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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Linear-Complexity Tensor Butterfly Algorithm for Compressing High-Dimensional Oscillatory Integral Operators

This paper presents a multilevel tensor compression algorithm called tensor butterfly algorithm for efficiently representing large-scale and high-dimensional oscillatory integral operators, including Green's functions for wave equations and integral transforms such as Radon transforms and Fourier transforms. The proposed algorithm leverages a tensor extension of the so-called complementary low-rank property of existing matrix butterfly algorithms. The algorithm partitions the discretized integral operator tensor into subtensors of multiple levels and factorizes each subtensor at the middle level as a Tucker-type interpolative decomposition, whose factor matrices are formed in a multilevel fashion. For a d-dimensional (d > 1) integral operator discretized into a 2d-mode tensor with n2d entries, the overall CPU time and memory requirement scale as O(nd), in stark contrast to the O(nd log n) complexity of existing matrix algorithms such as matrix butterfly algorithms and fast Fourier transforms (FFTs), where n is the number of points per direction. When comparing with other tensor algorithms such as quantized tensor train (QTT), the proposed algorithm also shows superior CPU and memory performance for tensor contraction. Remarkably, the tensor butterfly algorithm can efficiently model high-frequency Green's function interactions between two unit cubes, each spanning 512 wavelengths per direction, which represents problems of scale over 512× larger than that existing butterfly algorithms can handle, with the same amount of computation resources. On the other hand, for a problem representing 64 wavelengths per direction, which is the largest size existing algebraic matrix algorithms can handle, our tensor butterfly algorithm exhibits 200x speedups and 30× memory reduction compared with existing ones. Moreover, the tensor butterfly algorithm also permits O(nd)-complexity FFTs and Radon transforms up to d = 6 dimensions.

Kielstra, P Michael↗
DOE OSTI2022

Multisource Mobile Transfer Learning Algorithm Based on Dynamic Model Compression

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

Gao, Peng↗
DOE OSTI2026

3D Gaussian Splatting for Volume Compression

This codebase uses machine learning to train a collection of 3D Gaussian distributions to approximate scientific volume data. Because this collection uses less memory than the original dataset, it can be used as a compressed model of the original data for applications such as visualization.

Dyken, Landon↗
DOE OSTI2021

Deep reinforcement learning for dynamic control of fuel injection timing in multi-pulse compression ignition engines

Conventional compression-ignition (CI) engines have long offered high thermal efficiencies and torque across a wide range of loads, but often require extensive exhaust gas treatment that decreases efficiency to meet ever-increasing emissions regulations. One strategy to decrease emissions is to split the fuel injection into a series of smaller injections. In this paper, we explore a new way of discovering optimal control strategies for the next generation of CI engines using deep reinforcement learning (DRL). We outline a DRL procedure to maximize the weighted reward of engine work while minimizing end-of-cycle NO x emissions. Through the procedure outlined in this paper, we show that the DRL agent is able to reduce NO x emissions threefold while only decreasing network by 2%. We demonstrate the use of transfer learning (TL) across hierarchies of physical models to accelerate the learning process, making this approach feasible for a range of control problems within this space. This paper presents a framework and demonstration for using DRL to design control systems in technology areas such as multi-pulse engine control where a hierarchy of models combined with multi-objective rewards are used for optimal operation.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Atomistic Simulations of the Elastic Compression of Platinum Nanoparticles

Abstract The elastic behavior of nanoparticles depends strongly on particle shape, size, and crystallographic orientation. Many prior investigations have characterized the elastic modulus of nanoscale particles using experiments or simulations; however their reported values vary widely depending on the methods for measurement and calculation. To understand these discrepancies, we used classical molecular dynamics simulation to model the compression of platinum nanoparticles with two different polyhedral shapes and a range of sizes from 4 to 20 nm, loaded in two different crystal orientations. Multiple standard methods were used to calculate the elastic modulus from stress-vs-strain data for each nanoparticle. The magnitudes and particle-size dependence of the resulting moduli varied with calculation method and, even for larger nanoparticles where bulk-like behavior may be expected, the effective elastic modulus depended strongly on shape and orientation. Analysis of per-atom stress distributions indicated that the shape- and orientation-dependence arise due to stress triaxiality and inhomogeneity across the particle. When the effective elastic modulus was recalculated using a representative volume element in the center of a large nanoparticle, the elastic modulus had the expected value for each orientation and was shape independent. It is only for single-digit nanoparticles that meaningful differences emerged, where even the very center of the particle had a lower modulus due to the effect of the surface. These findings provide better understanding of the elastic properties of nanoparticles and disentangle geometric contributions (such as stress triaxiality and spatial inhomogeneity) from true changes in elastic properties of the nanoscale material.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Sweep-tracing algorithm: in silico slip crystallography and tension-compression asymmetry in BCC metals

Abstract Direct Molecular Dynamics (MD) simulations are being increasingly employed to model dislocation-mediated crystal plasticity with atomic resolution. Thanks to the dislocation extraction algorithm (DXA), dislocation lines can be now accurately detected and positioned in space and their Burgers vector unambiguously identified in silico, while the simulation is being performed. However, DXA extracts static snapshots of dislocation configurations that by themselves present no information on dislocation motion. Referred to as a sweep-tracing algorithm (STA), here we introduce a practical computational method to observe dislocation motion and to accurately quantify its important characteristics such as preferential slip planes (slip crystallography). STA reconnects pairs of successive snapshots extracted by DXA and computes elementary slip facets thus precisely tracing the motion of dislocation segments from one snapshot to the next. As a testbed for our new method, we apply STA to the analysis of dislocation motion in large-scale MD simulations of single crystal plasticity in BCC metals. We observe that, when the crystal is subjected to uniaxial deformation along its [001] axis, dislocation slip predominantly occurs on the {112} maximum resolved shear stress plane under tension, while in compression slip is non-crystallographic (pencil) resulting in asymmetric mechanical response. The marked contrast in the observed slip crystallography is attributed to the twinning/anti-twinning asymmetry of shears in the {112} planes relatively favoring dislocation motion in the twinning sense while hindering dislocations from moving in the anti-twinning directions.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Hybrid time–frequency domain dual-probe coherent anti-Stokes Raman scattering for simultaneous temperature and pressure measurements in compressible flows via spectral fitting

We demonstrate a hybrid time–frequency spectroscopic method for simultaneous temperature/pressure measurements in nonreacting compressible flows with known gas composition. Hybrid femtosecond–picosecond, pure-rotational coherent anti-Stokes Raman scattering (CARS), with two independent, time-delayed probe pulses, is deployed for single-laser-shot measurements of temperature and pressure profiles along an ∼ <#comment/> 5 -mm line. The theory of dual-probe CARS is presented, along with a discussion of the iterative fitting of experimental spectra. Temperature is obtained from spectra acquired with an early, near-collision-free probe time delay ( τ <#comment/> 1 = 0 p s ) and pressure from spectra obtained at probe delays of τ <#comment/> 2 = 150 − <#comment/> 1000 p s , where collisions significantly impact the spectral profile. Unique solutions for temperature and pressure are obtained by iteratively fitting the two spectra to account for small collisional effects observed for the near zero probe delay spectrum. A dual-probe pure-rotational CARS system, in a 1D line-imaging configuration, is developed to demonstrate effectively the simultaneous temperature and pressure profiles recorded along the axial centerline of a highly underexpanded jet. The underexpanded air jet permits evaluation of this hybrid time–frequency domain approach for temperature and pressure measurements across a wide range of low-temperature–low-pressure conditions of interest in supersonic ground-test facilities. Single-laser-shot measurement precisions in both quantities and pressure measurement accuracy are systematically evaluated in the quiet zone upstream of the Mach disk. Precise thermometry approaching 1%−2% is observed in regions of high CARS signal-to-noise ratios. Pressure measurements are optimized at probe time delays where the ratio of the late probe delay to the Raman lifetime exceeds four ( τ <#comment/> 2 / τ <#comment/> R > <#comment/> 4 ). The impact of low-temperature Raman linewidths on CARS pressure measurements is evaluated, and comparisons of CARS pressures obtained with our recent low-temperature pure-rotational Raman linewidth data and extrapolated high-temperature Q -branch linewidths are presented. Considering all measurements with τ <#comment/> 2 / τ <#comment/> R ≥ <#comment/> 4.0 , measured pressures were on average 7.9% of the computed isentropic values with average shot-to-shot deviations representing a combination of instrument noise and fluid fluctuations of 5.0%.

Retter, Jonathan E.↗
DOE OSTI2020

Compressive characterization of biphoton frequency spectra

Frequency-bin qudits constitute a promising tool for quantum information processing. Here we use compressive sensing to characterize the spectral correlations of entangled photon pairs in a quantum frequency comb, obtaining a 26-fold reduction in measurement time compared to raster scanning.

Simmerman, Emma↗
DOE OSTI2023

Numerical investigation of gas-filled multipass cells in the enhanced dispersion regime for clean spectral broadening and pulse compression

We show via numerical simulations that the regime of enhanced frequency chirp can be achieved in gas-filled multipass cells. Our results demonstrate that there exists a region of pulse and cell parameters for which a broad and flat spectrum with a smooth parabolic-like phase can be generated. This spectrum is compatible with clean ultrashort pulses, whose secondary structures are always below the 0.5% of its peak intensity such that the energy ratio (the energy contained within the main peak of the pulse) is above 98%. This regime makes multipass cell post-compression one of the most versatile schemes to sculpt a clean intense ultrashort optical pulse.

Segundo Staels, V. W. (ORCID:0000000200056157)↗
DOE OSTI2022

Energy scaling beyond the gas ionization threshold with divided-pulse nonlinear compression

Here, we demonstrate how pulse energy in hollow-core fiber can be scaled beyond gas-ionization limitations using divided-pulse nonlinear compression. With one pulse, ionization limits our fiber’s pulse energy to 2.7-mJ output at an input of 4 mJ. By dividing to four low-energy pulses before the fiber, we eliminated the ionization and scaled the pulse energy 2.5× to 6.6 mJ at an input energy of 10 mJ. Larger energy scaling is possible, as our maximum pulse energy has not reached the new gas ionization threshold. Our results motivate applying the method to state-of-the-art systems for large pulse energy scaling without prohibitive system size increases.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Experimental Investigation of Concrete Fatigue in Axial Compression

The use of concrete in fatigue critical structures, such as wind turbine towers, has necessitated the replacement of simplistic (and therefore insufficient) fatigue design recommendations with more accurate and advanced fatigue models that can consider a greater complexity of stress states and materials. As the first part of a multistage test series, an experimental investigation was conducted to determine the fatigue life of concrete subjected to axial compressive fatigue loading, focusing on the relatively high-cycle fatigue domain. Here, a wide range of values for maximum stress levels and stress ratios were considered. The experimental results were compared against existing predictive models, and the most applicable model was identified. Concrete strain and stiffness behavior during fatigue loading were also investigated and reported. Based on the observed behavior, a hypothesis for identifying the impending occurrence of fatigue failure, based on the monotonic stress-strain curve, is examined.

17 WIND ENERGY↗
DOE OSTI2021

Structural response of 3D-printed rubber lattice structures under compressive fatigue

In this work, a new direct UV curing, polyurethane-like, highly stretchable rubber, Elastic ToughRubber 90™ (ETR 90), is demonstrated for 3D printing via photocuring with Texas Instruments DLP® optical systems. An array of test specimens, comprising a sandwich structure with a body-centered cubic lattice core, are 3D printed from ETR 90. The compressive behavior of these specimens is characterized up to 10 k and up to 100 k fatigue-loading cycles. The internal, visually obstructed, structures under loads are visualized using in situ X-ray tomography. All lattice structures remain intact and full recovery is observed, indicating the cyclic durability of the 3D-printed rubber.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Shock Compression of Materials: SSDDS Lecture 4

Lecture roadmap: Definitions, explanation of shock wave, properties of a shock wave, relations to equation of state and quasistatic compression, calculations, measurements, and problems.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Nemchinov-Dyson Solutions of the Two-Dimensional Axisymmetric Inviscid Compressible Flow Equations [Review]

We investigate the two-dimensional (2D) inviscid compressible flow equations in axisymmetric coordinates, constrained by an ideal gas equation of state (EOS). Beginning with the assumption that the 2D velocity field is space-time separable and linearly variable in each corresponding spatial coordinate, we proceed to derive an in nite family of elliptic or hyperbolic, uniformly expanding or contracting \gas cloud" solutions. Construction of specific example solutions belonging to this family is dependent on the solution of a system of nonlinear, coupled, second-order ordinary differential equations, and the prescription of an additional physical process of interest (e.g., uniform temperature or uniform entropy flow). The physical and computational implications of these solutions as pertaining to quantitative code verification or model qualification studies are discussed in some detail.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Heavy-Duty Mixed-Controlled Compression Ignition: Fuel Effects and Ducted Fuel Injection.

This project is focused on developing advanced combustion strategies for mixing-controlled compression ignition (MCCI, i.e., diesel-cycle) engines that are synergistic with renewable and/or unconventional fuels in a manner that enhances domestic energy security, economic competitiveness, and environmental quality. During this reporting period, the two focus areas were ducted fuel injection (DFI) and surrogate diesel fuels.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Integrated 10 kV SiC VSD and High-Speed MW Motor for Gas Compression Systems

The objective of this project was to develop and test an integrated drive package including a 10 kV silicon carbide (SiC) - based variable-speed drive (VSD) and a high-speed megawatt-rated motor. This design was intended for gas compression applications that meet the objectives and technical targets of DE-FOA-0001208.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Degradation Studies of Silicone Rubbers in Direct Contact with Corrosive Solids Under Compression

Silicone elastomers are polymer materials widely used within the complex, serving as cushions, adhesives, binders, and potting compounds. Excellent high-temperature resistance combined with low-temperature flexibility, high resistance to chemicals and long-term age-related stability, make silicones first-choice materials in many application areas. However, the siloxane bonds comprising the backbone of silicone elastomers are susceptible to hydrolysis when catalytic amounts of acid or base are present. Hydrolysis is a form of degradation that results in chain-cleavage and rearrangement reactions, which can manifest in changes to the physical response and mechanical properties of the elastomer. This task aims to assess and quantify the extent of degradation and/or corrosion of silicone elastomer materials when placed in direct compressive contact with an Arrhenius base under varying environments (oxidative/nonoxidative, relative humidity, temperature, time, etc.).

36 MATERIALS SCIENCE↗