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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 487 records · Page 27
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Rotational and reflectional equivariant convolutional neural network for data-limited applications: Multiphase flow demonstration

This article deals with approximating steady-state particle-resolved fluid flow around a fixed particle of interest under the influence of randomly distributed stationary particles in a dispersed multiphase setup using convolutional neural network (CNN). The considered problem involves rotational symmetry about the mean velocity (streamwise) direction. Thus, this work enforces this symmetry using SE(3)-equivariant, special Euclidean group of dimension 3, CNN architecture, which is translation and three-dimensional rotation equivariant. This study mainly explores the generalization capabilities and benefits of a SE(3)-equivariant network. Accurate synthetic flow fields for Reynolds number and particle volume fraction combinations spanning over a range of [86.22, 172.96] and [0.11, 0.45], respectively, are produced with careful application of symmetry-aware data-driven approach.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Transfer learning driven design optimization for inertial confinement fusion

Transfer learning is a promising approach to create predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations and then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previously, this technique has been used to create predictive models of Omega [Humbird et al., IEEE Trans. Plasma Sci. 48, 61–70 (2019)] and NIF [Humbird et al., Phys. Plasmas 28, 042709 (2021); Kustowski et al., Mach. Learn. 3, 015035 (2022)] inertial confinement fusion (ICF) experiments that are more accurate than simulations alone. Here, in this work, we conduct a transfer learning driven hypothetical ICF campaign in which the goal is to maximize experimental neutron yield via Bayesian optimization. The transfer learning model achieves yields within 5% of the maximum achievable yield in a modest-sized design space in fewer than 20 experiments. Furthermore, we demonstrate that this method is more efficient at optimizing designs than traditional model calibration techniques commonly employed in ICF design. Such an approach to ICF design could enable robust optimization of experimental performance under uncertainty.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Probing itinerant carrier dynamics at the diamond surface using single nitrogen vacancy centers

Color centers in diamond are widely explored for applications in quantum sensing, computing, and networking. Their optical, spin, and charge properties have extensively been studied, while their interactions with itinerant carriers are relatively unexplored. Here, we show that NV centers situated 10 ± 5 nm of the diamond surface can be converted to the neutral charge state via hole capture. By measuring the hole capture rate, we extract the capture cross section, which is suppressed by proximity to the diamond surface. The distance dependence is consistent with a carrier diffusion model, indicating that the itinerant carrier lifetime can be long, even at the diamond surface. Measuring dynamics of near-surface NV centers offers a tool for characterizing the diamond surface and investigating charge transport in diamond devices.

Physics↗
DOE OSTI2023

Linear shaped-charge jet optimization using machine learning methods

Linear shaped charges are used to focus energy into rapidly creating a deep linear incision. The general design of a shaped charge involves detonating a confined mass of high explosive (HE) with a metal-lined concave cavity on one side to produce a high velocity jet for the purpose of striking and penetrating a given material target. This jetting effect occurs due to the interaction of the detonation wave with the cavity geometry, which produces an unstable fluid phenomenon known as the Richtmyer–Meshkov instability and results in the rapid growth of a long narrow jet. We apply machine learning and optimization methods to hydrodynamics simulations of linear shaped charges to improve the simulated jet characteristics. The designs that we propose and investigate in this work generally involve modifying the behavior of the detonation waves prior to interaction with the liner material. These designs include the placement of multiple detonators and the use of metal inclusions within the HE. In conclusion, we are able to produce a linear shaped-charge design with a higher penetration depth than the baseline case that we consider and accomplish this using the same amount of or less HE.

36 MATERIALS SCIENCE↗
DOE OSTI2024

3D reconstruction of an inertial-confinement fusion implosion with neural networks using multiple heterogeneous data sources

3D asymmetries are major degradation mechanisms in inertial-confinement fusion implosions at the National Ignition Facility (NIF). These asymmetries can be diagnosed and reconstructed with the neutron imaging system (NIS) on three lines of sight around the NIF target chamber. Conventional tomographic reconstructions are used to reconstruct the 3D morphology of the implosion using NIS [Volegov et al., J. Appl. Phys. 127, 083301 (2020)], but the problem is ill-posed with only three imaging lines of sight. Asymmetries can also be diagnosed with the real-time neutron activation diagnostics (RTNAD) and the neutron time-of-flight (nToF) suite. Since the NIS, RTNAD, and nToF each sample a different part of the implosion using different physical principles, we propose that it is possible to overcome the limitations of too few imaging lines of sight by performing 3D reconstructions that combine information from all three heterogeneous data sources. This work presents a new machine learning-based reconstruction technique to do just this. By using a simple physics model and group of neural networks to map 3D morphologies to data, this technique can easily account for data of multiple different types. A simple proof-of-principle is presented, demonstrating that this technique can accurately reconstruct a hot-spot shape using synthetic primary neutron images and a hot-spot velocity vector. In particular, the hot-spot’s asymmetry, quantified as spherical harmonic coefficients, is reconstructed to within ±4% of the radius in 90% of test cases. In the future, this technique will be applied to actual NIS, RTNAD, and nToF data to better understand 3D asymmetries at the NIF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

A physics-constrained deep learning treatment of runaway electron dynamics

An adjoint formulation leveraging a physics-informed neural network (PINN) is employed to advance the density moment of a runaway electron (RE) distribution forward in time. A distinguishing feature of this approach is that once the adjoint problem is solved, its solution can be used to project the RE density forward in time for an arbitrary initial momentum space distribution of REs. Furthermore, by employing a PINN, a parametric solution to the adjoint problem can be learned. Thus, once trained, this adjoint-deep learning framework is able to efficiently project the RE density forward in time across various plasma conditions while still including a fully kinetic description of RE dynamics. As an example application, the temporal evolution of the density of primary electrons is studied, with particular emphasis on evaluating the decay of a RE population when below threshold. Predictions from the adjoint-deep learning framework are found to be in good agreement with a traditional relativistic electron Fokker–Planck solver, for several distinct initial conditions, and across an array of physics parameters. Once trained, the PINN thus provides a means of generating RE density time histories with exceptionally low online execution time.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

An intercomparison of wall fluxes in a turbulent thermal convection chamber: Direct numerical simulations and wall-modeled large-eddy simulations enhanced by machine learning

Thermal convection in a closed chamber is driven by a warm bottom, a cold top, and side walls at various temperatures. Although wall fluxes are the source of convection energy, accurately modeling these fluxes (i.e., the wall model) is challenging. In large-eddy simulations (LESs), many wall models are traditionally derived from the canonical boundary layer, which may be unsuitable for thermal convection bounded by both horizontal and vertical walls. This study conducts a model intercomparison of dry convection in a cubic-meter chamber using three direct numerical simulations (DNSs) and four LESs with different wall models. The LESs employ traditional wall models, a new wall model employing physics-aware neural networks, and a refined grid near the walls. The experiment involves four cases with varying sidewall temperatures. Our results show that LESs capture the main flow features and the trends of mean fluxes. The physics-aware neural networks and refined wall grids can improve the temporally averaged local fluxes when the large-scale circulation has a preferred direction. Even without the local improvement of wall fluxes, the LES flow quantities (temperature and velocities) can still largely match those in DNSs, provided the mean flux largely matches the DNSs. Additionally, DNSs reveal that a variation in corner treatments has minimal impacts on the flow quantities away from corners. Finally, LESs underestimate the mean fluxes of the entire wall due to their inability to resolve corner regions, but their mean flux away from the corner can better match DNS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

WAVEMOTH-FAST SPHERICAL HARMONIC TRANSFORMS BY BUTTERFLY MATRIX COMPRESSION

We present Wavemoth, an experimental open source code for computing scalar spherical harmonic transforms (SHTs). Such transforms are ubiquitous in astronomical data analysis. Our code performs substantially better than existing publicly available codes owing to improvements on two fronts. First, the computational core is made more efficient by using small amounts of pre-computed data, as well as paying attention to CPU instruction pipelining and cache usage. Second, Wavemoth makes use of a fast and numerically stable algorithm based on compressing a set of linear operators in a pre-computation step. The resulting SHT scales as O(L{sup 2}log{sup 2} L) for the resolution range of practical interest, where L denotes the spherical harmonic truncation degree. For low- and medium-range resolutions, Wavemoth tends to be twice as fast as libpsht, which is the current state-of-the-art implementation for the HEALPix grid. At the resolution of the Planck experiment, L {approx} 4000, Wavemoth is between three and six times faster than libpsht, depending on the computer architecture and the required precision. Because of the experimental nature of the project, only spherical harmonic synthesis is currently supported, although adding support for spherical harmonic analysis should be trivial.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2012

Upgrade of the CMS event builder

The Data Acquisition system of the Compact Muon Solenoid experiment at CERN assembles events at a rate of 100 kHz, transporting event data at an aggregate throughput of 100 GB/s. By the time the LHC restarts after the 2013/14 shut-down, the current computing and networking infrastructure will have reached the end of their lifetime. This paper presents design studies for an upgrade of the CMS event builder based on advanced networking technologies such as 10/40 Gb/s Ethernet and Infiniband. The results of performance measurements with small-scale test setups are shown.

Bauer, G.↗
DOE OSTI2022

Effect of localization on photoluminescence and zero-field splitting of silicon color centers

The study of defect centers in silicon has been recently reinvigorated by their potential applications in optical quantum information processing. A number of silicon defect centers emit single photons in the telecommunication O-band, making them promising building blocks for quantum networks between computing nodes. The two-carbon G-center, self-interstitial W-center, and spin-1/2 T-center are the most intensively studied silicon defect centers, yet despite this, there is no consensus on the precise configurations of defect atoms in these centers, and their electronic structures remain ambiguous. Here, in this work, we employ ab initio density functional theory to characterize these defect centers, providing insight into the relaxed structures, band structures, and photoluminescence spectra, which are compared to experimental results. Motivation is provided for how these properties are intimately related to the localization of electronic states in the defect centers. In particular, we present the calculation of the zero-field splitting for the excited triplet state of the G-center defect as the structure is linearly interpolated from the A-configuration to the B-configuration, showing a sudden increase in the magnitude of the D zz component of the zero-field-splitting tensor. By performing projections onto the local orbital states of the defect, we analyze this transition in terms of the symmetry and bonding character of the G-center defect, which sheds light on its potential application as a spin-photon interface.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

Gamma-ray emission of hot astrophysical plasma

Very hot plasmas with ion temperature exceeding 10 10 K can be formed in certain astrophysical environments. The distinct radiation signature of such plasmas is the γ-ray emission dominated by the prompt deexcitation nuclear lines and π 0 -decay γ rays. Using a large nuclear reaction network, we compute the time evolution of the chemical composition of such hot plasmas and their γ-ray line emissivity. At higher energies, we provide simple but accurate analytical presentations for the π 0 -meson production rate and the corresponding π 0 → 2γ emissivity derived for the Maxwellian distribution of protons. Furthermore, we discuss the impact of the possible deviation of the high energy tail of the particle distribution function from the “nominal” Maxwellian distribution on the plasma γ-ray emissivity.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Streaming readout for next generation electron scattering experiments

Current and future experiments at the high-intensity frontier are expected to produce an enormous amount of data that needs to be collected and stored for offline analysis. Thanks to the continuous progress in computing and networking technology, it is now possible to replace the standard ‘triggered’ data acquisition systems with a new, simplified and outperforming scheme. ‘Streaming readout’ (SRO) DAQ aims to replace the hardware-based trigger with a much more powerful and flexible software-based one, that considers the whole detector information for efficient real-time data tagging and selection. Considering the crucial role of DAQ in an experiment, validation with on-field tests is required to demonstrate SRO performance. In this paper, we report results of the on-beam validation of the Jefferson Lab SRO framework. In this work, we exposed different detectors (PbWO-based electromagnetic calorimeters and a plastic scintillator hodoscope) to the Hall-D electron-positron secondary beam and to the Hall-B production electron beam, with increasingly complex experimental conditions. By comparing the data collected with the SRO system against the traditional DAQ, we demonstrate that the SRO performs as expected. Furthermore, we provide evidence of its superiority in implementing sophisticated AI-supported algorithms for real-time data analysis and reconstruction.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

HotSpots: Failure Cascades on Heterogeneous Critical Infrastructure Networks

Critical Infrastructure Systems such as transportation, water and power grid systems are vital to our national security, economy, and public safety. Recent events, like the 2012 hurricane Sandy, show how the interdependencies among different CI networks lead to catastrophic failures among the whole system. Hence, analyzing these CI networks, and modeling failure cascades on them becomes a very important problem. However, traditional models either do not take multiple CIs or the dynamics of the system into account, or model it simplistically. In this paper, we study this problem using a heterogeneous network viewpoint. We first construct heterogeneous CI networks with multiple components using national-level datasets. Then we study novel failure maximization problems on these networks, to compute critical nodes in such systems. We then provide HotSpots, a scalable and effective algorithm for these problems, based on careful transformations. Finally, we conduct extensive experiments on real CIS data from multiple US states, and show that our method HotSpots outperforms non-trivial baselines, gives meaningful results and that our approach gives immediate benefits in providing situational-awareness during large-scale failures.

Chen, Liangzhe↗
DOE OSTI2017

A Pattern Language for High-Performance Computing Resilience

High-performance computing systems (HPC) provide powerful capabilities for modeling, simulation, and data analytics for a broad class of computational problems. They enable extreme performance of the order of quadrillion floating-point arithmetic calculations per second by aggregating the power of millions of compute, memory, networking and storage components. With the rapidly growing scale and complexity of HPC systems for achieving even greater performance, ensuring their reliable operation in the face of system degradations and failures is a critical challenge. System fault events often lead the scientific applications to produce incorrect results, or may even cause their untimely termination. The sheer number of components in modern extreme-scale HPC systems and the complex interactions and dependencies among the hardware and software components, the applications, and the physical environment makes the design of practical solutions that support fault resilience a complex undertaking. To manage this complexity, we developed a methodology for designing HPC resilience solutions using design patterns. We codified the well-known techniques for handling faults, errors and failures that have been devised, applied and improved upon over the past three decades in the form of design patterns. In this paper, we present a pattern language to enable a structured approach to the development of HPC resilience solutions. The pattern language reveals the relations among the resilience patterns and provides the means to explore alternative techniques for handling a specific fault model that may have different efficiency and complexity characteristics. Using the pattern language enables the design and implementation of comprehensive resilience solutions as a set of interconnected resilience patterns that can be instantiated across layers of the system stack.

Hukerikar, Saurabh↗
DOE OSTI2022

Ultra-broadband quadrature squeezing with thin-film lithium niobate nanophotonics

Squeezed light is a key quantum resource that enables quantum advantages for sensing, networking, and computing applications. The scalable generation and manipulation of squeezed light with integrated platforms are highly desired for the development of quantum technology with continuous variables. In this Letter, we demonstrate squeezed light generation with thin-film lithium niobate integrated photonics. Parametric down-conversion is realized with quasi-phase matching using ferroelectric domain engineering. With sub-wavelength mode confinement, efficient nonlinear processes can be observed with single-pass configuration. We measure 0.56 ± 0.09 dB quadrature squeezing (∼2.6 dB inferred on-chip). The single-pass configuration further enables the generation of squeezed light with large spectral bandwidth up to 7 THz. This work represents a significant step towards the on-chip implementation of continuous-variable quantum information processing.

Chen, Pao-Kang↗
DOE OSTI2011

Secure Data Transfer Guidance for Industrial Control and SCADA Systems

This document was developed to provide guidance for the implementation of secure data transfer in a complex computational infrastructure representative of the electric power and oil and natural gas enterprises and the control systems they implement. For the past 20 years the cyber security community has focused on preventative measures intended to keep systems secure by providing a hard outer shell that is difficult to penetrate. Over time, the hard exterior, soft interior focus changed to focus on defense-in-depth adding multiple layers of protection, introducing intrusion detection systems, more effective incident response and cleanup, and many other security measures. Despite much larger expenditures and more layers of defense, successful attacks have only increased in number and severity. Consequently, it is time to re-focus the conventional approach to cyber security. While it is still important to implement measures to keep intruders out, a new protection paradigm is warranted that is aimed at discovering attempted or real compromises as early as possible. Put simply, organizations should take as fact that they have been, are now, or will be compromised. These compromises may be intended to steal information for financial gain as in the theft of intellectual property or credentials that lead to the theft of financial resources, or to lie silent until instructed to cause physical or electronic damage and/or denial of services. This change in outlook has been recently confirmed by the National Security Agency [19]. The discovery of attempted and actual compromises requires an increased focus on monitoring events by manual and/or automated log monitoring, detecting unauthorized changes to a system's hardware and/or software, detecting intrusions, and/or discovering the exfiltration of sensitive information and/or attempts to send inappropriate commands to ICS/SCADA (Industrial Control System/Supervisory Control And Data Acquisition) systems.

03 NATURAL GAS↗
DOE OSTI2011

Performance Analysis of and Tool Support for Transactional Memory on BG/Q

Martin Schindewolf worked during his internship at the Lawrence Livermore National Laboratory (LLNL) under the guidance of Martin Schulz at the Computer Science Group of the Center for Applied Scientific Computing. We studied the performance of the TM subsystem of BG/Q as well as researched the possibilities for tool support for TM. To study the performance, we run CLOMP-TM. CLOMP-TM is a benchmark designed for the purpose to quantify the overhead of OpenMP and compare different synchronization primitives. To advance CLOMP-TM, we added Message Passing Interface (MPI) routines for a hybrid parallelization. This enables to run multiple MPI tasks, each running OpenMP, on one node. With these enhancements, a beneficial MPI task to OpenMP thread ratio is determined. Further, the synchronization primitives are ranked as a function of the application characteristics. To demonstrate the usefulness of these results, we investigate a real Monte Carlo simulation called Monte Carlo Benchmark (MCB). Applying the lessons learned yields the best task to thread ratio. Further, we were able to tune the synchronization by transactifying the MCB. Further, we develop tools that capture the performance of the TM run time system and present it to the application's developer. The performance of the TM run time system relies on the built-in statistics. These tools use the Blue Gene Performance Monitoring (BGPM) interface to correlate the statistics from the TM run time system with performance counter values. This combination provides detailed insights in the run time behavior of the application and enables to track down the cause of degraded performance. Further, one tool has been implemented that separates the performance counters in three categories: Successful Speculation, Unsuccessful Speculation and No Speculation . All of the tools are crafted around IBM's xlc compiler for C and C++ and have been run and tested on a Q32 early access system.

97 MATHEMATICS AND COMPUTING↗