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At least 163 records · Page 9

All sources NASA NTRS DOE OSTI

DOE OSTI2023

AEflow (Autoencoder fluid flow compression network) [SWR-22-29]

As the size of turbulent flow simulations continues to grow, in situ data compression is becoming increasingly important for visualization, analysis, and restart checkpointing. For these applications, single-pass compression techniques with low computational and communication overhead are crucial. In this paper we present a deep-learning approach to in situ compression using an autoencoder architecture that is customized for three-dimensional turbulent flows and is well suited for contemporary heterogeneous computing resources. The autoencoder is compared against a recently introduced randomized single-pass singular value decomposition (SVD) for three different canonical turbulent flows: decaying homogeneous isotropic turbulence, a Taylor-Green vortex, and turbulent channel flow. Our proposed fully convolutional autoencoder architecture compresses turbulent flow snapshots by a factor of 64 with a single pass, allows for arbitrarily sized input fields, is cheaper to compute than the randomized single-pass SVD for typical simulation sizes, performs well on unseen flow configurations, and has been made publicly available. The results reported here show that the autoencoder dramatically outperforms a randomized single-pass SVD with similar compression ratio and yields comparable performance to a higher-rank decomposition with an order of magnitude less compression in regard to preserving a number of important statistical quantities such as turbulent kinetic energy, enstrophy, and Reynolds stresses.

King, Ryan↗

DOE OSTI2022

Toward Quantity-of-Interest Preserving Lossy Compression for Scientific Data

Today's scientific simulations and instruments are producing a large amount of data, leading to difficulties in storing, transmitting, and analyzing these data. While error-controlled lossy compressors are effective in significantly reducing data volumes and efficiently developing databases for multiple scientific applications, they mainly support error controls on raw data, which leaves a significant gap between the data and user's downstream analysis. This may cause unqualified uncertainties in the outcomes of the analysis, a.k.a quantities of interest (QoIs), which are the major concerns of users in adopting lossy compression in practice. In this paper, we propose rigorous mathematical theories to preserve four families of QoIs that are widely used in scientific analysis during lossy compression along with practical implementations. Specifically, we first develop the error control theory for univariate QoIs which are essential for computing physical properties such as kinetic energy, followed by multivariate QoIs that are more commonly used in real-world applications. The proposed method is integrated into a state-of-the-art compression framework in a modular fashion, which could easily adapt to new QoIs and new compression algorithms. Experiments on real-world datasets demonstrate that the proposed method provides faithful error control on important QoIs including kinetic energy, regional average, and isosurface without trials and errors, while offering compression ratios that are up to 4x of the compression ratios provided by state-of-the-art compressors.

Jiao, Pu↗

DOE OSTI2026

Scientific Data Compression for Large Scale Computational Fluid Dynamics (CFD) Simulations

This Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and General Electric (GE) investigated methods for reducing the size of large computational fluid dynamics (CFD) simulation datasets using scientific data compression techniques. The work focused on adapting the MultiGrid Adaptive Reduction of Data (MGARD) compression framework and integrating it with high-performance I/O and visualization tools used in CFD workflows. MGARD uses hierarchical multilevel decomposition to enable error-controlled compression of floating-point scientific data while preserving quantities of interest. During the project, MGARD compression was integrated with the ADIOS I/O framework and visualization tools such as ParaView to enable efficient storage, transfer, and analysis of simulation data. The collaboration also explored approaches for improving compression performance for CFD data defined on unstructured meshes. Results demonstrate that scientific data compression can significantly reduce storage requirements and improve data management for large-scale CFD simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Small-amplitude Compressible Magnetohydrodynamic Turbulence Modulated by Collisionless Damping in Earth’s Magnetosheath: Observation Matches Theory

Plasma turbulence is a ubiquitous dynamical process that transfers energy across many spatial and temporal scales and affects energetic particle transport. Recent advances in the understanding of compressible magnetohydrodynamic (MHD) turbulence demonstrate the important role of damping in shaping energy distributions on small scales, yet its observational evidence is still lacking. This study provides the first observational evidence of substantial collisionless damping (CD) modulation on the small-amplitude compressible MHD turbulence cascade in Earth’s magnetosheath using four Cluster spacecraft. Based on an improved compressible MHD decomposition algorithm, turbulence is decomposed into three eigenmodes: incompressible Alfvén modes and compressible slow and fast (magnetosonic) modes. Our observations demonstrate that CD enhances the anisotropy of compressible MHD modes because CD has a strong dependence on wave propagation angle. The wavenumber distributions of slow modes are mainly stretched perpendicular to the background magnetic field (B 0 ) and weakly modulated by CD. In contrast, fast modes are subjected to a more significant CD modulation. Fast modes exhibit a weak, scale-independent anisotropy above the CD truncation scale. Below the CD truncation scale, the anisotropy of fast modes enhances as wavenumbers increase. As a result, fast-mode fractions in the total energy of compressible modes decrease with the increase of perpendicular wavenumber (to B 0 ) or wave propagation angle. Our findings reveal how the turbulence cascade is shaped by CD and its consequences for anisotropies in the space environment.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Compressing micro-operations in scheduler entries in a processor

An electronic device includes a processor having a micro-operation queue, multiple scheduler entries, and scheduler compression logic. When a pair of micro-operations in the micro-operation queue is compressible in accordance with one or more compressibility rules, the scheduler compression logic acquires the pair of micro-operations from the micro-operation queue and stores information from both micro-operations of the pair of micro-operations into different portions in a single scheduler entry. In this way, the scheduler compression logic compresses the pair of micro-operations into the single scheduler entry.

Boyer, Michael W.↗

DOE OSTI2023

Classical and quantum compression for edge computing: the ubiquitous data dimensionality reduction

Edge computing aims to address the challenges associated with communicating and transferring large amounts of data generated remotely to a data center in a timely and efficient manner. A central pillar of edge computing is local (i.e., at- or near-source) data processing capability so that data transfer to a data center for processing can be minimized. Data compression at the edge is therefore a natural component of edge workflows. Here we present a survey of data compression algorithms with a focus on edge computing. Not all compression algorithms can accommodate the data type heterogeneity, tight processing and communication time constraints, or energy efficiency requirement characteristics of edge computing. We discuss specific examples of compression algorithms that are being explored in the context of edge computing. We end our review with a brief survey of emerging quantum compression techniques that are of importance in quantum information processing, including the proposed concept of quantum edge computing.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Macro- and micro-compression testing of shales

Here, macro- and micro-geomechanical properties of shales were investigated at millimeter- and micrometer-scale using an example of the Woodford Shale. Geomechanical properties, such as uniaxial (unconfined) compressive strength and Young's modulus, were quantified at the millimeter-scale and compared with results from micro-compression testing experiments of FIB-SEM-nanofabricated micro-pillars. Size-scale and compositional/structural heterogeneity effect on elasto-plastic deformation and failure behavior of shales were investigated. Also, relationship between elemental/mineral composition and unconfined compressive strength and Young's modulus, at a micrometer-scale, was discussed. It was shown that geomechanical properties of shales are scale-dependent and are strongly affected by compositional/structural anisotropy. In particular, micro-compression testing experiments showed non-uniformly distributed compressive strength and Young's modulus within the investigated Woodford Shale rock samples. It was demonstrated, that geomechanical properties of shales, investigated at the micrometer-scale, tend to be affected by different minerals and/or pores, while the same properties, investigated at the millimeter-scale, are governed by different micro-facies (and/or micro-fractures) present within the rock. This shows that geomechanical properties of shales can be dramatically different depending on the scale of investigation and compositional/structural heterogeneity of these rocks, and therefore are not easily transferable across the scales.

58 GEOSCIENCES↗

DOE OSTI2025

Massive compression for high data rate macromolecular crystallography (HDRMX): impact on diffraction data and subsequent structural analysis

New higher-count-rate, integrating, large-area X-ray detectors with framing rates as high as 17400 images per second are beginning to be available. These will soon be used for specialized macromolecular crystallography experiments but will require optimal lossy compression algorithms to enable systems to keep up with data throughput. Some information may be lost. Can we minimize this loss with acceptable impact on structural information? To explore this question, we have considered several approaches: summing short sequences of images, binning to create the effect of larger pixels, use of JPEG-2000 lossy wavelet-based compression, and use of Hcompress, which is a Haar-wavelet-based lossy compression borrowed from astronomy. We also explore the effect of the combination of summing, binning, and Hcompress or JPEG-2000. In each of these last two methods one can specify approximately how much one wants the result to be compressed from the starting file size. These provide particularly effective lossy compressions that retain essential information for structure solution from Bragg reflections.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Progressive Tree-Based Compression of Large-Scale Particle Data

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Physics-Driven Convolutional Autoencoder Approach for CFD Data Compressions: Preprint

With the growing size and complexity of turbulent flow models, data compression approaches are of the utmost importance to analyze, visualize, or restart the simulations. Recently, in-situ autoencoder-based compression approaches have been proposed and shown to be effective at producing reduced representations of turbulent flow data. However, these approaches focus solely on training the model using point-wise sample reconstruction losses that do not take advantage of the physical properties of turbulent flows. In this paper, we show that training autoencoders with additional physics-informed regularizations, e.g., enforcing incompressibility and preserving enstrophy, improves the compression model in three ways: (i) the compressed data better conform to known physics for homogeneous isotropic turbulence without negatively impacting point-wise reconstruction quality, (ii) inspection of the gradients of the trained model uncovers changes to the learned compression mapping that can facilitate the use of explainability techniques, and (iii) as a performance byproduct, training losses are shown to converge up to 12x faster than the baseline model.

auto-encoders↗

DOE OSTI2021

Effect of particle characteristics on the evolution of particle size, particle morphology, and fabric of sands loaded under uniaxial compression

This work presents the results and analyses of uniaxial compression experiments performed on three silica sands. The sands have comparable particle-size distributions, but their particles differ in morphology and strength. Cylindrical samples of the three sands were compressed in a loading device placed inside an X-ray microscope (XRM) and scanned at multiple stress levels during uniaxial compression. 3D tomography data of the samples obtained from the XRM at different stress levels were then analyzed to obtain the distributions of particle size, particle morphology, and interparticle contact normals within the sample. Results indicate that: (1) the compressibility of the sands loaded under uniaxial compression is closely tied to particle morphology and strength and (2) the anisotropy in the orientations of interparticle contact normals generally increases with axial stress; however, this increase is limited by the occurrence of particle crushing in the sample.

3D X-ray computed tomography↗

DOE OSTI2020

Impacts of swinging door lossy compression of synchrophasor data

As the number of operational Phasor Measurement Units (PMUs) is increasing, compression is becoming crucial for efficiently transferring and storing enormous amount of synchrophasor data continuously measured from a power system. Some utilities in the U.S. have already adopted a nonlinear compression technique, called Swinging Door Algorithm (SDA), assuming compressed data meets technical and regulatory requirements without resulting in any degradation of the measurements. This paper studies impacts of this compression algorithm on field measured and simulated PMU data, and presents results from spectral analyses of raw and decompressed measurements. The results show blind application of the SDA compression alters frequency content in the measurements and introduces false harmonics and aliasing. These can lead to false conclusions being drawn from the measurements about the power system condition.

42 ENGINEERING↗

DOE OSTI2021

Transport of Compressed Woody Biomass: Correlating Rheology and Microcompounder Measurements

Transport of milled solid biomass can be problematic in biomass conversion processes due to the material’s high cohesiveness, low bulk density, and poor flowability. Here, the goal is to investigate the flow behavior of compressed biomass under conditions similar to compression-screw feeders. To mimic the flow of woody biomass inside a compression-screw feeder, two lignocellulosic biomass feedstocks were fed to a co-rotating twin-screw microcompounder. Force and screw speed from the microcompounder were correlated to viscosity in a traditional rheometer, where low-density polyethylene served as a control. The deformation of compressed biomass in a lab-scale compounder is strongly dependent on screw speed and moisture content. Shear stress in woody biomass decreased with the shear rate, indicating negative plastic viscosity. Moreover, viscosity decreased one order of magnitude as screw speed increased from 10 to 90 rpm. Increasing moisture from 10 to 50% reduced the viscosity of the compressed biomass by 60%.

09 BIOMASS FUELS↗

DOE OSTI2026

An Algorithm for Atom-Centered Lossy Compression of the Atomic Orbital Basis in Density Functional Theory Calculations

Large atomic-orbital (AO) basis sets of at least triple and preferably quadruple-ζ (QZ) size are required to adequately converge Kohn–Sham density functional theory (DFT) calculations toward the complete basis set limit. However, incrementing the cardinal number by one nearly doubles the AO basis dimension, and the computational cost scales as the cube of the AO dimension, so this is very computationally demanding. Here, in this work, we develop and test a threshold-based natural atomic orbital (NAO) scheme in which ϵ-NAOs are obtained as eigenfunctions of atomic blocks of the density matrix in a one-center orthogonalized representation. This enables compression of the AO basis that is optimal for a given threshold, 10 –ϵ , by discarding NAOs with occupation numbers below that threshold. Extensive pilot test calculations using the Hartree–Fock functional and taking the converged density matrix as input suggest that a threshold of 10 –5 can yield a compression factor (ratio of AO to compressed ϵ-NAO dimension) between 2.5 and 4.5 for the QZ pc-3 basis. The errors in relative energies are typically less than 0.1 kcal/mol when the compressed basis is used instead of the uncompressed basis. Between 10 and 100 times smaller errors (i.e., usually less than 0.01 kcal/mol) can be obtained with a threshold 10 –7 , while the compression factor is typically between 2 and 2.5.

basis sets↗

DOE OSTI2025

System and Machine Learning-Guided Materials Design for High-Pressure Hydrogen Compression

Cost-effective and reliable hydrogen compression remains a challenging barrier in the widespread adoption of hydrogen as an energy carrier. The prevailing technology of mechanical compression suffers from several drawbacks, some of which can be addressed by nonmechanical compression strategies (e.g., electrochemical or metal hydride-based thermal compression). Thermally driven metal hydride compression strategies typically rely on multistage metal hydride-based compressors; however, discovering or optimizing low-stability metal hydrides that can pressurize hydrogen upward of 1000 bar is difficult, both with respect to computational predictions and experimental validation. Here, in this study, we (1) demonstrate that simple machine learning-derived design rules can inform the rational design of alloying strategies yielding low-stability hydrides, (2) validate their experimental pressure–composition–temperature (PCT) isotherms up to 875 bar, and (3) utilize a dynamic system-level model of a metal hydride compressor design to evaluate their performance under realistic operating conditions. Importantly, this analysis yields predicted operational efficiencies of both 2-stage (90–875 bar) and 3-stage (20–875 bar) metal hydride compressors to enable further evaluation of this technology and its techno-economic outlook.

alloy optimization↗

DOE OSTI2022

A compact x-ray diffraction system for dynamic compression experiments on pulsed-power generators

Pulsed-power generators can produce well-controlled continuous ramp compression of condensed matter for high-pressure equation-of-state studies using the magnetic loading technique. X-ray diffraction (XRD) data from dynamically compressed samples provide direct measurements of the elastic compression of the crystal lattice, onset of plastic flow, strength–strain rate dependence, structural phase transitions, and density of crystal defects, such as dislocations. Here, we present a cost-effective, compact, pulsed x-ray source for XRD measurements on pulsed-power-driven ramp-loaded samples. This combination of magnetically driven ramp compression of materials with a single, short-pulse XRD diagnostic will be a powerful capability for the dynamic materials’ community to investigate in situ dynamic phase transitions critical to equation of states. Finally, we present results using this new diagnostic to evaluate lattice compression in Zr and Al and to capture signatures of phase transitions in CdS.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

Three-dimensional full-field velocity measurements in shock compression experiments using stereo digital image correlation

Shock compression plate impact experiments conventionally rely on point-wise velocimetry measurements based on laser-based interferometric techniques. This study presents an experimental methodology to measure the free surface full-field particle velocity in shock compression experiments using high-speed imaging and three-dimensional (3D) digital image correlation (DIC). The experimental setup has a temporal resolution of 100 ns with a spatial resolution varying from 90 to 200 μm/pixel. Experiments were conducted under three different plate impact configurations to measure spatially resolved free surface velocity and validate the experimental technique. First, a normal impact experiment was conducted on polycarbonate to measure the macroscopic full-field normal free surface velocity. Second, an isentropic compression experiment on Y-cut quartz–tungsten carbide assembly is performed to measure the particle velocity for experiments involving ramp compression waves. To explore the capability of the technique in multiaxial loading conditions, a pressure shear plate impact experiment was conducted to measure both the normal and transverse free surface velocities under combined normal and shear loading. The velocities measured in the experiments using digital image correlation are validated against previous data obtained from laser interferometry. Numerical simulations were also performed using established material models to compare and validate the experimental velocity profiles for these different impact configurations. The novel ability of the employed experimental setup to measure full-field free surface velocities with high spatial resolutions in shock compression experiments is demonstrated for the first time in this work.

47 OTHER INSTRUMENTATION↗

DOE OSTI2024

Static and shock compression studies of eutectic high-entropy alloy AlCoCrFeNi 2.1 to ultrahigh pressures

The high-entropy alloy with composition AlCoCrFeNi 2.1 , additively manufactured with the laser powder-bed fusion technique, has a far-from-equilibrium BCC/FCC eutectic nanolamellar structure. We studied the high-pressure response of this alloy under both static compression and high-strain rate shock compression. The response to static compression using a diamond anvil cell was studied at pressures up to 302 GPa with synchrotron x-ray diffraction at the advanced photon source. The high-pressure FCC-only phase of the EHEA previously observed by Pope et al. [AIP Adv. 13, 035124 (2023)] is found to be stable up to the highest pressure achieved in this study with a volume compression of V/V 0 = 0.587 at ambient temperature. The shock experiments were performed by using GEKKO XII lasers at the Institute of Laser Engineering, Osaka University. The principal Hugoniot equation-of-state of the EHEA was measured up to a pressure of 515 GPa and a compression of V/V 0 = 0.613. Additionally, the thermal equation of state of the EHEA was measured up to 6.2 GPa and 1623 K using a large-volume Paris–Edinburgh cell to obtain the temperature dependence of bulk modulus and thermal expansion coefficients. The melting temperature for EHEA AlCoCrFeNi 2.1 at a pressure of 5.6 GPa was measured to be 1648 ± 25 K. These results can be used to refine stochastic (or special) quasi-random structure (SQS) models for high-pressure high-temperature behavior of high-entropy alloys.

36 MATERIALS SCIENCE↗