization of Asymmetry in Magnetoacoustic Emission Burst by Numerical Processes

It has been well known that the pattern of the magnetoacoustic emission (MAE) burst observed during the sweep over one half-cycle of the hysteresis loop becomes asymmetric depending on the strength of the magnetic domain wall-defect interaction and the state of residual stresses in a ferromagnet. The ascending asymmetry due to the former has been observed at a very low frequency (.7 Hz) of applied AC magnetic field at a given amplitude. The descending asymmetry due to uniaxial compressive stress has been typically observed at the AC applied magnetic field frequency of 20 Hz. The physical interpretation of both types of asymmetry has been well established. It is, however, necessary to perform investigations of the dependence of asymmetry on externally controlled parameters such as the amplitude and frequency of the AC applied magnetic fields. The purpose of the present study is therefore to devise a mathematical means that describes the degree of asymmetry of the MAE burst and apply this scheme to investigate the AC magnetic field amplitude dependence of the asymmetry.

Namkung, M.↗

Gravity-Dependent Combustion and Fluids Research - From Drop Towers to Aircraft to the ISS

Driven by the need for knowledge related to the low-gravity environment behavior of fluids in liquid fuels management, thermal control systems and fire safety for spacecraft, NASA embarked on a decades long research program to understand, accommodate and utilize the relevant phenomena. Beginning in the 1950s, and continuing through to today, drop towers and aircraft were used to conduct an ever broadening and increasingly sophisticated suite of experiments designed to elucidate the underlying gravity-dependent physics that drive these processes. But the drop towers and aircraft afford only short time periods of continuous low gravity. Some of the earliest rocket test flights and manned space missions hosted longer duration experiments. The relatively longer duration low-g times available on the space shuttle during the 1980s and 1990s enabled many specialized experiments that provided unique data for a wide range of science and engineering disciplines. Indeed, a number of STS-based Spacelab missions were dedicated solely to basic and applied microgravity research in the biological, life and physical sciences. Between 1980 and 2000, NASA implemented a vigorous Microgravity Science Program wherein combustion science and fluid physics were major components. The current era of space stations from the MIR to the International Space Station have opened up a broad range of opportunities and facilities that are now available to support both applied research for technologies that will help to enable the future exploration missions and for a continuation of the non-exploration basic research that began over fifty years ago. The ISS-based facilities of particular value to the fluid physics and combustion/fire safety communities are the Fluids and Combustion Facility Combustion Integrated Rack and the Fluids Integrated Rack.

Urban, David L.↗

LANL Weapons Physics Directorate: Mission Scope and Opportunities [Slides]

The Weapons Physics Directorate stewards a number of critical capabilities to meet the LANL mission. To execute our mission, ALDX develops and applies cutting-edge theory, computational models and multi-physics simulation codes, and designs, executes, and analyzes complex experiments. Our mission is to sustain the current stockpile, provide future stockpile options, and help shape a globalized nuclear world.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Study of Ring Current Dynamics During Geomagnetic Storms

This research program considered modeling the dynamical evolution of the ring current during several geomagnetic storms. The first year (6/01/1997-5/31/1998) of this successful collaborative research between the University of New Hampshire (UNH) and the University of California Los Angeles (UCLA) was supported by NASA grant NAG5-4680. The second and third years (6/01/1998-5/31/2000) were funded at UNH under NASA grant NAG5-7368. Research work at UNH concentrated on further development of a kinetic model to treat all of the important physical processes that affect the ring current ion population during storm conditions. This model was applied to simulate ring current development during several International Solar-Terrestrial Physics (ISTP) events, and the results were directly compared to satellite observations. A brief description of our major accomplishments and a list of the publications and presentations resulting from this effort are given.

Jordanova, Vania K.↗

Interactions between Artificial Gravity, the Affected Physiological Systems, and Nutrition

Malnutrition, either by insufficient supply of some nutrients or by overfeeding, has a profound effect on the health of an organism. Therefore, optimal nutrition is a necessity in normal gravity on Earth, in microgravity, and when applying artificial gravity to the human system. Reduced physical activity, such as observed in microgravity or bed rest, has an effect on many physiological systems, such as the cardiovascular, musculoskeletal, immune, and body fluids regulation systems. There is currently no countermeasure that is effective to counteract both the cardiovascular and musculoskeletal deconditioning when applied for a short duration (see Chapter 1). Artificial gravity therefore seems the simplest physiological approach to keep these systems intact. The application of intermittent daily dose of artificial gravity by means of centrifugation has often been proposed as a potential countermeasure against the physiological deconditioning induced by spaceflight. However, neither the optimal gravity level, nor its optimal duration of exposure have been enough studied to recommend a validated, effective, and efficient artificial gravity application. As discussed in previous chapters, artificial gravity has a very high potential to counteract any changes caused by reduced physical activity. The nutrient supply, which ideally should match the actual needs, will interact with these changes and therefore has also to be taken into account. This chapter reviews the potential interactions between these nutrients (energy intake, vitamins, minerals) and the other physiological systems affected by artificial gravity generated by an on-board short-radius centrifuge.

Heer, Martina↗

HOSS: an implementation of the combined finite-discrete element method

Nearly thirty years since its inception, the combined finite-discrete element method (FDEM) has made remarkable strides in becoming a mainstream analysis tool within the field of Computational Mechanics. FDEM was developed to effectively “bridge the gap” between two disparate Computational Mechanics approaches known as the finite and discrete element meth-ods. At Los Alamos National Laboratory (LANL) researchers developed the Hybrid Optimization Software Suite (HOSS) as a hybrid multi-physics platform, based on FDEM, for the simulation of solid material behavior complemented with the latest technological enhancements for full fluid–solid interaction. Furthermore, in HOSS, several newly developed FDEM algorithms have been implemented that yield more accurate material deformation formulations, inter-particle interaction solvers, and fracture and fragmentation solutions. Additionally, an explicit computational fluid dynamics solver and a novel fluid–solid interaction algorithms have been fully integrated (as opposed to coupled) into the HOSS’ solid mechanical solver, allowing for the study of an even wider range of problems. Advancements such as this are leading HOSS to become a tool of choice for multi-physics problems. Finally, HOSS has been successfully applied by a myriad of researchers for analysis in rock mechanics, oil and gas industries, engineering application (structural, mechanical and biomedical engineering), mining, blast loading, high velocity impact, as well as seismic and acoustic analysis. This paper intends to summarize the latest development and application efforts for HOSS.

58 GEOSCIENCES↗

Computer Simulation of Proton Transport in Fuel Cell Membranes (Final Report)

This DOE-supported research grant focused on understanding the nature of proton transport in complex systems such as proton exchange membranes (PEMs). The most unique aspect of the research was the development and implementation of a novel multiscale reactive molecular dynamics (MS-RMD) methodology. In this approach, covalent bonds can dynamically break and form, allowing one to accurately treat the proton hopping process essential to capturing the physics of proton transport. The Voth group applied this method to proton exchange membrane systems, providing insight into their proton transport mechanism. They found that protons can diffuse most rapidly in the water-rich regions, but that protons actually spend so little time in such regions that transport along the hydrophobic – hydrophilic interface controls the membrane performance. In addition, the group worked to increase understanding of acidic solutions, developing methods for simulating and interpreting experimental infrared vibrational spectroscopy for excess protons (acidic solutions). The group also implemented novel tools for developing proton transport reactive MD models using a relative entropy minimization scheme.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Anomaly Detection and Mitigation for Dynamic Frequency Regulation in Hydropower-Battery Systems: Preprint

Hydropower operators and energy storage providers are increasingly interested in participating in frequency regulation services, driven by the incentives offered by independent system operators, such as the Pennsylvania-New Jersey-Maryland Interconnection (PJM), in a competitive electricity market. This transition, however, unfolds against the backdrop of a modernizing and rapidly digitizing power grid, exposing the integrated legacy infrastructure and vulnerable communication networks to a multitude of cybersecurity threats. These evolving threats not only endanger grid operations but also have the potential to trigger cascading disruptions across the broader grid network and influence regulation markets. This work presents an approach for developing an anomaly detection and mitigation system to address cybersecurity challenges during the participation of a hydropower-integrated battery energy storage system (BESS) in a frequency regulation market. The applied anomaly detector utilizes machine learning algorithms to provide detailed classification of cyber-physical events and provide a comprehensive situation awareness to grid operators. Later, the applied mitigation system triggers predefined corrective actions to minimize the impact of data integrity attacks on the regulation market and system stability. We evaluated the proposed approach on a fully active BESS topology using the slow regulation signal (Reg A) coming from the PJM market. Our simulation results reveal that the proposed approach performs well in detecting data integrity attacks within the allocated time frame and also minimizes the system's instability and economic loss during the participation of hydropower and BESS in the regulation market.

battery energy storage system↗

Estimation of plasma properties using an extended Kalman filter with plasma global models

A physically-constrained extended Kalman filter (EKF) is applied to various zero-dimensional global models for the estimation of plasma properties using time-dependent experimental data such as the plasma density or ion flux. The capability of the EKF is demonstrated to estimate unknown system states simultaneously, such as reaction rate coefficients and the absorbed electron input power, which can be difficult, if not impossible, to measure experimentally. Global models accounting for pure argon reactions and argon-oxygen reactions are used in this work to demonstrate the ability of the filter to estimate dynamic and complex systems. Furthermore, the results obtained from the EKF plasma global model illustrate that model-data fusion techniques can be used to estimate plasma properties and processes for time-varying systems, such as pulsed discharges.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Physics-guided Deep Learning for Power System State Estimation

In the past decade, dramatic progress has been made in the field of machine learning. This paper explores the possibility of applying deep learning in power system state estimation. Traditionally, physics-based models are used including weighted least square (WLS) or weighted least absolute value (WLAV). These models typically consider a single snapshot of the system without capturing temporal correlations of system states. In this paper, a physics-guided deep learning (PGDL) method is proposed. Specifically, inspired by autoencoders, deep neural networks (DNNs) are used to learn the temporal correlations. The estimated system states from DNNs are then checked against physics laws by running through a set of power flow equations. Hence, the proposed PGDL is both data-driven and physics-guided. The accuracy and robustness of the proposed PGDL method are compared with traditional methods in standard IEEE cases. Simulations show promising results and the applicability is further discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Algebraic Generation Of Two-Dimensional Grids

Two-Boundary Grid Generation (TBGG) computer program applies interactive algebraic grid-generation technique in two dimensions. Incorporates algebraic equations that relate uniform rectangular computational domain to arbitrary physical domain. Hermite cubic interpolation performed between fixed, nonintersecting boundaries. Applied to variety of problems in which discrete techniques used to solve such partial differential equations as governing equations of fluid flow. Written in FORTRAN 77.

Smith, Robert E.↗

Slicing with deep learning models at ProtoDUNE-SP

DUNE is a cutting-edge experiment aiming to study neutrinos in detail, with a special focus on the flavor oscillation mechanism. The prototype of the DUNE Far Detector Single Phase TPC (ProtoDUNE-SP) was built and operated at CERN with a full set of reconstruction tools. To implement these reconstruction tools, Pandora, a multi-algorithm framework, has been developed. A large number of these algorithms, some of them being exploiting traditional clustering, detector physics and deep learning approaches, have been applied to images to gradually build up a picture out of singular events. One of such algorithms is the Pandora slicing algorithm which aims to partition the detector hits of an event in sets called slices. Each slice represents a single interaction in the detector and should identify all the hits related to the interacting particle and its subsequent decay products. We expect the order of tens of slices per event in ProtoDUNE-SP. In this paper we present a deep learning approach to the problem, designing a model able to outperform the state-of-the-art slicing algorithm which is currently implemented within Pandora. We assess the performance of our tool in terms of efficiency and accuracy, while exploiting hardware accelerating setups. The ultimate goal is to incorporate this deep learning approach in the Pandora reconstruction tool.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗