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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.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

HAWC observations of the acceleration of very-high-energy cosmic rays in the Cygnus Cocoon

We report cosmic rays with energies up to a few PeV are known to be accelerated within the Milky Way. Traditionally, it has been presumed that supernova remnants were the main source of these very-high-energy cosmic rays, but theoretically it is difficult to accelerate protons to PeV energies and observationally there simply is no evidence of the remnants being sources of hadrons with energies above a few tens of TeV. One possible source of protons with those energies is the Galactic Centre region9. Here, we report observations of 1–100 TeV γ rays coming from the ‘Cygnus Cocoon’, which is a superbubble that surrounds a region of massive star formation. These γ rays are likely produced by 10–1,000 TeV freshly accelerated cosmic rays that originate from the enclosed star-forming region Cyg OB2. Until now it was not known that such regions could accelerate particles to these energies. The measured flux likely originates from hadronic interactions. The spectral shape and the emission profile of the Cocoon changes from GeV to TeV energies, which reveals the transport of cosmic particles and historical activity in the superbubble.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Polarized blazar X-rays imply particle acceleration in shocks

Most of the light from blazars, active galactic nuclei with jets of magnetized plasma that point nearly along the line of sight, is produced by high-energy particles, up to around 1 TeV. Although the jets are known to be ultimately powered by a supermassive black hole, how the particles are accelerated to such high energies has been an unanswered question. The process must be related to the magnetic field, which can be probed by observations of the polarization of light from the jets. Measurements of the radio to optical polarization—the only range available until now—probe extended regions of the jet containing particles that left the acceleration site days to years earlier and hence do not directly explore the acceleration mechanism, as could X-ray measurements. Here we report the detection of X-ray polarization from the blazar Markarian 501 (Mrk 501). We measure an X-ray linear polarization degree Π X of around 10%, which is a factor of around 2 higher than the value at optical wavelengths, with a polarization angle parallel to the radio jet. This points to a shock front as the source of particle acceleration and also implies that the plasma becomes increasingly turbulent with distance from the shock.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Generation of focusing ion beams by magnetized electron sheath acceleration

Abstract We present the first 3D fully kinetic simulations of laser driven sheath-based ion acceleration with a kilotesla-level applied magnetic field. The application of a strong magnetic field significantly and beneficially alters sheath based ion acceleration and creates two distinct stages in the acceleration process associated with the time-evolving magnetization of the hot electron sheath. The first stage delivers dramatically enhanced acceleration, and the second reverses the typical outward-directed topology of the sheath electric field into a focusing configuration. The net result is a focusing, magnetic field-directed ion source of multiple species with strongly enhanced energy and number. The predicted improvements in ion source characteristics are desirable for applications and suggest a route to experimentally confirm magnetization-related effects in the high energy density regime. We additionally perform a comparison between 2D and 3D simulation geometry, on which basis we predict the feasibility of observing magnetic field effects under experimentally relevant conditions.

42 ENGINEERING↗
DOE OSTI2021

Electro-optical measurement of intense electric field on a high energy pulsed power accelerator

We describe a direct electro-optical approach to measuring a strong 118 MV/m narrow pulse width (~ 33 ns) electric field in the magnetically insulated transmission line (MITL) of a pulsed power accelerator. To date, this is the highest direct external electric field measured electro-optically in a pulsed power accelerator, and it is between two to three orders of magnitude higher than values reported in comparable high energy scientific experiments. The MITL electric field is one of the most important operating parameters in an accelerator and is critical to understanding the properties of the radiation output. However, accurately measuring these high fields using conventional pulsed power diagnostics is difficult due to the strength of interfering particles and fields. Our approach uses a free-space laser beam with a dielectric crystal sensor that is highly immune to electromagnetic interference and does not require an external calibration. Here we focus on device theory, operating parameters, laboratory and pulsed power accelerator experiments as well as challenges that were overcome in the measurement environment.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Predicting metal–metal interactions. II. Accelerating generalized schemes through physical insights

Operando-computational frameworks that integrate descriptors for catalyst stability within catalyst screening paradigms enable predictions of rates and selectivity on chemically faithful representations of nanoparticles under reaction conditions. These catalyst stability descriptors can be efficiently predicted by density functional theory (DFT)-based models. The alloy stability model, for example, predicts the stability of metal atoms in nanoparticles with site-by-site resolution. In this work, we use physical insights to present accelerated approaches of parameterizing this recently introduced alloy-stability model. These accelerated approaches meld quadratic functions for the energy of metal atoms in terms of the coordination number with linear correlations between model parameters and the cohesive energies of bulk metals. By interpolating across both the coordination number and chemical space, these accelerated approaches shrink the training set size for 12 fcc p- and d-block metals from 204 to as few as 24 DFT calculated total energies without sacrificing the accuracy of our model. We validate the accelerated approaches by predicting adsorption energies of metal atoms on extended surfaces and 147 atom cuboctahedral nanoparticles with mean absolute errors of 0.10 eV and 0.24 eV, respectively. This efficiency boost will enable a rapid and exhaustive exploration of the vast material space of transition metal alloys for catalytic applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Analog architectures for neural network acceleration based on non-volatile memory

Analog hardware accelerators, which perform computation within a dense memory array, have the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. Exploiting the intrinsic computational advantages of memory arrays, however, has proven to be challenging principally due to the overhead imposed by the peripheral circuitry and due to the non-ideal properties of memory devices that play the role of the synapse. We review the existing implementations of these accelerators for deep supervised learning, organizing our discussion around the different levels of the accelerator design hierarchy, with an emphasis on circuits and architecture. We explore and consolidate the various approaches that have been proposed to address the critical challenges faced by analog accelerators, for both neural network inference and training, and highlight the key design trade-offs underlying these techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Machine-learning accelerated geometry optimization in molecular simulation

Geometry optimization is an important part of both computational materials and surface science because it is the path to finding ground state atomic structures and reaction pathways. These properties are used in the estimation of thermodynamic and kinetic properties of molecular and crystal structures. This process is slow at the quantum level of theory because it involves an iterative calculation of forces using quantum chemical codes such as density functional theory (DFT), which are computationally expensive and which limit the speed of the optimization algorithms. It would be highly advantageous to accelerate this process because then one could do either the same amount of work in less time or more work in the same time. Here, we provide a neural network (NN) ensemble based active learning method to accelerate the local geometry optimization for multiple configurations simultaneously. We illustrate the acceleration on several case studies including bare metal surfaces, surfaces with adsorbates, and nudged elastic band for two reactions. In all cases, the accelerated method requires fewer DFT calculations than the standard method. In addition, we provide an Atomic Simulation Environment (ASE)-optimizer Python package to make the usage of the NN ensemble active learning for geometry optimization easier.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Effect of Nozzle Curvature on Supersonic Gas Jets Used in Laser-Plasma Acceleration

Supersonic gas jets produced by converging-diverging (C-D) nozzles are commonly used as targets for laser-plasma acceleration (LPA) experiments. A major point of interest for these targets is the gas density at the region of interaction where the laser ionizes the gas plume to create a plasma, providing the acceleration structure. Tuning the density profiles at this interaction region is crucial to LPA optimization. A "flat-top" density profile is desired at this line of interaction to control laser propagation and high energy electron acceleration, while a short high-density profile is often preferred for acceleration of lower-energy tightly-focused laser-plasma interactions. A particular design parameter of interest is the curvature of the nozzle's diverging section. We examine three nozzle designs with different curvatures: the concave "bell", straight conical and convex "trumpet" nozzles. We demonstrate that, at mm-scale distances from the nozzle exit, the trumpet and straight nozzles, if optimized, produce "flat-top" density profiles whereas the bell nozzle creates focused regions of gas with higher densities. An optimization procedure for the trumpet nozzle is derived and compared to the straight nozzle optimization process. We find that the trumpet nozzle, by providing an extra parameter of control through its curvature, is more versatile for creating flat-top profiles and its optimization procedure is more refined compared to the straight nozzle and the straight nozzle optimization process. Furthermore, we present results for different nozzle designs from computational fluid dynamics (CFD) simulations performed with the program ANSYS Fluent and verify them experimentally using neutral density interferometry.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

High-yield and high-angular-fluence neutron generation from deuterons accelerated by laser-driven collisionless shock

Since a bright collimated neutron source is an essential tool for global security missions and fundamental scientific research. In this paper, we study a compact high-yield and high-angular-fluence neutron source particularly suitable for high-energy neutron applications utilizing the breakup reaction of laser-driven deuterons in a 9 Be converter. The neutron generation scaling from such a reaction is used to guide the choice and optimization of the acceleration process for bulk ions in a low density CD 2 foam. In particular, the collisionless shock acceleration mechanism is exploited with proper choice in the laser and target parameter space to accelerate these ions toward energies above the temperature of the distribution. Particle-in-cell and Monte Carlo simulations are coupled here to investigate this concept and possible adverse effects as well as the contribution from the surface ions accelerated and the optimal converter design. The simulation results indicated that our design can be a practical approach to increase both the neutron yield and angular fluence of laser-driven neutron sources, reaching >10 11 neutron/pulse (or >10 8 neutron/J) and >10 11 neutron/sr (or >10 8 neutron/sr/J) with present-day kJ-class high-power lasers. Such developments will advance fundamental neutron science, high precision radiography, and other global security applications with laser-driven sources.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Theory and simulations of linear and nonlinear two-dimensional Rayleigh–Taylor dynamics with variable acceleration

Interfacial Rayleigh–Taylor mixing is crucial to describing important natural and engineering processes, such as exploding supernovae, laser micromachining, hot spots in inertial confinement fusion, and optical telecommunications. These require the characterization of the time dependence of the driving acceleration. We compare our theoretical formulation based on group theory foundations with interface-capturing numerical simulations for linear and nonlinear two-dimensional Rayleigh–Taylor instabilities in a finite-sized domain with time-varying acceleration over broad ranges of Atwood numbers and acceleration exponents. Detailed corroboration between theory and simulations is provided for this foundational case. Both demonstrate the strong interfacial nature of Rayleigh–Taylor instabilities, which suggests that practical flow fields can be reconstructed from the derived fluid potential using the proposed theory. A robust agreement is also obtained for the early and late-time evolution of the amplitudes of the bubble and spike, which demonstrate that the Rayleigh–Taylor flow can transition to the mixing regime even for a single-mode initial perturbation. Corroboration with experiments of high energy density plasmas motivated by studies of supernovae is also achieved. In addition, a long-standing puzzle in Rayleigh–Taylor dynamics on the interplay between the acceleration, the shear, and the interface morphology in the theory and simulations is resolved by accounting for finite viscosity of the fluids. The characterization of Rayleigh–Taylor instabilities as a highly interfacial phenomenon provides valuable insight into its multiscale nature, which enhances the design and understanding of numerous processes of practical interest.

42 ENGINEERING↗
DOE OSTI2024

Efficient laser-driven proton acceleration from a petawatt contrast-enhanced second harmonic mixed-glass laser system

Efficient laser-driven plasma acceleration of ion beams requires precision control of the target–plasma profile, which is crucial to optimize the laser energy transfer. Along the laser propagation direction, this can be achieved by tailoring the temporal structure of the laser pulse. We show for the first time that frequency-doubling of a short pulse (hundreds-femtosecond range) petawatt-class mixed-glass laser system, which results in temporal intensity contrast enhancement, enables surface and volumetric laser–energy coupling, and the acceleration of proton beams from few-nanometer-thick foil targets. Experimentally, maximum ion energies and laser-to-proton energy conversion efficiencies were found to be both maximized at optimum laser and target conditions manifested when the normalized target density nearly equalizes the normalized laser vector potential, which is in agreement with theory and simulations. These signatures are recognized as a unique indication of the interaction between ultra-intense laser pulses with high temporal intensity contrast and ultra-thin nanometer-scale targets. Transverse modulations of accelerated proton beams in the form of bubble- and ring-like structures measured in the thinnest targets provide additional evidence of volumetric laser-driven particle acceleration regimes and transitional features in ultra-thin foil targets specific to laser–plasma interactions characterized by a high temporal intensity contrast. These results open avenues in the generation of high contrast laser pulses from short-pulse-femtosecond petawatt mixed-glass laser systems and demonstrate the feasibility of this technique for applications requiring high laser intensity contrast with high efficiency.

Physics↗
DOE OSTI2021

Integrating Advanced Modeling and Accelerated Testing for a Modernized Fuel Qualification Paradigm

With the increasing interest in sodium fast reactor technology, as seen by applications to the U.S. Nuclear Regulatory Commission for the OKLO Aurora plant, fuel testing for the TerraPower Traveling Wave Reactor, and the impending construction and startup of the versatile test reactor (VTR), a modernized, accelerated approach to fuel qualification is needed. To guide this effort, a Phenomena Identification Ranking Table–styled analysis was performed for a U-Pu-Zr sodium-free annular fuel system. This analysis evaluated a series of fuel design properties and parameters against their contributions to key fuel performance phenomena. The resulting priority parameters were then reviewed against existing modeling and experimental capabilities to support investigation of the highest-priority parameters. A pathway for qualification was then established using highthroughput, high-volume experiments from MiniFuel and FAST in parallel with advanced physics-based model development. This effort outlines how the first stages of qualification can be reduced from the typical 20+-year development cycle to 5 to 7 years by deploying accelerated irradiation testing platforms. As with any accelerated test, these methods are prototypic in some aspects and less so in others; however, by coupling with advanced fuel performance modeling and simulation capabilities, the larger space of irradiation parameters and material response provided offers advantages for the validation of physics-based models supporting the deployment of novel fuel designs. As a test case, this paper utilizes a proposed Mark II fuel system for the upcoming VTR. Thus, an accelerated qualification method can be tested for the development of MARK II driver fuel so that by the time of VTR startup, lead test assemblies for a Mark II fuel can be initiated.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Accelerating multilevel Markov Chain Monte Carlo using machine learning models

Here, this work presents an efficient approach for accelerating multilevel Markov Chain Monte Carlo (MCMC) sampling for large-scale problems using low-fidelity machine learning models. While conventional techniques for large-scale Bayesian inference often substitute computationally expensive high-fidelity models with machine learning models, thereby introducing approximation errors, our approach offers a computationally efficient alternative by augmenting high-fidelity models with low-fidelity ones within a hierarchical framework. The multilevel approach utilizes the low-fidelity machine learning model (MLM) for inexpensive evaluation of proposed samples thereby improving the acceptance of samples by the high-fidelity model. The hierarchy in our multilevel algorithm is derived from geometric multigrid hierarchy. We utilize an MLM to accelerate the coarse level sampling. Training machine learning model for the coarsest level significantly reduces the computational cost associated with generating training data and training the model. We present an MCMC algorithm to accelerate the coarsest level sampling using MLM and account for the approximation error introduced. We provide theoretical proofs of detailed balance and demonstrate that our multilevel approach constitutes a consistent MCMC algorithm. Additionally, we derive the expression for cost reduction due to machine learning model to facilitate cost analysis of the hierarchical sampling algorithm. Our technique is demonstrated on a standard benchmark inference problem in groundwater flow, where we estimate the probability density of a quantity of interest using a four-level MCMC algorithm. Our proposed algorithm accelerates multilevel sampling by a factor of two while achieving similar accuracy compared to sampling using the standard multilevel algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Online accelerator optimization with a machine learning-based stochastic algorithm

Abstract Online optimization is critical for realizing the design performance of accelerators. Highly efficient stochastic optimization algorithms are needed for many online accelerator optimization problems in order to find the global optimum in the non-linear, coupled parameter space. In this study, we propose to use the multi-generation Gaussian process optimizer for online accelerator optimization and demonstrate that the algorithm is significantly more efficient than other stochastic algorithms that are commonly used in the accelerator community.

Zhang, Zhe (ORCID:0000000281430381)↗
DOE OSTI2022

FINETUNA: fine-tuning accelerated molecular simulations

Abstract Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However, simulation techniques based on first principles, such as density functional theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to Vienna ab initio Simulation Package (VASP) to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

ML-CGRA: An Integrated Compilation Framework to Enable Efficient Machine Learning Acceleration on CGRAs

Coarse-Grained Reconfigurable Arrays (CGRAs) can achieve higher energy-efficiency than general-purpose processors and accelerators or fine-grained reconfigurable devices, while maintaining adaptability to different computational patterns. CGRAs have shown some success as a platform to accelerate machine learning (ML) thanks to their flexibility, which allows them to support new models not considered by fixed accelerators. However, current solutions for CGRAs employ low level instruction-based compiler approaches and lack specialized compilation infrastructures from high-level ML frameworks that could leverage semantic information from the models, limiting the ability to efficiently map them on the recon- figurable substrate. This paper proposes ML-CGRA, an integrated compilation framework based on the MLIR infrastructure that en- ables efficient ML acceleration on CGRAs. ML-CGRA provides an end-to-end solution for mapping ML models on CGRAs that out- performs conventional approaches by 3.15× and 6.02 × on 4×4 and 8×8 CGRAs, respectively.

machine learning↗
DOE OSTI2022

ROAM: A Remotely Operated Accelerator Monitor

Monitoring accelerators in operation is a well known challenge due to the radiation environment. However, there are significant benefits in being able to deploy particular sensors in specific locations of accelerator enclosures for monitoring or troubleshooting purposes. Learning from experience at other labs, we used Commercial Off The Shelf (COTS) components and an open source robot control software framework (ROS) to build a remotely controlled robot platform including a standard suite of instruments such as cameras, LIDAR, and ultrasound, with the ability to incorporate other ad-hoc sensors for specific measurements, such as a Gamma radiation monitor. Special attention was given to the robot's ability to maintain safety in the high risk environment of an accelerator, with multiple failure contingencies in place to ensure collision free operation. Testing was performed to prove the platform's viability and showcase its capability for accelerator monitoring.

Thayer, Thomas C.↗
DOE OSTI2020

Quantitative Prediction of Perovskite Stability Using Accelerated Testing and Machine Learning

PV technologies based on hybrid perovskites offer the potential for reducing solar cell costs, but they are particularly vulnerable to degradation by environmental factors such as moisture, oxygen, and illumination. Commercialization will require not only stable materials and device architectures but also accelerated testing protocols and models that can predict degradation from the accelerated testing data. Here, we report results from in situ photoluminescence (PL) and photoconductivity (PC) measurements during perovskite degradation with simultaneous optical transmittance (Tr) measurements or reflected dark field (DF) imaging. From PL, PC, Tr, and DF, we determine (respectively) the steady-state quasi-Fermi level splitting, the mean effective carrier diffusion length, the extent of conversion of perovskite to higher bandgap degradation products, and the extent of scattering from domains with different orientation or composition, all as a function of time during degradation. Simultaneous measurement of PL-PC-Tr or PL-PC-DF on perovskite absorbers in an environmental chamber over a wide range of humidity, oxygen, temperature, and illumination levels yields a rich data set. We use machine learning to develop a model that accurately predicts (within 10%) the time for the diffusion length to decrease to 85% of its initial value. The model takes the environmental conditions and the first few measurements from the PL-PC-Tr or PL-PC-DF experiment as input. Thus, the model provides a framework to interpret the results of accelerated testing of absorber materials. One of the dominant features in the model of degradation for CH 3 NH 3 PbI 3 is the initial rate at which transmittance increase. For devices with opaque contacts, the pixel-averaged rate of change of the intensity in dark-field images can be used in place of transmittance. Results are also presented on full PV devices with in-situ current-voltage (JV) measurements. Here, the data from simultaneous PL-DF-JV under environmental stresses reveal intimate connection between degradation and shunts and provide a framework to extend accelerated testing to devices. Further, the presentation reveals underlying universal behavior in degradation pathways over a broad range of environmental stresses and perovskite compositions.

14 SOLAR ENERGY↗