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At least 991 records · Page 55

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DOE OSTI2018

SLED-II: A New Method of RF Pulse Compression

In the SLED 1 method of RF pulse compression, two high Q resonators store energy from an RF source for a relatively long time interval (typically 3 to 5 J.1sec). Triggered by a reversal in RF phase, this stored energy is then released during a much shorter interval equal to the filling time of the accelerating structure. A peak power gain on the order of three and a compression efficiency on the order of 60% are typically attained. The shape of the output pulse is, however, a sharply decaying expo- nential. In SLED-II the two cavities are replaced by two lengths of resonant line, forming a Resonant Line SLED (RELS) and resulting in a flat output pulse. Therefore, RELS stages can be cascaded to give a greater peak power gain. Using two stages, a peak power gain greater than ten can be achieved with a reasonable compression efficiency. Unlike the BEC,2 the RELS compression fac- tor per stage is not limited to two, albeit at the expense of intrinsic efficiency. Like the BEC, it uses long lines rather than short cavities.

Farkas, Z.↗

DOE OSTI2019

Ignition, Flame Progation, and End-Gas Autoignition Studies of Natural Gas/ EGR Blends in a Rapid Compression Machine

The development of high efficiency, spark ignited natural gas engines is currently limited by engine knock at high power density (at high compression ratio and/or elevated boost pressures) and misfire (at lean conditions and/or high exhaust gas recirculation levels). The knock and misfire limits are further confounded by the wide variety in fuel reactivity observed in “pipeline quality” natural gas. In this study, a rapid compression machine (RCM) was used to characterize the effects of variation in natural gas fuel reactivity and exhaust gas recirculation (EGR) on homogeneous ignition delay, flame propagation rate and end-gas autoignition (EGAI) propensity for stoichiometric natural gas/oxidizer/EGR blends. Pipeline quality natural gas with variable reactivity (68 < Methane Number < 95) was simulated using mixtures of methane, ethane, and propane. EGR was simulated with mixtures of Ar, CO2, CO, and NO. EGR substitution rates were varied from 0 to 30 mass percent. Ignition delay period under homogeneous autoignition conditions was measured at compressed pressures of 24.0 to 33.1 bar and compressed temperatures of 667 to 980 K. Flame propagation rate and EGAI propensity was measured at spark-ignition pressures of 29.9 to 32.9 bar and temperatures of 721 to 782 K.

Mohr, Jeffrey↗

DOE OSTI2019

Flow Characterization of Compressed Woody Biomass

Global interest for using terrestrial biomass as an energy source has been increasing recently due to potentially significant socio-economic and environmental benefits relative to fossil energy. Thermochemical biomass processes converting biomass to fuels are relatively well developed, e.g. for direct combustion, gasification, and pyrolysis. However, critical problems often arise when attempting to feed biomass into commercial-scale reactors continuously and without interruption. Biomass particles, whether derived from woody or agricultural feedstock, vary greatly in size, shape, density, and moisture content. High cohesiveness, low bulk density, and poor material flow characteristics often prevent continuous feeding due to bridging, rat-hole formation, blockage, and seal failures. Among all different types of feeders utilized to feed biomass into reactors, screw feeding has received significant attention in recent years. Screw feeders are fuel-specific and require careful design to be reliable, efficient, and economical, especially for reactors operating at elevated pressures. In order to mimic the flow properties of compressed biomass inside a compression-screw feeder, rheological properties of three types of compressed lignocellulosic biomass were investigated using a co-rotating twin screw microcompounder. Experiments were conducted to find the impact of moisture content, particle size and type, and screw speed on rheological properties of forest residual particles as well as sawdust. Increasing moisture from 10 to 50% reduced the apparent viscosity of the compressed biomass. Also, higher screw speeds (10 to 90 rpm) led to a lower relative viscosity. At elevated screw speeds, significant temperature differences were measured between the initial and final states, which should be accounted for in equipment design and operation.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Predicting uniaxial compressive strength from empirical relationships between ultrasonic p-wave velocities, porosity, and core measurements in a potential geothermal reservoir, Snake River Plain, Idaho

Empirical, core-based, predictive correlations for the calculation of uniaxial compressive strength (UCS) were developed from compressive sonic velocity measurements (55 whole core samples) and porosity, density, and unconfined compressive tests (110 whole core samples). The samples were collected at 55 different depths in a 550 m (1800 ft) interval of core from the MH-2 borehole located in southern Idaho, USA. The western Snake River Plain is a known region of high heat flow and the borehole was drilled into a potential geothermal reservoir characterized by artesian flow of high-temperature (~140°C) fluids from fractured basalt. UCS was measured in unconfined compressive tests, density and porosity were measured using a He-pycnometer, and p-wave velocities were measured in a pressure vessel under variable confining pressures. We use correlations between density and p-wave velocity (R 2 = 0.91), UCS and porosity (R 2 = 0.78), and UCS and p-wave velocity (R 2 = 0.79) in a method to calculate calibrated UCS from wireline logs. Here, the impact of this predictive correlation is that UCS can be calculated from as little as a bulk density log when sonic logs and core are not available, greatly increasing the number of wells in which we can obtain a local UCS estimate.

15 GEOTHERMAL ENERGY↗

DOE OSTI2020

Byte select cache compression

Techniques are disclosed for designing cache compression algorithms that control how data in caches are compressed. The techniques generate a custom “byte select algorithm” by applying repeated transforms applied to an initial compression algorithm until a set of suitability criteria is met. The suitability criteria include that the “cost” is below a threshold and that a metadata constraint is met. The “cost” is the number of blocks that can be compressed by an algorithm as compared with the “ideal” algorithm. The metadata constraint is the number of bits required for metadata.

Das, Shomit N.↗

DOE OSTI2021

Data compression system using base values and methods thereof

In some embodiments, a memory controller in a processor includes a base value cache, a compressor, and a metadata cache. The compressor is coupled to the base value cache and the metadata cache. The compressor compresses a data block using at least a base value and delta values. The compressor determines whether the size of the data block exceeds a data block threshold value. Based on the determination of whether the size of the compressed data block generated by the compressor exceeds the data block threshold value, the memory controller transfers only a set of the compressed delta values to memory for storage. A decompressor located in the lower level cache of the processor decompresses the compressed data block using the base value stored in the base value cache, metadata stored in the metadata cache and the delta values stored in memory.

Seyedzadehdelcheh, SeyedMohammad↗

DOE OSTI2022

MODELLING OF ADDITIVE MANUFACTURING – COMPRESSION MOLDING PROCESS USING COMPUTATIONAL FLUID DYNAMICS

A computational fluid dynamics model has been developed to predict the behavior of a printed strands during a novel material extrusion additive manufacturing and compression molding process. While the traditional additive manufacturing process enables control over the fiber orientation within the part, it would also result in high void content. On the other hand, compression molding produces parts with low porosity levels at rapid processing cycle time but lacks control over the microstructure. The novel additive manufacturing – compression molding (AM-CM) integrating both these processes offers control over both the microstructure and porosity to manufacturing high performance composite parts. The numerical model developed here enables to analyze the effect of processing parameters on the behaviour of printed layer that is subsequently compressed and to determine the optimal printing parameters for high-performance composite part design.

Seta, Berin↗

DOE OSTI2022

Electrochemical Ammonia Compression

An electrochemical (EC) compressor is a solid-state compression device. For decades, researchers have been studying EC compressors for applications for a variety of energy systems. As the world transitions away from fossil fuel energy sources, EC compression emerges as a promising technique for energy storage, specifically energy stored in the form of pressurized ammonia. Ammonia is also a commonly used refrigerant. The present studies examine the viability of EC compression for ammonia storage and refrigeration. EC ammonia compression increases the concentration of an ammonia-hydrogen mixture via the input of electrical energy and a series of chemical reactions. A polymer electrolyte membrane separates the low-concentration side from the high-concentration side of the device. On the low-pressure (anode) side hydrogen atoms oxidize and react with ammonia molecules, forming positively charge ammonium ions. The ions traverse the membrane electrolytically. The ammonium ions are reduced and revert back to hydrogen and ammonia upon reaching the high-pressure (cathode). An external circuit provides the current needed to sustain the reactions. In this project, we studied the performance of the ammonia EC compressor under a variety of different conditions. We replicated preliminary data and using a small cell with 5 cm2 of active area. We demonstrated the operation of larger cells with 100 cm2 of active area. Further, we used a commercially available hydrogen EC compressor stack to analyze the scaled-up compressor performance. While previous experiments examined only the transient EC compressor performance, we developed test facilities that allowed the compressor to reach steady state. We analyzed the effects of pressure and current on the EC compressor performance. We measured the flow rates of gas leaving the compressors and analyzed the composition using gas chromatography. We tested methods of separating ammonia from the effluent vapor, which contained hydrogen and water vapor. We found that back diffusion adversely affected the performance, especially when we maintained high pressure lifts.

25 ENERGY STORAGE↗

DOE OSTI2023

A Lookup Table Based Low Resource Usage Waveform Data Compression Block in FPGA

aA lookup table based lossless data compression scheme suitable for FPGA implementation, with very low silicon resource usage, is developed. Different algorithms can be implemented by simply changing the contents of the lookup tables in the functional block while keeping the logic circuits unchanged. It exhibits performance comparable to the zip compression software in Linux distributions. The compression block can fit into almost any existing FPGA used in the online data acquisition systems of high energy physics experiments. The decompression process can be performed in either software or in FPGA firmware, allowing users to process compressed data flexibly.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

Data compression system using base values and methods thereof

Seyedzadehdelcheh, Seyed Mohammad↗

DOE OSTI2024

DETERMINATION OF STRUT QUALITY FACTORS IN ADDITIVELY MANUFACTURED LATTICES USING IN-SITU COMPRESSION TESTING µ-CT

In response to the need for an automated, commercial method to qualify additively manufactured (AM) lattice components, an experiment was conducted to evaluate the effects of defective lattice struts on the structural compression strength. Lattice samples with known defective or missing struts were compressed using a Deben CT5000RT and imaged using xray µ-CT. The compressive force and x-ray computed tomography results were compared to defect free standards to evaluate the impact of each defect type on the overall structure’s compressive strength. This analysis will allow for simplifications to Finite Element Analysis (FEA) on AM parts without sacrificing model fidelity. Understanding the contribution of each defect type and severity will also better inform non-destructive evaluation (NDE) personnel of the inspection parameters necessary to detect the smallest feature of importance.

Dinova, Vincent A.↗

DOE OSTI2023

Neural Image Compression: Generalization, Robustness, and Spectral Biases

Recent advances in neural image compression (NIC) have resulted in models which are starting to outperform traditional codecs. While this has led to growing excitement about using these methods in real-world applications, the successful adoption of any machine learning system (including NIC) in the wild requires it to generalize (and be robust) to unseen distribution shifts at deployment time. Unfortunately, current research lacks comprehensive datasets and informative tools to evaluate and understand compression performance in real-world settings. To bridge this crucial gap, first, this paper presents a comprehensive benchmark suite to evaluate the out-of-distribution (OOD) performance of image compression methods. Specifically, we design CLIC-C and Kodak-C by introducing 15 common corruptions to popular CLIC and Kodak benchmarks. Next, we propose spectrally inspired introspection tools to gain a deeper understanding of errors introduced by image compression methods as well as their OOD performance. To this end, we carry out a detailed performance comparison of the classical codec with various variants of NIC (e.g., original, variable rate, pruned), revealing intriguing findings that challenge our current understanding of the strengths and limitations of NIC. Finally, we corroborate our empirical findings with theoretical analysis, providing an in-depth view of the OOD performance of NIC. Our benchmarks, spectral introspection tools, and findings provide a crucial bridge to the real-world adoption of NIC. We hope that our work will propel future efforts in designing more robust and generalizable NIC methods.

neural networks, Variational Autoencoder, robustne↗

DOE OSTI2025

Dynamic Low-Rank Training with Spectral Regularization: Achieving Robustness in Compressed Representations

Deployment of neural networks on resource-constrained devices demands models that are both compact and robust to adversarial inputs. However, compression and adversarial robustness often conflict. In this work, we introduce a dynamical low-rank training scheme enhanced with a novel spectral regularizer that controls the condition number of the low-rank core in each layer. This approach mitigates the sensitivity of compressed models to adversarial perturbations without sacrificing clean accuracy. The method is model- and data-agnostic, computationally efficient, and supports rank adaptivity to automatically compress the network at hand. Extensive experiments across standard architectures, datasets, and adversarial attacks show the regularized networks can achieve over 94\% compression while recovering or improving adversarial accuracy relative to uncompressed baselines.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗

DOE OSTI2025

Dynamical Low-Rank Compression of Neural Networks with Robustness under Adversarial Attacks

Deployment of neural networks on resource-constrained devices demands models that are both compact and robust to adversarial inputs. However, compression and adversarial robustness often conflict. In this work, we introduce a dynamical low-rank training scheme enhanced with a novel spectral regularizer that controls the condition number of the low-rank core in each layer. This approach mitigates the sensitivity of compressed models to adversarial perturbations without sacrificing clean accuracy. The method is model- and data-agnostic, computationally efficient, and supports rank adaptivity to automatically compress the network at hand. Extensive experiments across standard architectures, datasets, and adversarial attacks show the regularized networks can achieve over 94 compression while recovering or improving adversarial accuracy relative to uncompressed baselines.

Schotthoefer, Steffen [ORNL] (ORCID:00000002156965↗

DOE OSTI2021

Meso-scale modelling of compressive fracture in concrete with irregularly shaped aggregates

This paper presents a meso-scale modelling framework to investigate the fracture process in concrete subjected to uniaxial and biaxial compression accounting for its mesostructural characteristics. 3D mesostructure of concrete consisting of coarse aggregates, mortar and interfacial transition zone between them was developed using an in-house code based on the Voronoi tessellation and splining method, which enables to generate the realistic-look aggregates with controllable structural features such as content, location, size and shape. Based on the generated 3D mesostructure, the concrete damage plasticity approach was employed to simulate the compressive fracture behaviour of concrete in terms of crack morphology and stress-strain response against the shape parameters of aggregate. Results indicate that the shape of aggregate has a negligible effect on compressive strength of concrete, which is highly associated with the random location and size distribution of aggregate. The aggregate irregularity has a significant influence on crack initiation and growth of concrete.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Multifacets of lossy compression for scientific data in the Joint-Laboratory of Extreme Scale Computing

The Joint Laboratory on Extreme-Scale Computing (JLESC) was initiated at the same time lossy compression for scientific data became an important topic for the scientific communities. The teams involved in the JLESC played and are still playing an important role in developing the research, techniques, methods, and technologies making lossy compression for scientific data a key tool for scientists and engineers. Here, in this paper, we present the evolution of lossy compression for scientific data from 2015, describing the situation before the JLESC started, the evolution of this discipline in the past 8 years (until 2023) through the prism of the JLESC collaborations on this topic and some of the remaining open research questions.

Compression for AI↗

DOE OSTI2015

High-pressure crystallography and compression behavior of the alkali-scandium-germanate end-members LiScGe{sub 2}O{sub 6} and NaScGe{sub 2}O{sub 6}

Synthetic single-crystal samples of the monoclinic pyroxene-type compounds LiScGe{sub 2}O{sub 6} and NaScGe{sub 2}O{sub 6} have been investigated by means of in-situ high-pressure Raman spectroscopy and X-ray diffraction techniques. Crystal-structure investigations at hydrostatic high-pressure conditions were carried out in addition to 10{sup −4} GPa measurements using the diamond-anvil cell technique up to ∼9.5 GPa. Both samples, LiScGe{sub 2}O{sub 6} (space group P2{sub 1}/c, a=9.9999(8) Å, b=9.1040(2) Å, c=5.4610(2) Å, β=109.240(2)° at 10{sup −4} GPa) and NaScGe{sub 2}O{sub 6} (space group C2/c, a=10.1678(5) Å, b=9.1583(5) Å, c=5.5672(3) Å, β=107.257(3)° at 10{sup −4} GPa), did not undergo any compression-induced change of symmetry as confirmed by single-crystal diffraction patterns. Series of high-pressure Raman spectra confirm the absence of any symmetry-related structural phase transition with band positions shifting in a smooth fashion on increasing pressure. High-precision lattice parameters, as determined from X-ray diffraction profile analyzes, were used to monitor both the compressibilities of lattice directions and the evolution of the unit-cell volume with pressure. The experimental data was fitted using a third-order Birch–Murnaghan equation-of-state approach and parameterized fits yield V{sub 0}=469.60(11) Å{sup 3}, K{sub 0}=85.6(1.3) GPa and K′=5.9(4) for LiScGe{sub 2}O{sub 6} and V{sub 0}=495.95(19) Å{sup 3}, K{sub 0}=79(3) GPa and K′=3.9(1.1) for NaScGe{sub 2}O{sub 6}. The systematic comparison of volume properties and compression behavior shows that both pyroxene-type compounds follow established trend lines with similar slopes in comparing volumes versus bulk moduli, and confirm that the position of trend lines in this systematics describing variations with M1 critically depend on the type of M2 and T cations, independent on the space-group symmetry. - Graphical abstract: Polyhedral model of the LiScGe{sub 2}O{sub 6} crystal structure at 9.51 GPa. Red and blue: crystallographic distinctive germanium-tetrahedral chains; Green: scandium-octahedral chains; Purple: lithium atoms. - Highlights: • High-quality high-pressure data of previously uninvestigated germanate compounds. • Results confirmed through complementing sXRD with Raman spectroscopy. • Comparison and relation of high-pressure pyroxene-type compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Estimating Lossy Compressibility of Scientific Data Using Deep Neural Networks

Simulation based scientific applications generate increasingly large amounts of data on high-performance computing (HPC) systems. To allow data to be stored and analyzed efficiently, data compression is often utilized to reduce the volume and velocity of data. However, a question often raised by domain scientists is the level of compression that can be expected so that they can make more informed decisions, balancing between accuracy and performance. In this letter, we propose a deep neural network based approach for estimating the compressibility of scientific data. To train the neural network, we build both general features as well as compressor-specific features so that the characteristics of both data and lossy compressors are captured in training. Our approach is demonstrated to outperform a prior analytical model as well as a sampling based approach in the case of a biased estimation, i.e., for SZ. However, for the unbiased estimation (i.e., ZFP), the sampling based approach yields the best accuracy, despite the high overhead involved in sampling the target dataset.

97 MATHEMATICS AND COMPUTING↗