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At least 1,189 records · Page 66

Particle Acceleration in Active Galactic Nuclei

The high efficiency of energy generation inferred from radio observations of quasars and X-ray observations of Seyfert active galactic nuclei (AGNs) is apparently achieved only by the gravitational conversion of the rest mass energy of accreting matter onto supermassive black holes. Evidence for the acceleration of particles to high energies by a central engine is also inferred from observations of apparent superluminal motion in flat spectrum, core-dominated radio sources. This phenomenon is widely attributed to the ejection of relativistic bulk plasma from the nuclei of active galaxies, and accounts for the existence of large scale radio jets and lobes at large distances from the central regions of radio galaxies. Reports of radio jets and superluminal motion from galactic black hole candidate X-ray sources indicate that similar processes are operating in these sources. Observations of luminous, rapidly variable high-energy radiation from active galactic nuclei (AGNs) with the Compton Gamma Ray Observatory show directly that particles are accelerated to high energies in a compact environment. The mechanisms which transform the gravitational potential energy of the infalling matter into nonthermal particle energy in galactic black hole candidates and AGNs are not conclusively identified, although several have been proposed. These include direct acceleration by static electric fields (resulting from, for example, magnetic reconnection), shock acceleration, and energy extraction from the rotational energy of Kerr black holes. The dominant acceleration mechanism(s) operating in the black hole environment can only be determined, of course, by a comparison of model predictions with observations. The purpose of the work proposed for this grant was to investigate stochastic particle acceleration through resonant interactions with plasma waves that populate the magnetosphere surrounding an accreting black hole. Stochastic acceleration has been successfully applied to the problem of ion and electron energization in solar flares, and is capable of accounting for a wide range of both neutral and charged particle emissions. It is also a component in diffusive shock acceleration, since pitch-angle scattering (which is necessary for multiple shock crossings) is accompanied by diffusion in momentum space, which in turn yields a net systematic energy gain; however, stochastic energization will dominate the first-order shock process only in certain parameter regimes. Although stochastic acceleration has been applied to particle energization in the lobes of radio galaxies, its application to the central regions of AGNs has only recently been considered, but not in detail. We proposed to systematically investigate the plasma processes responsible for stochastic particle acceleration in black hole magnetospheres along with the energy-loss processes which impede particle energization. To this end we calculated acceleration rates and escape time scales for protons and electrons resonating with Alfven waves, and for electrons resonating with whistlers. Assuming either a Kolmogorov or Kraichnan wave spectrum, accretion at the Eddington limit, magnetic field strengths near equipartition, and turbulence energy densities approx. 10% of the total magnetic field energy density, we find that Alfven waves accelerate protons to Lorentz factors approx, equals 10(exp 4) - 10(exp 6) before they escape from the system. Acceleration of electrons by fast mode and whistler waves can produce a nonthermal population of relativistic electrons whose maximum energy is determined by a competition with radiation losses.

Miller, James A.↗

EXAGRAPH: Graph and combinatorial methods for enabling exascale applications

Combinatorial algorithms in general and graph algorithms in particular play a critical enabling role in numerous scientific applications. However, the irregular memory access nature of these algorithms makes them one of the hardest algorithmic kernels to implement on parallel systems. With tens of billions of hardware threads and deep memory hierarchies, the exascale computing systems in particular pose extreme challenges in scaling graph algorithms. The codesign center on combinatorial algorithms, ExaGraph, was established to design and develop methods and techniques for efficient implementation of key combinatorial (graph) algorithms chosen from a diverse set of exascale applications. Algebraic and combinatorial methods have a complementary role in the advancement of computational science and engineering, including playing an enabling role on each other. In this paper, we survey the algorithmic and software development activities performed under the auspices of ExaGraph from both a combinatorial and an algebraic perspective. In particular, we detail our recent efforts in porting the algorithms to manycore accelerator (GPU) architectures. We also provide a brief survey of the applications that have benefited from the scalable implementations of different combinatorial algorithms to enable scientific discovery at scale. We believe that several applications will benefit from the algorithmic and software tools developed by the ExaGraph team.

97 MATHEMATICS AND COMPUTING↗

CHARM-SYCL: New Unified Programming Environment for Multiple Accelerator Types

Addressing performance portability across diverse accelerator architectures has emerged as a major challenge in the development of application and programming systems for high-performance computing environments. Although recent programming systems that focus on performance portability have significantly improved productivity in an effort to meet this challenge, the problem becomes notably more complex when compute nodes are equipped with multiple accelerator types—each with unique performance attributes, optimal data layout, and binary formats. To navigate the intricacies of multi-accelerator programming, we propose CHARM-SYCL as an extension of our CHARM multi-accelerator execution environment [27]. This environment will combine our SYCL-based performance-portability programming front end with a back end for extremely heterogeneous architectures as implemented with the IRIS runtime from Oak Ridge National Laboratory. Our preliminary evaluation indicates potential productivity boost and reasonable performance compared to vendor-specific programming system and runtimes.

Fujita, Norihisa↗

Destabilizing high-capacity high entropy hydrides via earth abundant substitutions: From predictions to experimental validation

The vast chemical space of high entropy alloys (HEAs) makes trial-and-error experimental approaches for materials discovery intractable and often necessitates data-driven and/or first principles computational insights to successfully target materials with desired properties. In the context of materials discovery for hydrogen storage applications, a theoretical prediction-experimental validation approach can vastly accelerate the search for substitution strategies to destabilize high-capacity hydrides based on benchmark HEAs, e.g. TiVNbCr alloys. Here, in this study, machine learning predictions, corroborated by density functional theory calculations, predict substantial hydride destabilization with increasing substitution of earth-abundant Fe content in the (TiVNb) 75 Cr 25-x Fe x system. The as-prepared alloys crystallize in a single-phase bcc lattice for limited Fe content x < 7, while larger Fe content favors the formation of a secondary C14 Laves phase intermetallic. Short range order for alloys with x < 7 can be well described by a random distribution of atoms within the bcc lattice without lattice distortion. Hydrogen absorption experiments performed on selected alloys validate the predicted thermodynamic destabilization of the corresponding fcc hydrides and demonstrate promising lifecycle performance through reversible absorption/desorption. This demonstrates the potential of computationally expedited hydride discovery and points to further opportunities for optimizing bcc alloy ↔ fcc hydrides for practical hydrogen storage applications.

36 MATERIALS SCIENCE↗

Perception-action relationships reconsidered in light of spatial display instruments

Spatial display instruments convey information about both the identity and the location of objects in order to assist surgeons, astronauts, pilots, blind individuals, and others in identification, remote manipulations, navigation, and obstacle avoidance. Scientists believe that these instruments have not reached their full potential and that progress toward new applications, including the possibility of restoring sight to the blind, will be accelerated by advancing the understanding of perceptual processes. This stimulating challenge to basic researchers was advanced by Paul Bach-Y-Rita (1972) and by the National Academy of Science (1986) report on Electronic Aids for the Blind. Although progress has been made, new applications of spatial display instruments in medicine, space, aviation, and rehabilitation await improved theoretical and empirical foundations.

Shebilske, Wayne L.↗

Kuang's Semi-Classical Formalism for Calculating Electron Capture Cross Sections: A Space- Physics Application

Accurate estimates of electroncapture cross sections at energies relevant to the modeling of the transport, acceleration, and interaction of energetic neutral atoms (ENA) in space (approximately few MeV per nucleon) and especially for multi-electron ions must rely on detailed, but computationally expensive, quantum-mechanical description of the collision process. Kuang's semi-classical approach is an elegant and efficient way to arrive at these estimates. Motivated by ENA modeling efforts for apace applications, we shall briefly present this approach along with sample applications and report on current progress.

Barghouty, A. F.↗

Accelerating Random Forest Classification on GPU and FPGA

Random Forests (RFs) are a commonly used machine learning method for classification and regression tasks spanning a variety of application domains, including bioinformatics, business analytics, and software optimization. While prior work has focused primarily on improving performance of the training of RFs, many applications, such as malware identification, cancer prediction, and banking fraud detection, require fast RF classification. In this work, we accelerate RF classification on GPU and FPGA. In order to provide efficient support for large datasets, we propose a hierarchical memory layout suitable to the GPU/FPGA memory hierarchy. We design three RF classification code variants based on that layout, and we investigate GPU- and FPGA-specific considerations for these kernels. Our experimental evaluation, performed on an Nvidia Xp GPU and on a Xilinx Alveo U250 FPGA accelerator card using publicly available datasets on the scale of millions of samples and tens of features, covers various aspects. First, we evaluate the performance benefits of our hierarchical data structure over the standard compressed sparse row (CSR) format. Second, we compare our GPU implementation with cuML, a machine learning library targeting Nvidia GPUs. Third, we explore the performance/accuracy tradeoff resulting from the use of different tree depths in the RF. Finally, we perform a comparative performance analysis of our GPU and FPGA implementations. Our evaluation shows that for high accuracy targets, our GPU implementation yields 5-9x speedup over CSR, and up to a 2x speedup over cuML.

FPGA, Xilinx FPGA, GPU, Random Forest classificati↗

U.S. Research Impact Alliance IMPACT Accelerator

The U.S. Research Impact Alliance (USRIA) was created with a vision to leverage successful models that accelerate technology development and commercialization, and support startup businesses based upon federal research investments. Building upon lessons learned and feedback from cohort teams, the USRIA programs have morphed into customized solutions that offer services which provide the most impact to startup businesses. Based upon discussions with small and startup businesses, entrepreneurs, and technology developers, a variety of resources have been developed to support each of these target audiences. USRIA created program differentiators to offer one-on-one engagement and direct communication with cohort teams, highly relevant conversations with targeted introductions, and minimal “class” time (only used when an issue is applicable to all cohort members).

43 PARTICLE ACCELERATORS↗

White Paper on High Temperature Superconducting Bi-2212 Magnets for Energy Frontier Circular Colliders

As the only high-temperature superconducting (HTS) material available as an isotropic, twisted, multifilamentary round wire, Bi-2212 is very promising for expanding the high-field superconducting accelerator magnet toolbox beyond the round-wire, isotropic Nb-Ti and Nb3Sn conductors used by HEP so far. In this paper, we describe the important roles that Bi-2212 might play for future high energy circular colliders including both high energy proton and muon colliders. We describe its present technology status (conductor development, magnet design concepts, prototype magnet status) and then provide a ten-year plan for >15 T accelerator magnets and >25 T solenoid magnets within an integrated strategy to engage industry and the entire US and international scientific enterprise interested in HTS magnet applications.

43 PARTICLE ACCELERATORS↗

ZMPY3D: accelerating protein structure volume analysis through vectorized 3D Zernike moments and Python-based GPU integration

Abstract Motivation Volumetric 3D object analyses are being applied in research fields such as structural bioinformatics, biophysics, and structural biology, with potential integration of artificial intelligence/machine learning (AI/ML) techniques. One such method, 3D Zernike moments, has proven valuable in analyzing protein structures (e.g., protein fold classification, protein–protein interaction analysis, and molecular dynamics simulations). Their compactness and efficiency make them amenable to large-scale analyses. Established methods for deriving 3D Zernike moments, however, can be inefficient, particularly when higher order terms are required, hindering broader applications. As the volume of experimental and computationally-predicted protein structure information continues to increase, structural biology has become a “big data” science requiring more efficient analysis tools. Results This application note presents a Python-based software package, ZMPY3D, to accelerate computation of 3D Zernike moments by vectorizing the mathematical formulae and using graphical processing units (GPUs). The package offers popular GPU-supported libraries such as CuPy and TensorFlow together with NumPy implementations, aiming to improve computational efficiency, adaptability, and flexibility in future algorithm development. The ZMPY3D package can be installed via PyPI, and the source code is available from GitHub. Volumetric-based protein 3D structural similarity scores and transform matrix of superposition functionalities have both been implemented, creating a powerful computational tool that will allow the research community to amalgamate 3D Zernike moments with existing AI/ML tools, to advance research and education in protein structure bioinformatics. Availability and implementation ZMPY3D, implemented in Python, is available on GitHub (https://github.com/tawssie/ZMPY3D) and PyPI, released under the GPL License.

Lai, Jhih-Siang (ORCID:0000000156775890)↗

Fixing Amdahl's Law within the Limits of Accelerated Systems: FALLACY

Closeout report for FALLACY project. The performance of Data Model Convergence Initiative (DMC) applications on parallel machines is far below the limit set by Amdahl’s law. Whether the machine is based on many-core, GPUs, FPGAs, or a heterogeneous combination, usually the most significant bottleneck is accessing data from the memory system. Aligning with DMC’s HW/architecture thrust, this project developed a set of memory-centric tools called ‘MemGaze’ that inform the HW/SW stack about an application’s memory behavior, including data access latency and diagnosing poor data layout and data composition. Our approach uses architectural modeling and analysis of workload data accesses.

97 MATHEMATICS AND COMPUTING↗

In-Situ Monitoring Assisted Large-Scale Additive Manufacturing of Mild Steel and 316L Alloys for Nuclear Application

The Advanced Materials and Manufacturing Technologies (AMMT) Program is aimed at developing cross-cutting technologies in support of a broad range of nuclear reactor parts, and to maintain U.S. leadership in materials and manufacturing technologies for nuclear energy applications. The overarching vision of the AMMT program is to accelerate the development, qualification, demonstration and deployment of advanced materials and manufacturing technologies to enable reliable and economical nuclear energy. One of its three goals is to target big challenges and game-changing technologies, to realize the mission and vision of AMMT program. Based on this context, this multi-year work package focuses on understanding the current state of large-scale additive manufacturing (AM) technology for the deposition of 316L stainless steel materials for final components and mild steel for use in nuclear manufacturing processes. The targeted AM modality is directed energy deposition (DED), capable of fabricating components on the size scale of meters including valves, pumps, impellers. etc. that are challenging or difficult to source, especially when developing new systems or replacing obsolete components. Accordingly, the current writeup aims at providing a baseline literature survey on structure-property relationships in mild steel and 316L alloys. Also, information on preliminary trials to date involving these two alloys show tremendous potential of printing parts having complex geometry and thin- walled structures, such as nuclear valve and Hot isostatic Press (HIP) can, using wire based (Wire Arc Additive Manufacturing and Hybrid Additive Manufacturing) as well as blown powder DED machines. All of this is aimed towards (i) demonstrating the ability to fabricate large components for pressure boundary applications relevant to the nuclear community and nuclear manufacturing technology, and (ii) understanding the effect of different manufacturing technology on AM can production and post HIPed material for nuclear applications. Another target of this writeup is to compile various in-situ monitoring tools that have been incorporated for different DED AM modalities, in order to understand process variability during the entire fabrication process. This can be correlated with processing-structure-property response surfaces and would add confidence around process quality verification and ultimately component certification for nuclear applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

E-Beam Destruction of Contaminants in Water

Next-generation electron beam (E-beam) technology creates the potential to treat the complex and difficult waste streams of today and tomorrow. Fermi National Accelerator Laboratory brings 50 years of groundbreaking E-beam experience across technology research, innovation, and application to unlocking this potential from a capacity and cost perspective using proprietary Lab intellectual property. Fermilab’s technology will deliver a high throughput, portable, scalable, and cost-effective solution to a wide variety of contaminants. Potential applications include contaminants of increasing health, regulatory, economic, and public relations concerns.

43 PARTICLE ACCELERATORS↗

Cluster Dynamics Modeling Needs for the Advanced Materials and Manufacturing Technologies Program

This milestone report aims to identify and assess the cluster dynamics (CD) modeling requirements within the Department of Energy's Office of Nuclear Energy (DOE-NE) Advanced Materials and Manufacturing Technologies (AMMT) program and to communicate these needs to the DOE-NE Nuclear Energy Advanced Modeling and Simulation (NEAMS) program. The goal is to ensure NEAMS is well-informed about the CD modeling requirements to support AMMT's mission of accelerating the development, qualification, demonstration, and deployment of advanced structural materials and manufacturing for nuclear energy applications. CD modeling is an essential tool for predicting the degradation of structural materials under irradiation, which is a key component of AMMT's accelerated qualification process. The AMMT program focuses on both additively manufactured and wrought structural alloys, such as laser powder-bed fusion 316H austenitic stainless steel, alloy 709, Haynes 244, and alloy 617. These materials require a generalized CD modeling framework to facilitate rapid model development and computational simulation. A flexible, generalized CD software, similar to the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework, would enable modeling of various cluster types, including defect clusters, defect-solute clusters, and multicomponent clusters, incorporating thermodynamics and kinetics parameters. Radiation effects, microstructural feature evolution, and multi-dimensional modeling are critical considerations for the CD model. The usability of the CD code should allow for easy modification and coupling with MOOSE-based simulations. Additionally, the software should adhere to Nuclear Quality Assurance-1 standards, include a testing suite for verification and validation, and be version-controlled within a national laboratory-managed Git repository. Benchmark problems are needed to assess code predictions and performance.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Bridging the time scale in exascale computing of chemical systems (Final Technical Report)

This report summarizes the work carried out with support of the United States Department of Energy under Award DE-SC0019441. The theme of this project was to develop and apply methods that allowed for the acceleration of atomistic calculations, particularly in challenging areas such as multiphase systems, electrified interfaces, uncertainty estimation, and applications requiring chemical accuracy, which tend to be applications where simulation time is severely bottlenecked by the computational time requirements. Much of the focus was on the application of emerging machine-learning methodologies, although a wide range of methodologies were employed. This report has two major sections. The first focuses on the methodological advances themselves. Within this part, we report a number of major advances, a few examples of which are described here. We report the first machine-learning scheme for the acceleration of electronically grand-canonical calculations (that is, those applicable to electrochemistry). We report new methods of performing transfer learning, in which physics-based priors can be used to provide predictions, often with uncertainty estimates, of images well outside of training sets; we also offer ways to fine-tune these transfer-learning models. We provide a new systematic means to generate and apply minimal training data sets to very large (10,000’s of atoms) systems, with only small training sets appropriate for electronic structure. We developed new methodologies to integrate surface vibrations into surface adsorption calculations. We made advances to the applicability of diffusion Monte Carlo methods to allow (learned) force prediction, finite-size error correction, and force-free means of searching for transition states. We integrated machine-learned atomistic predictions into mechanism generation codes. Additionally, we released new software including AmpTorch, a modernized version of our original atomistic machine-learning code Amp. The second part of this report focuses on the scientific applications that accompanied, and were often enabled by, the methodological advances described earlier. A few examples follow, but full details are in the individual chapters of the report. For example, we developed a general theory of phonon-induced friction on molecular adsorbates. We showed fundamentally how solvent influences the adsorption and desorption process and how it differs from the processes typically involved at the solid–gas interface, making aqueous-phase and electrocatalysis different from traditional thermocatalysis. We examined how metal–insulator and magnetic transitions can be probed, and accelerated exciton dynamics via Frenkel Hamiltonian parameters. We showed that the nearsighted force-training approach, developed within this project, can predict both the stability and reactivity of large nanoparticles, and can also lead to insights on catalyst coverage on binding energies and entropies. These applied studies, which generally integrated with our method development, allowed us to push forward the theoretical understanding of several reaction classes.

08 HYDROGEN↗

Pumped Storage Hydropower FAST Commissioning Technical Analysis

This report is designed to address barriers and solutions to modern pumped storage hydropower (PSH) development by establishing baseline project development knowledge, defining key aspects of project development, and identifying opportunities to reduce project timelines, costs, and risks. This report’s scope includes post-licensing activities and excludes factors related to permitting or licensing. The U.S. PSH fleet is composed of 43 projects providing the majority (95%) of utility-scale electricity storage in the US. However, only one new PSH facility has become operational in the past 20 years. Several factors contribute to diminishing PSH growth in the US, including the magnitude of project costs and financing interest during development and construction; the length of time from project investment until project revenue; permitting challenges and construction risks; competition from other storage technologies; and unrecognized energy storage valuation. Although innovative PSH concepts (including underground, small, and modular systems) have been investigated, widespread application has yet to occur. In short, the time, cost, and risk associated with modern PSH development has resulted in limited recent growth in the United States, despite the rising energy storage demand from increased deployment of variable renewable technologies. To address these challenges, the US Department of Energy’s (DOE) Water Power Technologies Office initiated the PSH Furthering Advancements to Shorten Time to (FAST) Commissioning project, aimed at catalyzing new solutions, designs, and strategies to accelerate PSH development. This report uses available data from previous license applications, ongoing project cost data, and other global PSH project information based on a typical closed-loop PSH project.

13 HYDRO ENERGY↗

Design, characterization, and control of the NASA three degree of freedom reaction compensation platform

Increasing research is being done into industrial uses for the microgravity environment aboard orbiting space vehicles. However, there is some concern over the effects of reaction forces produced by moving objects, especially motors, robotic actuators, and astronauts. Reaction forces produced by the movement of these objects may manifest themselves as undesirable accelerations in the space vehicle making the vehicle unusable for microgravity applications. It is desirable to provide compensation for such forces using active means. This paper presents the design and experimental evaluation of the NASA three degree of freedom reaction compensation platform, a system designed to be a testbed for the feasibility of active attenuation of reaction forces caused by moving objects in a microgravity environment. Unique 'linear motors,' which convert electrical current directly into rectilinear force, are used in the platform design. The linear motors induce accelerations of the displacer inertias. These accelerations create reaction forces that may be controlled to counteract disturbance forces introduced to the platform. The stated project goal is to reduce reaction forces by 90 percent, or -20 dB. Description of the system hardware, characterization of the actuators and the composite system, and design of the software safety system and control software are included.

Birkhimer, Craig↗