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Results for “compact data structures”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A comparative study of multi-material data structures for computational physics applications

The data structures used to represent the multi-material state of a computational physics application can have a significant impact on its performance. Here, we look at efficient data structures for applications where there may be many materials overall, but where most computational cells contain only one or a few of these materials. We develop simple performance models for selecting possible data structures and programming patterns. We verify the analytic performance models with a small test program of the representative cases. We discuss the impact of these techniques and analysis for multi-material physics applications, which are also applicable to a wide range of sparse computational data structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

High-dose temperature-dependent neutron irradiation effects on the optical transmission and dimensional stability of amorphous fused silica

The primary concern for implementing amorphous fused silica (a-SiO 2 ) fiber optic sensors in a nuclear environment is the radiation-induced attenuation (RIA) of the light signal due to the formation of radiation-induced color centers. In addition, Bragg grating sensors drift under irradiation due to radiation-induced compaction of the a-SiO 2 structure. This work provides new data regarding RIA and radiation-induced compaction of a-SiO 2 samples irradiated to a fast neutron fluence of 2.4 × 10 21 n/cm 2 at temperatures of 95, 298, and 688 °C. Results show that RIA may be approaching saturation for the range of photon energies evaluated in this paper and that the hydroxyl content has a significant impact on RIA when the irradiation temperature is increased to 688 °C. Furthermore, a model was developed for predicting radiation-induced compaction, and the resulting signal drift for Bragg grating sensors, as a function of neutron fluence and temperature.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Experimental Observations of the Topology of Convolutional Neural Network Activations

Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures. Deep neural networks (DNNs) learn millions of parameters associated with a series of transformations defined by the model architecture resulting in high-dimensional, difficult to interpret internal representations of input data. As DNNs become more ubiquitous across multiple sectors of our society, there is increasing recognition that mathematical methods are needed to aid analysts, researchers, and practitioners in understanding and interpreting how these models' internal representations relate to the final classification. In this paper we apply cutting edge techniques from TDA with the goal of gaining insight towards interpretability of convolutional neural networks used for image classification. We use two common TDA approaches to explore several methods for modeling hidden layer activations as high-dimensional point clouds, and provide experimental evidence that these point clouds capture valuable structural information about the model's process. First, we demonstrate that a distance metric based on persistent homology can be used to quantify meaningful differences between layers and discuss these distances in the broader context of existing representational similarity metrics for neural network interpretability. Second, we show that a mapper graph can provide semantic insight as to how these models organize hierarchical class knowledge at each layer. These observations demonstrate that TDA is a useful tool to help deep learning practitioners unlock the hidden structures of their models.

topological data analysis, deep learning↗
DOE OSTI2023

MFA-DVR: direct volume rendering of MFA models

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for large-scale volumetric data, and uniform representation of both structured and unstructured data. Here, in this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured and unstructured volumetric datasets. We demonstrate improved rendering quality using MFA-DVR on both synthetic and real datasets through a comparative study. We show that MFA-DVR not only generates more faithful volume rendering than using local filters but also performs faster on high-order interpolations on structured and unstructured datasets. MFA-DVR is implemented in the existing volume rendering pipeline of the Visualization Toolkit (VTK) to be accessible by the scientific visualization community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Graph-based Compact Modeling (GCM) of CMOS transistors for efficient parameter extraction: A machine learning approach

Parameter extraction of compact transistor models is an expensive process, heavily relying on engineering knowledge and experience. To automate such a process, we propose a novel approach, Graph-based Compact Model (GCM), that integrates physical modeling and data-driven learning. GCM utilizes Graph Neural Networks (GNNs) to establish the model structure, while retaining the physicality in compact models. Here, we implement our GCM in Verilog-A to support circuit simulations. As demonstrated with an academic 7 nm FinFET PDK, the new approach automatically generates a GCM model within a minute, and achieves excellent accuracy and efficiency in SPICE.

42 ENGINEERING↗
DOE OSTI2021

The Stellar decomposition: A compact representation for simplicial complexes and beyond

Here, we introduce the Stellar decomposition, a model for efficient topological data structures over a broad range of simplicial and cell complexes. A Stellar decomposition of a complex is a collection of regions indexing the complex’s vertices and cells such that each region has sufficient information to locally reconstruct the star of its vertices, i.e., the cells incident in the region’s vertices. Stellar decompositions are general in that they can compactly represent and efficiently traverse arbitrary complexes with a manifold or non-manifold domain. They are scalable to complexes in high dimension and of large size, and they enable users to easily construct tailored application-dependent data structures using a fraction of the memory required by a corresponding global topological data structure on the complex. As a concrete realization of this model for spatially embedded complexes, we introduce the Stellar tree, which combines a nested spatial tree with a simple tuning parameter to control the number of vertices in a region. Stellar trees exploit the complex’s spatial locality by reordering vertex and cell indices according to the spatial decomposition and by compressing sequential ranges of indices. Stellar trees are competitive with state-of-the-art topological data structures for manifold simplicial complexes and offer significant improvements for cell complexes and non-manifold simplicial complexes. We conclude with a high-level description of several mesh processing and analysis applications that utilize Stellar trees to process large datasets.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Latest Results from the CUORE Experiment

Abstract The Cryogenic Underground Observatory for Rare Events (CUORE) is the first cryogenic experiment searching for $$0\nu \beta \beta $$ 0 ν β β decay that has been able to reach the one-tonne mass scale. The detector, located at the Laboratori Nazionali del Gran Sasso (LNGS) in Italy, consists of an array of 988 $${\mathrm{TeO}}_{2}$$ TeO 2 crystals arranged in a compact cylindrical structure of 19 towers. CUORE began its first physics data run in 2017 at a base temperature of about 10 mK and in April 2021 released its $$3{\mathrm{rd}}$$ 3 rd result of the search for $$0\nu \beta \beta $$ 0 ν β β , corresponding to a tonne-year of $$\mathrm{TeO}_{2}$$ TeO 2 exposure. This is the largest amount of data ever acquired with a solid state detector and the most sensitive measurement of $$0\nu \beta \beta $$ 0 ν β β decay in $${}^{130}\mathrm{Te}$$ 130 Te ever conducted . We present the current status of CUORE search for $$0\nu \beta \beta $$ 0 ν β β with the updated statistics of one tonne-yr. We finally give an update of the CUORE background model and the measurement of the $${}^{130}\mathrm{Te}$$ 130 Te $$2\nu \beta \beta $$ 2 ν β β decay half-life and decay to excited states of $${}^{130}\mathrm{Xe}$$ 130 Xe , studies performed using an exposure of 300.7 kg yr.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Numerical evaluation of AGR-2 fission product release

The AGR-2 experiment produced normal operation and accident condition fuel performance data for tri-structural isotropic (TRISO) particles with UCO and UO 2 kernels. Data for compacts with no failed particles and for compacts with one or more failed particles are available. To model the fission product diffusion and release from these compacts, it is important to consider the computational mesh as well as the heat conduction and mass diffusion properties of the materials in a TRISO particle. Code and solution verification studies with the Bison fuel performance code were performed to show that the code is computing correct solutions. Bison comparisons to post-irradiation examination data and PARFUME predictions were made for several sets of compacts. Furthermore, these comparisons considered silver, cesium, strontium, and krypton with and without failed particles. Bison predictions closely match those of PARFUME, while both codes generally overpredict post-irradiation examination data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Lignin-Graft-Poly(lactic- co -glycolic) Acid Biopolymers for Polymeric Nanoparticle Synthesis

A lignin-graft-poly(lactic- co -glycolic) acid (PLGA) biopolymer was synthesized with two types of lignin (LGN), alkaline lignin (ALGN) and sodium lignosulfonate (SLGN), at different (A/S)LGN/PLGA ratios (1:2, 1:4, and 1:6 w/w). 1 H NMR and Fourier-transform infrared spectroscopy (FT-IR) confirmed the conjugation of PLGA to LGN. The (A/S)LGN-graft-PLGA biopolymers were used to form nanodelivery systems suitable for entrapment and delivery of drugs for disease treatment. The LGN-graft-PLGA NPs were generally small (100–200 nm), increased in size with the amount of PLGA added, monodisperse, and negatively charged (-48 to -60 mV). Small-angle scattering data showed that particles feature a relatively smooth surface and a compact spherical structure with a distinct core and a shell. The core size and shell thickness varied with the LGN/PLGA ratio, and at a 1:6 ratio, the particles deviated from the core–shell structure to a complex internal structure. The newly developed (A/S)LGN-graft-PLGA NPs are proposed as a potential delivery system for applications in biopharmaceutical, food, and agricultural sectors.

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

Status and results from the CUORE experiment

The Cryogenic Underground Observatory for Rare Events (CUORE) is a tonne-scale cryogenic experiment located at the Laboratori Nazionali del Gran Sasso that exploits bolometric technique to search for neutrinoless double beta decay [0$vββ$] of 130 Te. The detector consists of a segmented array of 988 natural TeO 2 cubic crystals arranged in a cylindrical compact structure of 19 towers. The detector construction was completed in August 2016 and data taking started in Spring 2017. Here, we present a brief description of the bolometric technique for rare events search and the CUORE detector, then we concentrate on the data analysis results. In this respect, we focus on the procedure for data processing and on the first 0$vββ$ results we obtained from a total TeO 2 exposure of 86.3 kg · yr. Next, we illustrate the main background sources and the CUORE background model, from which we obtain the most precise measurement of 130 Te 2$vββ$ half-life to date. Finally, we discuss the improvements achieved with 2018 and 2019 detector optimization campaigns and the current perspectives of our experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

X‐ray characterization of three possible edible oleogelators: Experiment and theory

Abstract Three oleogelator molecules (Triacontane (TC), Stearic acid (SA), and Behenyl Lignocerate (BL)) were studied individually, in pairs, or all together to make an oleogel using triolein as the oil. WAXS, SAXS and USAXS were used to elucidate the solid structures from angstroms to a few micrometers. A two‐dimensional mapping of atomic positions for each molecule was carried out to understand the crystalline multilayer structures formed. We assumed that the molecules were rigidly extended and that they underwent no significant (hindered) rotations so that the free energy is determined by the Lennard‐Jones interactions of closely packed multilayers. TC molecules were predicted to form a tilt angle of , yielding a SAXS line at Å ─1 , in acceptable agreement with the measured . For SA crystals (predicted) yielding a SAXS line at compared to (observed). No mixed crystals were observed for any pair of molecules or when all three were used. USAXS data showed that SA forms large nanocrystals compared to TC and BL. All three combinations of molecular pairs showed basic scatterers smaller or similar to those of individual molecules. The theory presented here, together with the experimental results, showed why no mixed crystals are formed from two or all three molecules. Data from the USAXS region suggested that, when using all three molecules, a more compact fractal structure was obtained, compared with those if one or two of the molecules were used.

Chemistry↗
DOE OSTI2021

Ultra-Structural Imaging Provides 3D Organization of 46 Chromosomes of a Human Lymphocyte Prophase Nucleus

Three dimensional (3D) ultra-structural imaging is an important tool for unraveling the organizational structure of individual chromosomes at various stages of the cell cycle. Performing hitherto uninvestigated ultra-structural analysis of the human genome at prophase, we used serial block-face scanning electron microscopy (SBFSEM) to understand chromosomal architectural organization within 3D nuclear space. Acquired images allowed us to segment, reconstruct, and extract quantitative 3D structural information about the prophase nucleus and the preserved, intact individual chromosomes within it. Our data demonstrate that each chromosome can be identified with its homolog and classified into respective cytogenetic groups. Thereby, we present the first 3D karyotype built from the compact axial structure seen on the core of all prophase chromosomes. The chromosomes display parallel-aligned sister chromatids with familiar chromosome morphologies with no crossovers. Furthermore, the spatial positions of all 46 chromosomes revealed a pattern showing a gene density-based correlation and a neighborhood map of individual chromosomes based on their relative spatial positioning. A comprehensive picture of 3D chromosomal organization at the nanometer level in a single human lymphocyte cell is presented.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Experimental and Computational Insights into the Structural Dynamics of the Fc Fragment of IgG1 Subtype from Biosimilar VEGF‐Trap

The constant fragment (Fc) of the immunoglobulin G1 (IgG1) subtype is increasingly recognized as a crucial scaffold in the development of advanced therapeutics due to its enhanced specificity, efficacy, and extended half‐life. A prime example is VEGF‐Trap (Aflibercept), a recombinant fusion protein that merges the Fc region of the IgG1 subtype with the binding domains of vascular endothelial growth factor receptors (VEGFR)‐1 and VEGFR‐2. The Fc region's role in N‐glycosylation is particularly important, as it significantly influences protein stability. Herein, the first near‐physiological temperature structures of the N‐glycan‐bound Fc fragment of IgG1 subtype from a biosimilar VEGF‐Trap are presented, determined using the SPring‐8 Angstrom Compact free electron LAser (SACLA) and the Turkish Light Source (Turkish DeLight). Comparative analysis with cryogenic structures, including existing data, reveals alternate conformations within the glycan‐binding pocket. Furthermore, molecular dynamics simulations indicate the presence of a high degree of structural plasticity, explaining how the protein adapts its structure through conformational changes. The observed structural fluctuations/conformational changes demonstrate the effect of N‐glycans on protein stability. These findings offer new insights into the molecular basis of Fc‐mediated functions and provide valuable information for the design of next‐generation therapeutics.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Enabling Particulate Materials Processing Science for High-Consequence, Small-Lot Precision Manufacturing

This Laboratory Directed Research and Development project developed and applied closely coupled experimental and computational tools to investigate powder compaction across multiple length scales. The primary motivation for this work is to provide connections between powder feedstock characteristics, processing conditions, and powder pellet properties in the context of powder-based energetic components manufacturing. We have focused our efforts on multicrystalline cellulose, a molecular crystalline surrogate material that is mechanically similar to several energetic materials of interest, but provides several advantages for fundamental investigations. We report extensive experimental characterization ranging in length scale from nanometers to macroscopic, bulk behavior. Experiments included nanoindentation of well-controlled, micron-scale pillar geometries milled into the surface of individual particles, single-particle crushing experiments, in-situ optical and computed tomography imaging of the compaction of multiple particles in different geometries, and bulk powder compaction. In order to capture the large plastic deformation and fracture of particles in computational models, we have advanced two distinct meshfree Lagrangian simulation techniques: 1.) bonded particle methods, which extend existing discrete element method capabilities in the Sandia-developed , open-source LAMMPS code to capture particle deformation and fracture and 2.) extensions of peridynamics for application to mesoscale powder compaction, including a novel material model that includes plasticity and creep. We have demonstrated both methods for simulations of single-particle crushing as well as mesoscale multi-particle compaction, with favorable comparisons to experimental data. We have used small-scale, mechanical characterization data to inform material models, and in-situ imaging of mesoscale particle structures to provide initial conditions for simulations. Both mesostructure porosity characteristics and overall stress-strain behavior were found to be in good agreement between simulations and experiments. We have thus demonstrated a novel multi-scale, closely coupled experimental and computational approach to the study of powder compaction. This enables a wide range of possible investigations into feedstock-process-structure relationships in powder-based materials, with immediate applications in energetic component manufacturing, as well as other particle-based components and processes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Huntingtin structure is orchestrated by HAP40 and shows a polyglutamine expansion-specific interaction with exon 1

Huntington’s disease results from expansion of a glutamine-coding CAG tract in the huntingtin (HTT) gene, producing an aberrantly functioning form of HTT. Both wildtype and disease-state HTT form a hetero-dimer with HAP40 of unknown functional relevance. Here, we demonstrate in vivo and in cell models that HTT and HAP40 cellular abundance are coupled. Integrating data from a 2.6 Å cryo-electron microscopy structure, cross-linking mass spectrometry, small-angle X-ray scattering, and modeling, we provide a near-atomic-level view of HTT, its molecular interaction surfaces and compacted domain architecture, orchestrated by HAP40. Native mass spectrometry reveals a remarkably stable hetero-dimer, potentially explaining the cellular inter-dependence of HTT and HAP40. The exon 1 region of HTT is dynamic but shows greater conformational variety in the polyglutamine expanded mutant than wildtype exon 1. Our data provide a foundation for future functional and drug discovery studies targeting Huntington’s disease and illuminate the structural consequences of HTT polyglutamine expansion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Self-supervised learning of spatiotemporal thermal signatures in additive manufacturing using reduced order physics models and transformers

Microstructure control via additive manufacturing has enormous potential as manufacturers, materials scientists, and designers alike seek to exploit novel fabrication technologies to improve component performance. Recent works have demonstrated the feasibility of producing materials with controlled microstructures across various length scales. However, the experimental approach towards exploring the process-structure space can be laborious and costly. This is particularly true if also considering scan pattern optimization which is well suited for processes such as powder bed fusion electron beam melting. In this work we propose an approach for encoding additive manufacturing layer-wise thermal response signatures using self-supervised representation learning. Thermal simulations from a reduced order model are utilized to estimate the spatiotemporal response during printing. A machine learning framework, using video-transformers, is utilized to efficiently distill spatiotemporal patterns into a compact latent space representation. This latent state representation encodes the relevant physics which is then utilized to establish a data-driven process-structure model for an additively manufactured Ni-based superalloy. In conclusion, the proposed methodology could potentially be used towards in-situ process monitoring, scan pattern experimental design, and component qualification.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Kinetics and Mechanism of Nickel Oxide Reduction by Methane

Nickel oxide reduction by methane is of particular importance in catalysis, extractive metallurgy, and clean power generation technologies. Despite extensive investigations of the NiO + CH 4 reaction, many questions remain about its kinetics and molecular and structural transformation mechanisms. We discuss the reduction kinetics of bulk polycrystalline NiO by CH 4 using a new calorimetric method. The technique permits rapid, controllable heating of the NiO/Ni wires and continuous data (electrical power, the electrical resistivity of the wire, and temperature) acquisition with a frequency of 10 kHz. The method also allows arresting and preservation of the structure of the sample by programmed termination of electric power in thin specimens. This approach, coupled with ex situ electron microscopy, allows determination of the reaction rate and tracking of the ongoing structural transformations. The mechanism of NiO reduction by methane is suggested based on direct correlations between reaction kinetics and the observed microstructure transformations. The kinetic data treatment revealed that the nucleation-growth model describes the reaction kinetics. Ex situ microscopy observations of reacted specimens showed that the first nuclei rapidly accelerate the process. Pore formation occurs at the nucleation sites, and the porous structure then quickly grows into the bulk NiO. The second type of reduced Ni microstructure, compact layer, can be observed only on the outer surface of the wire. Based on kinetic data and microstructural observations, a mechanism of reduction was suggested according to which porous microstructure forms predominantly by NiO reduction with carbon. The compact layer observed on the outer surface of the wire forms through the diffusion-controlled hydrogen reduction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder

Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of how the topology of the unit cell structure affects the macroscopic behavior of the material in both the linear and the non-linear regimes encountered under large compression. Here, we have developed a machine learning (ML) approach capable of accelerating the prediction of the stress–strain response of a polymer-based cellular structure under uniaxial confined compression. As part of generating the training data for ML, 60,000 mesostructures were generated using a relatively novel approach based on cellular automata, and their corresponding stress–strain responses were obtained from the finite element simulations. Principal component analysis (PCA) was used to reduce the dimensionality of the stress–strain curves. With only 20 principal components, PCA captured 99.89% of the variance in the stress–strain curves while reducing the dimensionality by 5X. ML using supervised autoencoder was able to successfully speed up the prediction of the non-linear stress–strain response of a unit cell by up to 4600X. The proposed method can serve as an efficient data generation tool and a rapid means for predicting the structure–property relationship through accelerated forward modeling of cellular materials under compaction, in cases where the macroscopic stress–strain response is governed by the unit-cell topology.

36 MATERIALS SCIENCE↗