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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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DOE OSTI2024

Secondary Beams at Jefferson Laboratory

This thesis explores the possibility of producing secondary beams at the Thomas Jefferson National Accelerator Facility (TJNAF). Their possible applications in future experiments are studied using advanced Monte Carlo simulations with state-of-the-art software. In Jefferson Lab’s Hall-A, the interaction of the high-current O(100) µA, medium-energy O(11) GeV electron beam produced by the Continuous Electron Beams Accelerator Facility (CEBAF) with the Beam-Dump (BD) generates a substantial shower of standard model particles. This interaction creates a high-intensity secondary muon and neutrino beam and hypothetical Light Dark Matter (LDM) particles. Additionally, with the proposed Compact Photon Source (CPS), Hall-D could offer the possibility of producing an intense muon beam alongside a photon beam.

Fulci, Antonino↗

DOE OSTI2023

Batched Sparse Linear Algebra (Final Report for Subcontract B648960)

This report finalizes design specifications for developing batched kernels for small tensor operations for unassembled matrix-free iterative solvers, batched solvers for partially assembled operators, and batched solvers with support for various sparse formats. The outcome of the project milestones is a set of interfaces to Batched Sparse LA solvers running on hardware accelerators for use in ECP Libraries and Applications. It is part of the development of sparse batched kernels, solvers/preconditioners as well as creating interoperability in xSDK libraries with sparse and dense batched functions to benefit ECP applications. The participants included representatives from ECP libraries (not limited to the xSDK project), applications, and vendors (AMD, Intel, and NVIDIA). Batched sparse linear algebra solvers form the new frontier for algorithmic development and performance engineering. Many applications (ECP and non-ECP alike) require simultaneous solutions of small linear systems of equations that are structurally sparse. To move towards high hardware utilization, it is important to provide these applications with appropriate interfaces to efficient batched sparse solvers running on modern hardware accelerators. We present interface designs in use by HPC software libraries supporting batched sparse linear algebra and the development of sparse batched kernel codes for solvers and preconditioners. We also address the potential interoperability opportunities to keep the software portable between the major hardware accelerators from AMD, Intel, and NVIDIA. The presented interface specifications includes batched band, sparse iterative, and sparse direct solvers. This report summarizes progress in Kokkos Kernels and the xSDK libraries MAGMA, Ginkgo, hypre, SUNDIALS, and SuperLU_dist.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2016

Utilizing GPUs to Accelerate Turbomachinery CFD Codes

GPU computing has established itself as a way to accelerate parallel codes in the high performance computing world. This work focuses on speeding up APNASA, a legacy CFD code used at NASA Glenn Research Center, while also drawing conclusions about the nature of GPU computing and the requirements to make GPGPU worthwhile on legacy codes. Rewriting and restructuring of the source code was avoided to limit the introduction of new bugs. The code was profiled and investigated for parallelization potential, then OpenACC directives were used to indicate parallel parts of the code. The use of OpenACC directives was not able to reduce the runtime of APNASA on either the NVIDIA Tesla discrete graphics card, or the AMD accelerated processing unit. Additionally, it was found that in order to justify the use of GPGPU, the amount of parallel work being done within a kernel would have to greatly exceed the work being done by any one portion of the APNASA code. It was determined that in order for an application like APNASA to be accelerated on the GPU, it should not be modular in nature, and the parallel portions of the code must contain a large portion of the code's computation time.

computer programming↗

DOE OSTI2024

Dynamic Scaling Analysis of Accelerated Irradiation Testing on Additive Manufacturing Materials by Positron Annihilation

The timely applications of Additive Manufacturing (AM) materials in nuclear environments require accelerated irradiation tests, mainly ion irradiation to enable rapid prototyping. Low dose ion irradiation would cause sub-nanostructure changes by generation of lattice defects, vacancies, vacancy clusters and voids and void swelling caused by cellular dislocations. Positron Annihilation Lifetime (PAL), a novel technology, sensitive towards sub-nanostructure morphology with high accuracy (about 10-7 vacancy per atom), supported by Transition Electron Microscope (TEM) would be applied to identify the type and total size of the defects. The subsequent PAL measurements and TEM surface studies would be followed by PAL analysis that includes sophisticated trapping model. The PAS results would become an input to dynamic scaling analysis (that predicts radiation effects from low dose studies for high dose effects), which incorporate mean-field theory model. The final effect is an in-depth understanding of the microstructure evolution of AM materials under ion irradiation which can be extrapolated to the studies of neutron irradiation, since ion-irradiation takes less time and do not cause the irradiation hazard. The working hypothesis is that PAL technology, that have excellent sensitivity to low-defect concentration would help to identify ion-induced material damage on the atomic and nano-scale level, which then could be extrapolated to understand the neutron damage better.

accelerated irradiation testing↗

DOE OSTI2021

Using diglycolamides to improve rare-earth element separation

The family of 17 elements known as the “rare earth elements” are difficult to produce economically. Despite this, they are described as “critical raw materials” because of their unique properties and their various technological applications. This means their use is accelerating globally. Separation of these elements is a fundamental challenge, but this does not daunt Dr Santa Jansone-Popova and her team at Oak Ridge National Laboratory. In recovering praseodymium from neodymium using a variety of ligands known as diglycolamides, she has been demonstrating potentially game-changing advances in liquid-liquid separations that could realise more of these in-demand materials.

Jansone-Popova, Santa↗

DOE OSTI2020

Illinois Accelerator Research Center

Developing breakthroughs in accelerator science and translating them into applications for the nation’s health, wealth and security.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Characterizing the acceleration time of laser-driven ion acceleration with data-informed neural networks

Peak ion energy is an important figure-of-merit in short-pulse, laser-driven ion acceleration and is dependent on an associated acceleration time. Standard metrics for these quantities depend on analytical results such as the self-similar fluid model or empirical models based on relatively small experimental and simulation datasets. In this work we attempt to use a data-informed neural network (NN) as a surrogate model for a large ensemble of PIC simulations to investigate an effective acceleration time. We explore the application of a stacked convolutional and recurrent NN architecture for improved regression by incorporating the time dependencies of the data into the training process. Of particular note is how pretraining a network on lower fidelity data, e.g. 1D analytical results, greatly improves the network's ability to learn more complex, higher fidelity data. Finally, the dependency of the acceleration time on various laser and plasma parameters is explored.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS1993

The role of magnetic loops in particle acceleration at nearly perpendicular shocks

The acceleration of superthermal ions is investigated when a planar shock that is on average nearly perpendicular propagates through a plasma in which the magnetic field is the superposition of a constant uniform component plus a random field of transverse hydromagnetic fluctuations. The importance of the broadband nature of the transverse magnetic fluctuations in mediating ion acceleration at nearly perpendicular shocks is pointed out. Specifically, the fluctuations are composed of short-wavelength components which scatter ions in pitch angle and long-wavelength components which are responsible for a spatial meandering of field lines about the mean field. At nearly perpendicular shocks the field line meandering produces a distribution of transient loops along the shock. As an application of this model, the acceleration of a superthermal monoenergetic population of seed protons at a perpendicular shock is investigated by integrating along the exact phase-space orbits.

Decker, R. B.↗

DOE OSTI2024

Producing circular field harmonics inside elliptic magnet apertures with superconducting canted-cosine-theta coils

Superconducting magnets with noncircular aperture are desired for accelerators and many other high-field applications. This paper presents new methods for the analytic design of elliptic bore superconducting accelerator magnets. Part 1 of this work shares the derivation of current to field relations between a sheet current density on an elliptic cylinder and the magnetic field harmonics inside the aperture. This result is explored in the general context of elliptic bore magnet design with relevant scaling laws compared between elliptic and circular bore magnets. In part 2, this approach is applied to the specific geometry of canted-cosine-theta (CCT) accelerator magnets, enabling analytic winding design for single or mixed circular harmonics within elliptic aperture CCT magnets. Published by the American Physical Society 2024

43 PARTICLE ACCELERATORS↗

NASA NTRS1978

A new experimental method for the accelerated characterization of composite materials

The use of composite materials for a variety of practical structural applications is presented and the need for an accelerated characterization procedure is assessed. A new experimental and analytical method is presented which allows the prediction of long term properties from short term tests. Some preliminary experimental results are presented.

Yeow, Y. T.↗

DOE OSTI2020

Shaped Beams from Diamond Field-Emitter Array Cathodes

Diamond field-emitter arrays (DFEAs) are arrays of diamond pyramids with exquisitely sharp tips and micrometer-scale bases that produce high current densities. These arrays can be fabricated in arbitrary shapes, ranging from single tips to many millions of tips, so that they produce an inherently shaped electron beam. Each tip emits a modest current, but the large dense array can produce many Amps. We are investigating these cathodes for use in dielectric wakefield accelerators; however, they may also be applicable to vacuum microwave tubes. Recently, shaped beam production and transport have been demonstrated in the 1.3-GHz RF gun at the Argonne Cathode Test Stand at the Argonne National Laboratory. The charge was measured on a Faraday cup and the beam imaged on a YAG screen with peak electric field gradients on the cathode ranging from 12 to 35 MV/m. Three cathode geometries were tested: one 1-mm equilateral triangle with 7-mu m base pyramids and 10-mu m pitch, one 1-mm equilateral triangle with 10-mu m base and 25-mu m pitch, and one sparse 5 x 5 square array with 20-mu m base and 400-mu m pitch. The two triangular arrays emitted 35 nC in an RF macropulse at 35 MV/m and 13-nC charge at 27 MV/m, respectively, while the sparse array emitted 0.060-nC charge at 15 MV/m. This article presents the results of the triangular array experiments, including damage due to breakdown in the RF gun and initial models of tip-to-tip shielding.

Cathodes↗

NASA NTRS1999

Giant Magnetoresistive (GMR) Sensor Microelectromechanical System (MEMS) Device

The measurement of acceleration has been accomplished using several technologies in high-reliability applications such as guidance control, detonation, and shock/vibration measurement. Electromechanical, piezoelectric, piezoresistive, and capacitive acceleration sensors are available and the literature pertinent to giant magnetoresistive sensors (GMR) for the above applications are scanty.

acceleration MEMS GMR giant magnetoresistive senso↗

DOE OSTI2023

Reinforcement learning for real-time process control in high-temperature superconductor manufacturing

With high efficiency and low energy loss, high-temperature superconductors (HTS) have demonstrated their profound applications in various fields, such as medical imaging, transportation, accelerators, microwave devices, and power systems. The high-field applications of HTS tapes have raised the demand for producing cost-effective tapes with long lengths in superconductor manufacturing. However, achieving the uniform and enhanced performance of a long HTS tape is challenging due to the unstable growth conditions in the manufacturing process. Although it is confirmed that the process parameters during the advanced metal organic chemical vapor deposition (A-MOCVD) process influence the uniformity of the produced HTS tapes, the high-dimensional process parameter signals and their complicated interactions make it difficult to develop an effective control policy. In this paper, we propose a local measure for the uniformity of HTS tapes to provide instant feedback for our control policy. Then, we model the manufacturing of HTS tapes as a Markov decision process (MDP) with continuous state and action spaces to assess the instant reward in real time in our feedback control model. As our MDP involves continuous and high-dimensional state and action spaces, a neural fitted Q-iteration (NFQ) algorithm is adopted to solve the MDP with artificial neural network (ANN) function approximation. The collinearity of process parameters can restrict our capability of adjusting the process parameters, which is addressed by the principal component analysis (PCA) in our method. The control policy adjusts the PCA of process parameters using the NFQ algorithm. In conclusion, based on our case studies on real A-MOCVD dataset, the obtained control policy increases the average uniformity of tapes by 5.6% and performs especially well on sample HTS tapes with a low uniformity.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Advancing the utility of Data flow accelerators: Kokkos backend for Next Silicon

Next Silicon provides a novel reconfigurable accelerator, which is inherently a high-risk, early exploration effort. Higher readiness level for running NNSA mini-apps and initial mission applications on a data-flow accelerator Enable and demonstrate a new Kokkos back-end that directly drives an underlying dataflow accelerator

Garcia de Gonzalo, Simon Pedro [Sandia National La↗

NASA NTRS2002

Experimental Methods in Reduced-gravity Soldering Research

The National Center for Microgravity Research, NASA Glenn Research Center, and NASA Johnson Space Center are conducting an experimental program to explore the influence of reduced gravity environments on the soldering process. An improved understanding of the effects of the acceleration environment is important to application of soldering during current and future human space missions. Solder joint characteristics that are being considered include solder fillet geometry, porosity, and microstructural features. Both through-hole and surface mounted devices are being investigated. This paper focuses on the experimental methodology employed in this project and the results of macroscopic sample examination. The specific soldering process, sample configurations, materials, and equipment were selected to be consistent with those currently on-orbit. Other apparatus was incorporated to meet requirements imposed by operation onboard NASA's KC-135 research aircraft and instrumentation was provided to monitor both the atmospheric and acceleration environments. The contingent of test operators was selected to include both highly skilled technicians and less skilled individuals to provide a population cross-section that would be representative of the skill mix that might be encountered in space mission crews.

Pettegrew, Richard D.↗

NASA NTRS2024

EdgeCortix SAKURA-I Machine-Learning, PCIe Accelerator SEE Heavy Ion Test Report

To enable autonomy in space, machine-learning and computer vision applications become invaluable for sensor processing. However, these algorithms are computationally complex and unfeasible for many embedded central processing units (CPUs) and usually require external coprocessors, such as graphics processing units (GPUs) or accelerators specific to the application, including application specific integrated circuits (ASICs). In power-constrained systems, GPUs tend to consume more power than is acceptable (>40W), so lower-power accelerators have shown promise to provide the performance needed under spacecraft constraints. For radiation engineers, developing methodologies that can properly test CPUs, GPUs, and accelerators, and enable comparisons between them remains a necessary complication to solve as the devices become more complex. The methodology in this test aims to be a start in developing a baseline single-event effect (SEE) test for client-device machine learning accelerators. This category of devices do not host their own operating system. This testing campaign is a continuation of a previous 200 MeV proton test performed in January 2024. This report covers two heavy ion tests of the SAKURA-I card: one in April 2024, and one in June 2024. Additional data was needed after the April test due to ion-range issues experienced at higher linear-energy transfers (LETs). These range issues are described in more detail in Section 8. This experiment characterizes SEEs and data error susceptibility of the EdgeCortix SAKURA-I machine-learning accelerator under heavy ions. The device was monitored for single event upsets (SEUs) and single event functional interrupts (SEFIs) at the Lawrence Berkeley National Laboratory’s 88-inch cyclotron. The SAKURA-I board accelerates machine-learning inference applications on a host computer through a PCIex16 connection. For the purposes of devising an end to end automated analysis workflow for this experiment, the YOLO-V5 and SSD300 objection-detection models, and the ResNet-50, EfficientNet, and MobileNetV2 image classification models were used as a representative suite of analytical machine-learning models.

Seth S Roffe↗

DOE OSTI2022

Design and demonstration of an economical SRF structure for Continuous Wave (CW), high-energy, Megawatt-class beams

Due to its very high RF-to-beam efficiency, superconducting radiofrequency (SRF) cavities have become the technology of choice for the construction of large linear accelerators (linacs) for basic science applications (examples, ESS and PIP-II). The technology is also attractive for high volume industrial applications such as electron irradiation processing of wastewater and flue gas. However, SRF’s current reliance on liquid helium for cryogenic cooling introduces infrastructural and safety challenges for use in industrial settings. Cryocooler conduction-cooling is a recently developed novel technique for operating SRF cavities without requiring liquid helium. This project aims for technical design and economic assessment of a medium energy, high average power e-beam linac based on the conduction-cooled SRF technique for high volume irradiation treatment of wastewater. The goals of this project are (1) to design a medium energy (10 MeV), high average power (1 MW) electron beam accelerator around the cryocooler conduction-cooled SRF cavity technology, (2) demonstrate the required accelerating voltage on a prototype cavity, and (3) build a cost-model to evaluate capital and operating expense of the accelerator.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Mixed-precision numerics in scientific applications: survey and perspectives

The explosive demand for artificial intelligence (AI) workloads has led to a significant increase in silicon area dedicated to lower-precision computations on recent high-performance computing hardware designs. However, mixed-precision capabilities, which can achieve performance improvements of up to 8x compared to double-precision in extreme compute-intensive workloads, remain largely untapped in most scientific applications. A growing number of efforts have shown that mixed-precision algorithmic innovations can deliver superior performance without sacrificing accuracy. These developments should prompt computational scientists to seriously consider whether their scientific modeling and simulation applications could benefit from the acceleration offered by new hardware and mixed-precision algorithms. In this survey, we (1) review progress across diverse scientific domains—fluid dynamics, weather and climate, quantum chemistry, and computational genomics—that have begun adopting mixed-precision strategies; (2) examine state-of-the-art algorithmic techniques such as iterative refinement, splitting and emulation schemes, and adaptive precision solvers; (3) assess their implications for accuracy, performance, and resource utilization; and (4) survey the emerging software ecosystem that enables mixed-precision methods at scale. We conclude with perspectives and recommendations on cross-cutting opportunities, domain-specific challenges, and the role of co-design between application scientists, numerical analysts, and computer scientists. Collectively, this survey underscores that mixed-precision numerics can reshape computational science by aligning algorithms with the evolving landscape of hardware capabilities.

Graphics processing units↗