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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Position Papers for the ASCR Workshop on Reimagining Codesign

On behalf of the Advanced Scientific Computing Research (ASCR) program in the US Department of Energy (DOE) Office of Science, we are organizing a Workshop on Reimagining Codesign (ReCoDe). Codesign is the process of jointly designing interoperating components of a computing system—in particular: applications, algorithms, system software, programming models, and the hardware on which they run. The goal is to maximize the overall performance, efficiency, and other desirable qualities of the system as a whole. Codesign is a standard methodology in the embedded-systems community, where space, power, and cost constraints are commonly pitted against execution speed for a tightly constrained feature set. Over the last decade, the DOE has invested in codesign efforts to foster the development of exascale computing systems for broad classes of scientific and engineering applications. The ReCoDe workshop hopes to explore how scientific applications of interest to the DOE can be accelerated through close interactions with hardware designers and software-stack developers, in which all components adapt to each other’s requirements and constraints. We want to answer the question of what are the key tools and methodologies for accomplishing codesign in today’s computing landscape, and what will be the highest impact targets for meeting DOE’s emerging mission requirements. This workshop aims to bring together DOE, industry, and academia to identify opportunities to build on past codesign successes and identify new areas that are either emerging or that may need reimagining for the future. We want to continue to find opportunities that can be pursued as a joint effort and continue to break down the traditional customer/vendor dichotomy with true partnerships. From this work, DOE will benefit from increased application performance relative to what stock hardware or existing general-purpose roadmaps can provide, and vendors will benefit from expanding their hardware’s capabilities to address needs they might have not otherwise anticipated and thereby create more widespread interest in their products. The workshop will be structured around a set of breakout sessions, with every attendee expected to participate actively in the discussions. Afterward, workshop attendees—from DOE, industry, and academia—will produce a report for ASCR that summarizes the findings made during the workshop.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

H-GCN: A Graph Convolutional Network Accelerator on Versal ACAP Architecture

Recently Graph Neural Networks (GNNs) have drawn tremendous attentions due to their unique capability to extend the Machine Learning (ML) approaches to broadly defined applications with unstructured data, especially graphs. Comparing with other ML modalities, the acceleration of GNNs is as critical but even more challenging due to the irregularity and heterogeneity from graph typologies that together limit the performance. Existing efforts mainly focus on handling graphs’ irregularity, however, have not studied the heterogeneity. To this end, in this work, we propose H-GCN, a PL-AIE-based hybrid accelerator that leverages the emerging heterogeneity of Xilinx Versal ACAPs to achieve high-performance GNN inference. In particular, H-GCN partitions each graph into three subgraphs based on its inherent heterogeneity and processes them using PL and the newly emerged AIE respectively. To further improve the performance, we explore the sparsity support of AIE and develop an efficient density-aware method to map tiles of SpMM onto the systolic tensor array automatically. Compared with the current state-of-the-art GCN accelerator, HGCN achieves on average 1.5× speedups.

Zhang, Chengming↗
DOE OSTI2021

Acceleration of heavy ions in inverse free electron laser

In conventional linear accelerators, the beam is accelerated with a synchronous harmonic of the radio frequency field where the electric field component is collinear with the beam direction. This approach requires the design of complex accelerating structures, especially for low-energy heavy ions. If the beam motion were sustainably coupled to transverse electromagnetic fields, this could significantly simplify the accelerating structure design, and even allow acceleration with free-space waves. However, despite the long history of the proposed concept for accelerating low-velocity ion beams, it has not found practical application, partially because of the complexity of the technical design. In this paper, we present a practical design approach for this undulator-based accelerator for low-energy heavy-ions, reminiscent of the inverse free electron laser operating principle, but in a different parameter space.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

A review of neutronics and thermal hydraulics–based screening methods applied to accelerated nuclear fuel qualification

This paper reviews the state-of-the-art engineering approach for using thermal hydraulic (TH) and neutronics modeling and simulation (M&S) tools to perform rapid screening studies of novel nuclear fuel concepts within the context of accelerated fuel qualification. Global research efforts have introduced nuclear fuel and material concepts that mark a significant departure from traditional reactor materials. The number of new technologies being considered for development for light water reactors and advanced reactor types has created the need for an accelerated fuel qualification procedure. A key component of this procedure is the rapid identification of the most promising fuel concepts using computational screening studies. Advanced TH and neutronic M&S tools should be leveraged to efficiently determine whether the reactor performance and safety characteristics of a given concept warrant additional studies or whether the concept requires modification or elimination. This paper reviews best practices for performing these TH and neutronics screening studies at various stages during a fuel concept's progression through the qualification procedure. The motivation behind standardizing this approach is to minimize time and resources spent on qualification activities for fuel concepts that could be quickly refined or eliminated from consideration based on their reactor physics and TH characteristics. Adoption of this screening procedure—which focuses primarily on nuclear fuels but may be applicable to other reactor materials—will also help accelerate new material qualification by generating boundary conditions crucial to fuel performance evaluations and highlighting needed areas of separate effects experimentation. This article reviews the motivation behind the introduction of novel nuclear fuel concepts, provides incentive for utilizing TH- and neutronics-based screening studies, describes the screening approach and methodology, and includes discussion on how to interpret screening results to provide recommendations for the continued development of a given concept.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2019

Evaluation of Skyshine from an Accelerator Facility: Dependence on Distance and Angle

Neutron skyshine from Linac Coherent Light Source II 4 GeV electron beam operation at SLAC National Accelerator Laboratory can contribute to prompt radiation exposure to the public at distances far beyond the accelerator tunnel housing. One of the shielding design requirements at SLAC is that the annual dose to a member of the public is no more than 0.05 mSv y -1 . Our work uses Monte Carlo code FLUKA to simulate the generation of neutrons from 4 GeVelectron beam losses on a thick copper target inside a generalized geometry of the Linac Coherent Light Source II Beam Transport Hall accelerator tunnel section. The effective dose from neutron skyshine was characterized as a function of both distance from the tunnel wall (up to 1 km away) and angle relative to the beam direction (between 0° and 180°). Furthermore, this new methodology for evaluating neutron skyshine dose is applicable to high-energy GeV-range electron accelerator facilities both at SLAC and elsewhere.

43 PARTICLE ACCELERATORS↗
DOE OSTI2016

Demonstration of passive plasma lensing of a laser wakefield accelerated electron bunch

We report on the first demonstration of passive all-optical plasma lensing using a two-stage setup. An intense femtosecond laser accelerates electrons in a laser wakefield accelerator (LWFA) to 100 MeV over millimeter length scales. By adding a second gas target behind the initial LWFA stage we introduce a robust and independently tunable plasma lens. We observe a density dependent reduction of the LWFA electron beam divergence from an initial value of 2.3 mrad, down to 1.4 mrad (rms), when the plasma lens is in operation. Such a plasma lens provides a simple and compact approach for divergence reduction well matched to the mm-scale length of the LWFA accelerator. The focusing forces are provided solely by the plasma and driven by the bunch itself only, making this a highly useful and conceptually new approach to electron beam focusing. Possible applications of this lens are not limited to laser plasma accelerators. Since no active driver is needed the passive plasma lens is also suited for high repetition rate focusing of electron bunches. As a result, its understanding is also required for modeling the evolution of the driving particle bunch in particle driven wake field acceleration.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS2020

Artemis Sustained Translational Acceleration Limits: Human Tolerance Evidence from Apollo to ISS

The designers of the next generation of lunar landers may adopt novel, crew-body orientations outside of our flight history or applied to flight durations and environments outside of our experience. Current sustained translational acceleration requirements in NASA-STD-3001 are applicable only to crewmembers in a seated posture and are thus inadequate to address human tolerance in non-seated configurations. Initial designs for the Apollo Lunar Module (LM) included seats for both commander and pilot; however, these were subsequently removed from the vehicle due to mass constraints and a willingness to accept the unknown risks for short-duration missions given the limited human physiologic data at the time. In the years since Apollo, our evidence base has grown immensely. Initial Artemis mission timelines under consideration will be longer than the longest Apollo mission, by a significant margin, with timeframes more analogous to longer Space Shuttle missions. Given the incidence of postflight orthostatic intolerance following shuttle missions, a significant risk may exist for lander design(s) pursuing a standing crew configuration similar to Apollo LM. New sustained translational acceleration limits developed to address this risk are presented herein. These limits were derived from evaluations of Apollo biomedical and flight profile data during lunar descent and ascent operations, Soyuz and Space Shuttle flight profile and post-landing biomedical data, and analogue bed rest post-exposure data on orthostatic intolerance.

James M. Pattarini↗
DOE OSTI2021

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multi-threaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0x and 6.8x on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3x over the multi-thread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗
DOE OSTI2021

Revisiting Huffman Coding: Toward Extreme Performance on Modern GPU Architectures

Today's high-performance computing (HPC) applications are producing vast volumes of data, which are challenging to store and transfer efficiently during the execution, such that data compression is becoming a critical technique to mitigate the storage burden and data movement cost. Huffman coding is arguably the most efficient Entropy coding algorithm in information theory, such that it could be found as a fundamental step in many modern compression algorithms such as DEFLATE. On the other hand, today's HPC applications are more and more relying on the accelerators such as GPU on supercomputers, while Huffman encoding suffers from low throughput on GPUs, resulting in a significant bottleneck in the entire data processing. In this paper, we propose and implement an efficient Huffman encoding approach based on modern GPU architectures, which addresses two key challenges: (1) how to parallelize the entire Huffman encoding algorithm, including codebook construction, and (2) how to fully utilize the high memory-bandwidth feature of modern GPU architectures. The detailed contribution is fourfold. (1) We develop an efficient parallel codebook construction on GPUs that scales effectively with the number of input symbols. (2) We propose a novel reduction based encoding scheme that can efficiently merge the codewords on GPUs. (3) We optimize the overall GPU performance by leveraging the state-of-the-art CUDA APIs such as Cooperative Groups. (4) We evaluate our Huffman encoder thoroughly using six real-world application datasets on two advanced GPUs and compare with our implemented multithreaded Huffman encoder. Experiments show that our solution can improve the encoding throughput by up to 5.0× and 6.8× on NVIDIA RTX 5000 and V100, respectively, over the state-of-the-art GPU Huffman encoder, and by up to 3.3× over the multithread encoder on two 28-core Xeon Platinum 8280 CPUs.

Tian, Jiannan↗
DOE OSTI2025

Revealing the loss mechanisms of a 3D superconducting microwave cavity for use in a dark matter search

Superconducting microwave cavities have found applications in many areas including quantum computing, particle accelerators, and dark matter searches. Their extremely high quality factors translate to very narrow bandwidth, which makes them key components of sensitive detectors. In this study, we aim to understand the loss mechanisms of an aluminium cavity, with a fundamental mode frequency of about 2.5123 GHz, and how they change as the cavity material transitions from the superconducting to normal state. We found that at temperatures not much lower than the transition temperature $T_c$, losses are dominated by quasiparticle excitations and are well described by the Bardeen–Cooper–Schrieffer theory. The exponential decrease of the quasiparticle density below $T_c$ results in a 1000-fold increase of the quality factor, as well as a shift of the resonance frequency due to the change of the kinetic inductance of the superconductor. At very low temperatures, losses due to two-level systems begin to dominate giving a peak in the quality factor of about 2.76 x 10 7 at 130 mK. Understanding the loss mechanisms is invaluable, as the working temperature of the cavity may vary during operation regardless of its application.

Physics - Physics of elementary particles and fiel↗
DOE OSTI2016

WarpIV: In situ visualization and analysis of ion accelerator simulations

The generation of short pulses of ion beams through the interaction of an intense laser with a plasma sheath offers the possibility of compact and cheaper ion sources for many applications--from fast ignition and radiography of dense targets to hadron therapy and injection into conventional accelerators. To enable the efficient analysis of large-scale, high-fidelity particle accelerator simulations using the Warp simulation suite, the authors introduce the Warp In situ Visualization Toolkit (WarpIV). WarpIV integrates state-of-the-art in situ visualization and analysis using VisIt with Warp, supports management and control of complex in situ visualization and analysis workflows, and implements integrated analytics to facilitate query- and feature-based data analytics and efficient large-scale data analysis. WarpIV enables for the first time distributed parallel, in situ visualization of the full simulation data using high-performance compute resources as the data is being generated by Warp. The authors describe the application of WarpIV to study and compare large 2D and 3D ion accelerator simulations, demonstrating significant differences in the acceleration process in 2D and 3D simulations. WarpIV is available to the public via https://bitbucket.org/berkeleylab/warpiv. The Warp In situ Visualization Toolkit (WarpIV) supports large-scale, parallel, in situ visualization and analysis and facilitates query- and feature-based analytics, enabling for the first time high-performance analysis of large-scale, high-fidelity particle accelerator simulations while the data is being generated by the Warp simulation suite. Furthermore, this supplemental material https://extras.computer.org/extra/mcg2016030022s1.pdf provides more details regarding the memory profiling and optimization and the Yee grid recentering optimization results discussed in the main article.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Global characterization of a laser-generated neutron source

Laser-driven neutron sources are routinely produced by the interaction of laser-accelerated protons with a converter. They present complementary characteristics to those of conventional accelerator-based neutron sources (e.g. short pulse durations, enabling novel applications like radiography). We present here results from an experiment aimed at performing a global characterization of the neutrons produced using the Titan laser at the Jupiter Laser Facility (Livermore, USA), where protons were accelerated from 23 $\mathrm {\mu }$ m thick plastic targets and directed onto a LiF converter to produce neutrons. For this purpose, several diagnostics were used to measure these neutron emissions, such as CR-39, activation foils, time-of-flight detectors and direct measurement of $^7{\rm Be}$ residual activity in the LiF converters. The use of these different, independently operating diagnostics enables comparison of the various measurements performed to provide a robust characterization. These measurements led to a neutron yield of $2.0\times 10^{9}$ neutrons per shot with a modest angular dependence, close to that simulated.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

ORCHA: A performance portability system for extreme heterogeneity

Heterogeneity is the prevalent trend in the rapidly evolving high-performance computing (HPC) landscape in both hardware and application software. The diversity in hardware platforms, currently comprising various accelerators and a future possibility of specializable chiplets, poses a significant challenge for scientific software developers aiming to harness optimal performance across different computing platforms while maintaining the quality of solutions when their applications are simultaneously growing more complex. Code synthesis and code generation can provide mechanisms to mitigate this challenge. We have developed a divide and conquer approach where different aspects of performance are handled by different stand-alone tools that are interfaced with the application through generated code. This portability system, ORCHA, enables users to configure and orchestrate their computations among available resources on a platform by specifying a high-level recipe, thereby permitting a many-to-many paradigm where each recipe results in a different variant of the application. The core design goal is to let users decide the application’s hardware mapping and orchestration by editing only the high-level recipe—without modifying the maintained source code or binding the application to a particular runtime system. Tools in ORCHA distribution are: CG-Kit for translating the recipe into an execution graph; Milhoja to execute the graph by orchestrating data and task movement among hardware resources; and Macroprocessor that enables users to define their own code-shorthand for higher composability and easier management of code variants. Additionally, the design of ORCHA permits tools to work in a plug-and-play mode where the application can build and run without CG-Kit and Milhoja, and either tool can be swapped out for other tools with similar capabilities by modifying the code generation portion of ORCHA. In this paper, we describe the design of ORCHA and the role that code-generation plays in isolating applications from tools. We demonstrate the breadth of configurations ORCHA enables with a case study in which an application configuration is realized on three distinct hardware mappings—a GPU-centric, a CPU/GPU balanced, and a CPU/GPU concurrent layouts by using different recipes.

Lee, Youngjun↗
DOE OSTI2021

Metamaterial-Inspired Vacuum Electron Devices and Accelerators

Metamaterials (MTMs) are structured materials with subwavelength features that can be engineered to have some unique properties not found in nature, such as negative refractive index, reversed Doppler effect, and reversed Cherenkov radiation. Based on these novel MTMs, several important research groups have made great attempts to develop novel MTM-inspired vacuum electron devices (VEDs) and accelerators. Just as solid-state power devices are innovated by incessant emerging of new semiconductor materials, VEDs can also be inspired by MTMs to have very remarkable advantages, such as smaller size, higher power, higher efficiency, and/or larger gain relative to conventional VEDs, such as traveling-wave tubes, backward-wave oscillators, and klystrons. Similarly, relative to conventional accelerators, MTM-inspired accelerators have obvious advantages, such as smaller size and higher accelerating gradient. Furthermore, MTM-inspired devices have promising applications in areas such as radar, communication, electronic warfare, microwave heating, and imaging.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Pulsar’s Application in Energy Systems: Review of Current Status, Challenges, and Opportunities

To accelerate progress toward the realization of advanced energy systems, this review explores the potential of pulsar technology to create a more stable, economical, and environmentally friendly energy infrastructure. Pulsars, with their precise and reliable timing characteristics, have emerged as a promising tool for enhancing energy systems. This review begins by examining the development history of pulsar technology, shedding light on its evolution and the milestones achieved. It then provides a comprehensive summary of the current state of research, highlighting recent advancements and breakthroughs in this field. It also explores transformative pulsar applications in energy systems, including improved grid stability, advanced energy synchronization, and efficient energy storage management. However, implementing pulsar-related technologies presents significant technical, economic, and operational challenges. This review examines these hurdles and proposes strategies to overcome them, emphasizing the need for innovation, interdisciplinary collaboration, and supportive policies to fully integrate pulsar technologies into sustainable energy systems.

application↗
DOE OSTI2015

The use of aluminum nitride to improve Aluminum-26 Accelerator Mass Spectrometry measurements and production of Radioactive Ion Beams

We present results and discuss the use of aluminum nitride as a promising source material for Accelerator Mass Spectrometry (AMS) and Radioactive Ion Beams (RIBs) science applications of 26 Al isotopes. The measurement of 26 Al in geological samples by AMS is typically conducted on Al 2 O 3 targets. However, Al 2 O 3 is not an ideal source material because it does not form a prolific beam of Al- required for measuring low-levels of 26 Al. Multiple samples of aluminum oxide (Al 2 O 3 ), aluminum nitride (AlN), mixed Al 2 O 3 –AlN as well as aluminum fluoride (AlF 3 ) were tested and compared using the ion source test facility and the stable ion beam (SIB) injector platform at the 25-MV tandem electrostatic accelerator at Oak Ridge National Laboratory. Negative ion currents of atomic and molecular aluminum were examined for each source material. It was found that pure AlN targets produced substantially higher beam currents than the other materials and that there was some dependence on the exposure of AlN to air. The applicability of using AlN as a source material for geological samples was explored by preparing quartz samples as Al 2 O 3 and converting them to AlN using a carbothermal reduction technique, which involved reducing the Al 2 O 3 with graphite powder at 1600 °C within a nitrogen atmosphere. The quartz material was successfully converted to AlN. Thus far, AlN proves to be a promising source material and could lead towards increasing the sensitivity of low-level 26 Al AMS measurements. The potential of using AlN as a source material for nuclear physics is also very promising by placing 26 AlN directly into a source to produce more intense radioactive beams of 26 Al.

Janzen, Meghan S.↗
DOE OSTI2019

Reliable and explainable machine-learning methods for accelerated material discovery

Abstract Despite ML’s impressive performance in commercial applications, several unique challenges exist when applying ML in materials science applications. In such a context, the contributions of this work are twofold. First, we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material data. Specifically, we show that with imbalanced data, standard methods for assessing quality of ML models break down and lead to misleading conclusions. Furthermore, we find that the model’s own confidence score cannot be trusted and model introspection methods (using simpler models) do not help as they result in loss of predictive performance (reliability-explainability trade-off). Second, to overcome these challenges, we propose a general-purpose explainable and reliable machine-learning framework. Specifically, we propose a generic pipeline that employs an ensemble of simpler models to reliably predict material properties. We also propose a transfer learning technique and show that the performance loss due to models’ simplicity can be overcome by exploiting correlations among different material properties. A new evaluation metric and a trust score to better quantify the confidence in the predictions are also proposed. To improve the interpretability, we add a rationale generator component to our framework which provides both model-level and decision-level explanations. Finally, we demonstrate the versatility of our technique on two applications: (1) predicting properties of crystalline compounds and (2) identifying potentially stable solar cell materials. We also point to some outstanding issues yet to be resolved for a successful application of ML in material science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Union: An Automatic Workload Manager for Accelerating Network Simulation

With the rapid growth of the machine learning applications, the workloads of future HPC systems are anticipated to be a mix of scientific simulation, big data analytics, and machine learning applications. Simulation is a great research vehicle to understand the performance implications of co-running scientific applications with big data and machine learning workloads on large-scale systems. In this paper, we present Union, a workload manager that provides an automatic framework to facilitate hybrid workload simulation in CODES. Furthermore, we use Union, along with CODES, to investigate various hybrid workloads composed of traditional simulation applications and emerging learning applications on two dragonfly systems. The experiment results show that both message latency and communication time are important performance metrics to evaluate network interference. Network interference on HPC applications is more reflected by the message latency variation, whereas ML application performance depends more on the communication time.

heterogeneous workloads↗