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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

pnnl/memgaze

MemGaze is a memory analysis toolset that combines high-resolution trace analysis and low overhead measurement, both with respect to time and space. It provides high-resolution by collecting world-level memory access traces, where the highest resolution supported is back-to-back sequences. It achieves low-overhead in space and time by leveraging sampling and various methods of hardware support for collecting traces. Its post-mortem trace processing provides multiresolution analysis for locations vs. operations; accesses vs. spatio-temporal reuse, and reuse (distance, rate, volume) vs. access patterns.

Central, PNNL Developer↗
DOE OSTI2024

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 σ point-source depth of g = 24.3, r = 23 . 9 , i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of − 0 . 0013 , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

High-yield growth kinetics and spatial mapping of single-walled carbon nanotube forests at wafer scale

Emerging commercial applications of vertically aligned, single-walled carbon nanotube (SWCNT) “forests” require synthesis that minimizes nanotube diameter while maximizing number density across substrate areas exceeding centimeter scale. To address this need, we synthesized SWCNT forests on full silicon wafers with notable reproducibility and uniformity, and co-optimized growth for small diameters and high densities across large areas to access new territory in this 3D parameter space. We mapped the spatial uniformity of key structural features using Raman microscopy, synchrotron X-ray scattering, and Rutherford backscattering spectrometry. Low C 2 H 2 flux over sub-nm Fe/Mo catalysts produced small-diameter SWCNTs (2.1 nm) at high number densities (2.26 × 10 12 cm -2 ) on wafers up to 6 in. Although removing Mo resulted in larger SWCNT diameters and lower densities (<0.7 × 10 12 cm -2 ), mass conversion rates from C 2 H 2 to SWCNT product were high and remarkably invariant for catalyst compositions and densities (i.e., 47.7% or 1.30 × 10 6 % g-catalyst -1 on 4-in. wafers). These carbon conversion efficiencies far exceed typical benchtop reactors and are on par with the best reported literature values. Our detailed elucidation of correlations among structural characteristics within this resource-efficient process is expected to guide future scale-up efforts of SWCNT forest growth beyond wafer scale.

36 MATERIALS SCIENCE↗
DOE OSTI2024

High-entropy engineering of the crystal and electronic structures in a Dirac material

Dirac and Weyl semimetals are a central topic of contemporary condensed matter physics, and the discovery of new compounds with Dirac/Weyl electronic states is crucial to the advancement of topological materials and quantum technologies. Here we show a widely applicable strategy that uses high configuration entropy to engineer relativistic electronic states. We take the A MnSb 2 ( A = Ba, Sr, Ca, Eu, and Yb) Dirac material family as an example and demonstrate that mixing of Ba, Sr, Ca, Eu and Yb at the A site generates the compound (Ba 0.38 Sr 0.14 Ca 0.16 Eu 0.16 Yb 0.16 )MnSb 2 (denoted as A 5 MnSb 2 ), giving access to a polar structure with a space group that is not present in any of the parent compounds. A 5 MnSb 2 is an entropy-stabilized phase that preserves its linear band dispersion despite considerable lattice disorder. Although both A 5 MnSb 2 and A MnSb 2 have quasi-two-dimensional crystal structures, the two-dimensional Dirac states in the pristine A MnSb 2 evolve into a highly anisotropic quasi-three-dimensional Dirac state triggered by local structure distortions in the high-entropy phase, which is revealed by Shubnikov–de Haas oscillations measurements.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Utilizing distributed acoustic sensing and ocean bottom fiber optic cables for submarine structural characterization

The sparsity of permanent seismic instrumentation in marine environments often limits the availability of subsea information on geohazards, including active fault systems, in both time and space. One sensing resource that provides observational access to the seafloor environment are existing networks of ocean bottom fiber optic cables; these cables, coupled to modern distributed acoustic sensing (DAS) systems, can provide dense arrays of broadband seismic observations capable of recording both seismic events and the ambient noise wavefield. Here, we report a marine DAS application which demonstrates the strength and limitation of this new technique on submarine structural characterization. Based on ambient noise DAS records on a 20 km section of a fiber optic cable offshore of Moss Landing, CA, in Monterey Bay, we extract Scholte waves from DAS ambient noise records using interferometry techniques and invert the resulting multimodal dispersion curves to recover a high resolution 2D shear-wave velocity image of the near seafloor sediments. We show for the first time that the migration of coherently scattered Scholte waves observed on DAS records can provide an approach for resolving sharp lateral contrasts in subsurface properties, particularly shallow faults and depositional features near the seafloor. Our results provide improved constraints on shallow submarine features in Monterey Bay, including fault zones and paleo-channel deposits, thus highlighting one of many possible geophysical uses of the marine cable network.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Operator evolution from the similarity renormalization group and the Magnus expansion

The Magnus expansion is an efficient alternative to solving similarity renormalization group (SRG) flow equations with high-order, memory-intensive ordinary differential equation solvers. The numerical simplifications it offers for operator evolution are particularly valuable for in-medium SRG calculations, though challenges remain for difficult problems involving intruder states. Here we test the Magnus approach in an analogous but more accessible situation, which is the free-space SRG treatment of the spurious bound states arising from a leading-order chiral effective field theory (EFT) potential with very high cutoffs. In this work, we show that the Magnus expansion passes these tests and then use the investigations as a springboard to address various aspects of operator evolution that have renewed relevance in the context of the scale and scheme dependence of nuclear processes. These aspects include SRG operator flow with band- versus block-diagonal generators, universality for chiral EFT Hamiltonians and associated operators with different regularization schemes, and the impact of factorization arising from scale separation. Implications for short-range correlations physics and the possibilities for reconciling high- and low-resolution treatments of nuclear structure and reactions are discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Efficient I/O for Neural Network Training with Compressed Data

FanStore is a shared object store that enables efficient and scalable neural network training on supercomputers. By providing a global cache layer on node-local burst buffers using a compressed representation, it significantly enhances the processing capability of deep learning (DL) applications on existing hardware. In addition, FanStore allows POSIX-compliant file access to the compressed data in user space. We investigate the tradeoff between runtime overhead and data compression ratio using real-world datasets and applications, and propose a compressor selection algorithm to maximize storage capacity given performance constraints. We consider both asynchronous (i.e., with prefetching) and synchronous I/O strategies, and propose mechanisms for selecting compressors for both approaches. Using FanStore, the same storage hardware can host 2-13x more data for example applications without significant runtime overhead. Empirically, our experiments show that FanStore scales to 512 compute nodes with near linear performance scalability.

Zhang, Zhao↗
DOE OSTI2026

Lithographic crystallinity regulation in additive fabrication of thermoplastics (CRAFT)

For semicrystalline polyolefin thermoplastics, the balance between interconnected ordered crystalline and disordered amorphous regions is paramount to their performance and processability. However, contemporary manufacturing strategies, from injection molding to three-dimensional (3D) printing, result in monolithic objects, unable to spatially encode crystallinity. We develop a light-based approach for fabricating mechanically robust polyolefin thermoplastics with microscopic control over crystallinity in 3D space. Light dosage governs polymer stereochemistry giving access to a continuum of materials, from strong rigid plastics, such as high-density polyethylene, to more extensible materials akin to low-density polyethylene, all at the flick of a switch. Leveraging this finding in lithographic grayscale 3D printing enables rapid multimaterial fabrication with voxel-level control over optical and mechanical properties, opening avenues in information storage, soft robotics, and energy damping.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Dtc Commercialization Software Package

This code is the complete software and firmware components supporting DTC model radios H2 and BluSDR6. This software package contains all the hardware boot up code/config files(BSP), user space Linux code (Web, Network, MAC (media access control) & drivers), the field programable gate array HDL (hardware description language) code and the build environment to compile and organize these components together to work in the aforementioned radios. Additional details of these components are as follows: • Hardware support components o Board support package and configuration files o uBoot • Linux Components: o The web components include the user interface for setup, configuration, and status components of the system. o Vulture code configures the radio’s IP network, configures radio parameters and runs the MAC layer of the radio. • The Field Programmable Gate Array HDL contains hardware drivers, interface logic to go between the software to the physical layer and the radio hardware as well as the logic for the physical layer of the radio. • Build environment includes compilers and config files that compile and organize all the other components to be able to be run on the radios.

Loera, Jose [Idaho National Laboratory (INL), Idah↗
DOE OSTI2020

Reversed Field Pinch Research in MST (Final Technical Report)

This is the Final Technical Report for the cooperative agreement DE-FC02-05ER54814 titled “Reversed Field Pinch Research in MST” spanning the funding period 4/1/2005 to 12/14/2019. It summarizes key results from MST research, major publications and presentations, and the mentoring of graduate students and postdocs. The Madison Symmetric Torus (MST) is unique within the U.S. and world fusion plasma program. As one of just five operating reversed field pinch (RFP) experiments, and the only one in the U.S., MST has had an important role in advancing the fundamental understanding of the RFP plasma configuration. The MST research program targets topics that intersect with all three goals. While MST provides a unique opportunity to advance the science and fusion potential of the RFP, as a cousin to the tokamak and stellarator plasma configurations the RFP is a valuable partner in establishing validated predictive capability for fusion science more generally. Specific combinations of the major variables in toroidal confinement, like magnetic field strength, plasma current, and shaping, define the different configurations. By exploring adjacent regions in this major variable parameter space, the RFP exposes dependencies not otherwise accessible in the tokamak and stellarator. This diversity enlarges the arena for scientific discovery, both for fusion and plasma physics. The basic science emphasis for MST research has been the self-organizing behavior of RFP plasmas. This inspired MST’s participation in the NSF Physics Frontier Center for Magnetic Self-Organization in Laboratory and Astrophysical Plasmas (CMSO), which ran from 2004-2016 and motivated a substantial fraction of MST’s research goals and capabilities. By all measures the MST Program has been remarkably successful in publications, community engagement, and mentoring of young scientists. While the DOE-FES’s proof-of-principle program for the RFP now languishes through the cessation of this cooperative agreement, operation of MST continues as one of two devices included in the new Wisconsin Plasma Physics Laboratory (WiPPL) basic science user facility. The scope of present research for MST in WiPPL is narrowed to the basic science associated with the RFP magnetic configuration and the advancement of specific fusion topics, which now includes studies of disruptions in tokamak plasmas. The world’s effort in RFP fusion research continues, despite a lack of U.S. federal support to investigate the RFP configuration for fusion application, most notably through a major upgrade to the RFX-mod2 facility in Italy [1] and the recent construction of the KTX facility at USTC in China. First plasmas for RFX-mod2 are anticipated in 2021.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Userspace Squash Filesystem for Launching Linux Containers on HPC Systems [Thesis]

The demand for user defined software stacks (UDSS) has been increasing in the high-performance computing (HPC) community. Container technology has become popular due to the flexibility and isolation it provides to HPC users. Container images must be available to all nodes involved for use in HPC and can be distributed to compute nodes in a variety of ways. A common method for container image distribution is to simply copy the container image to memory on each compute node, which can be time consuming at scale, and uses valuable memory on each node. The kernel mounted squash filesystem (squashfs) has proven fast and efficient for this task but requires root-level access. This paper will show a user space mounted squashfs is an efficient and secure solution for container image distribution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications

Memory design space exploration methods study memory systems’ performances and limitations before implementation. The computer memory design space has grown exponentially because of the enormous growth of memory types, memory controllers, and application software. Computer simulators are commonly used for memory design space exploration. However, complex memory simulations take an enormous amount of time. Hence, in this paper, we proposed a machine learning-based design space exploration method for dynamic random-access memory and non-volatile memory systems. We applied our method to the CosmoGAN and LeNet applications to predict the following six memory response parameters: (i) bandwidth, (ii) power, (iii) average latency, (iv) average total latency, (v) memory reads, and (vi) memory writes. Our experimental results show that machine learning models can predict memory response parameter values faster than simulations. We used support vector machine, random forest, and gradient boosting machine learning models. We observed that the support vector machine provides better performance for bandwidth, average latency, and average total latency. The random forest model works better for memory reads and writes. The gradient boosting model provides superior prediction performance for power. We provide a detailed discussion on learning curve characteristics, error analysis, and memory type recommendation.

Hasan, S M Shamimul↗
DOE OSTI2020

Investigation of Polyurethane and Fire-Retardant Foams as a Radiological Contamination Fixatives - 20202

Fixation of radiological contamination in decommissioning and deactivation can reduce worker risk, and mitigate potentially hazardous conditions; however, nearly every marketed contamination fixative has been found to be flammable, a significant concern in radiological facilities. Coupled with this, industry fixatives are normally used as a thin coating which can present problems when attempting to stabilize irregular void spaces or areas that are difficult to access and ensure full coverage. To this end, Savannah River National Laboratory (SRNL), in conjunction with Florida International University (FIU), have begun investigating the applicability of polyurethane based foaming materials, which will expand to fill a given asymmetric volume and provide fire retardancy to ensure no release of contamination in the event of a facility fire. Commercial foams undergo exothermic reactions resulting in gaseous release that subsequently causes the curing foam material to expand. Heat generation during curing can potentially compromise rubber parts of an enclosure, a significant concern in nuclear facilities. To mitigate large body heat generation, SRNL has also examined multiple layering of the material to ensure compatibility of the curing material with a previously cured body of the same material. Environmental studies were also completed in order to determine the influence on polyurethane based foams curing process. These studies showed that nearly half of the materials tested delaminated from a cured body of the same material, a property not discussed in commercially available documentation on the products. Of note was the compatibility of the foaming material with the substrate, depending on the type material that the foam was cured in/on, significant delamination could occur. Mechanical testing via tensile pull tester, dynamic mechanical analysis (DMA), and thermogravimetric analysis (TGA) was also performed. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Traversing large graphs on GPUs with unified memory

Due to the limited capacity of GPU memory, the majority of prior work on graph applications on GPUs has been restricted to graphs of modest sizes that fit in memory. Recent hardware and software advances make it possible to address much larger host memory transparently as a part of a feature known as unified virtual memory. While accessing host memory over an interconnect is understandably slower, the problem space has not been sufficiently explored in the context of a challenging workload with low computational intensity and an irregular data access pattern such as graph traversal. We analyse the performance of breadth first search (BFS) for several large graphs in the context of unified memory and identify the key factors that contribute to slowdowns. Next, we propose a lightweight offline graph reordering algorithm, HALO (Harmonic Locality Ordering), that can be used as a pre-processing step for static graphs. HALO yields speedups of 1.5x-1.9x over baseline in subsequent traversals. Our method specifically aims to cover large directed real world graphs in addition to undirected graphs whereas prior methods only account for the latter. Additionally, we demonstrate ties between the locality ordering problem and graph compression and show that prior methods from graph compression such as recursive graph bisection can be suitably adapted to this problem.

Gera, Prasun↗
DOE OSTI2025

An Open-Source Framework for Rapid Validation of Scientific ASICs

Spacely is an open-source framework for the post-silicon validation of analog, digital, and mixed-signal ASICs (Application-Specific Integrated Circuits) which maximizes the reuse of hardware and software, reducing the time taken to achieve meaningful test results. Spacely specifically addresses the needs of small, flexible ASIC design teams commonly found in academia or research institutions which benefit most from sharing the overhead of test stand creation between many unique ASIC designs. Spacely is a set of software, firmware, and design practices. It targets two primary hardware platforms (NI-PXI and Caribou) as well as offering extensible support for bench instruments. Spacely provides a high-level Python interface to all test hardware for accessibility, while also giving more sophisticated teams the opportunity to integrate custom test firmware. The design principles of Spacely are presented in brief. Current documentation is available at https://github.com/SpacelyProject/spacely-docs.

Quinn, Adam [Fermilab]↗
DOE OSTI2024

An Open-Source Framework for Rapid Validation of Scientific ASICs

Spacely is an open-source framework for the post-silicon validation of analog, digital, and mixed-signal ASICs (Application-Specific Integrated Circuits) which maximizes the reuse of hardware and software, reducing the time taken to achieve meaningful test results. Spacely specifically addresses the needs of small, flexible ASIC design teams commonly found in academia or research institutions which benefit most from sharing the overhead of test stand creation between many unique ASIC designs. Spacely is a set of software, firmware, and design practices. It targets two primary hardware platforms (NI-PXI and Caribou) as well as offering extensible support for bench instruments. Spacely provides a high-level Python interface to all test hardware for accessibility, while also giving more sophisticated teams the opportunity to integrate custom test firmware. The design principles of Spacely are presented, along with a demonstrative example of using Spacely to test a pixel detector readout ASIC.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Comparing light-front quantization with instant-time quantization

Here in this paper we compare light-front quantization and instant-time quantization both at the level of operators and at the level of their Feynman diagram matrix elements. At the level of operators light-front quantization and instant-time quantization lead to equal light-front time commutation (or anticommutation) relations that appear to be quite different from equal instant-time commutation (or anticommutation) relations. Despite this we show that at unequal times instant-time and light-front commutation (or anticommutation) relations actually can be transformed into each other, with it only being the restriction to equal times that makes the commutation (or anticommutation) relations appear to be so different. While our results are valid for both bosons and fermions, for fermions there are subtleties associated with tip of the light cone contributions that need to be taken care of. At the level of Feynman diagrams we show for non-vacuum Feynman diagrams that the pole terms in four-dimensional light-front Feynman diagrams reproduce the widely used three-dimensional light-front on-shell Hamiltonian Fock space formulation in which the light-front energy and light-front momentum are on shell. Moreover, we show that the contributions of pole terms in non-vacuum instant-time and non-vacuum light-front Feynman diagrams are equal. However, because of circle at infinity contributions we show that this equivalence of pole terms fails for four-dimensional light-front vacuum tadpole diagrams. Then, and precisely because of these circle at infinity contributions, we show that light-front vacuum tadpole diagrams are not only nonzero, they quite remarkably are actually equal to the pure pole term instant-time vacuum tadpole diagrams. Light-front vacuum diagrams are not correctly describable by the on-shell Hamiltonian formalism, and thus not by the closely related infinite momentum frame prescription either. Thus for the light-front vacuum sector we must use the off-shell Feynman formalism as it contains information that is not accessible in the on-shell Hamiltonian Fock space approach. We show that light-front quantization is intrinsically nonlocal, and that for fermions this nonlocality is present in Ward identities. One can project fermion spinors into so-called good and bad components, and both of these components contribute in Ward identities. Central to our analysis is that the transformation from instant-time coordinates and fields to light-front coordinates and fields is a unitary, spacetime-dependent translation. Consequently, not only are instant-time quantization and light-front quantization equivalent, because of general coordinate invariance they are unitarily equivalent.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

The Data Foundry: Secure Collaboration for the Geothermal Industry: Preprint

The Data Foundry provides secure, cloud-based storage and universal access to digital information, enabling the greater geothermal industry to collaborate seamlessly with the Department of Energy (DOE), national labs, universities, and private organizations. Originally developed to support the EGS Collab project, the Data Foundry has been expanded to support FORGE, EDGE, and other DOE-funded projects, some collaborative, some private, by providing each project with a secure space and the ability to fine-tune individual access controls. In response to user feedback, it now also features improved integration with DOE’s Geothermal Data Repository (GDR), to provide a clear and convenient pathway from collaboration to publication, and to register collaborative data projects with well-known data registries like Data.gov and the National Geothermal Data System (NGDS). This paper will explore how recent concerns raised by data-centric, proprietary projects have informed development on the Data Foundry and highlight improvements designed to streamline workflows, improve access control, and promote the timely dissemination of information to the geothermal industry.

Data Foundry↗