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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Ta compressibility to 20+ Mbar

Developing techniques for experimentally constraining equation of state (EOS) models for important programmatic materials under extreme conditions is vital for advancing our modeling and predictive capabilities. Tantalum is a frequently used standard material for both calibration and testing, and this report describes our work to both measure Ta compressibility to very high pressures and densities using the Ramp Compression Equation of State platform on the National Ignition Facility (NIF). Using a series of seven shots, increasing in peak pressure with each shot, we have made absolute measurements of the compressibility of Ta along the ramp compression path up to 2.3 TPa. Previous experimental measurements constrained the cold compressibility up to ~400 GPa, and there is a spread in the EOS models above that pressure. To improve communication and collaboration between the experimental team and the EOS development group, these new data, along with other experimental and theoretical constraints, were used to develop a Ta equation of state table, M73000. This report first describes the experimental measurements made at the NIF, and then discusses the construction of the EOS model. The ramp compression data can be found in tabular form in Appendix A. Appendix B contains some additional experimental details. In Appendix C we include some additional details on the EOS modeling. At the end of this document, we have attached a detailed report on the NIF ramp compression platform itself and in the main text referenced specific sections for additional details.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Device and method for cache utilization aware data compression

A processing device is provided which includes memory and at least one processor. The memory includes main memory and cache memory in communication with the main memory via a link. The at least one processor is configured to receive a request for a cache line and read the cache line from main memory. The at least one processor is also configured to compress the cache line according to a compression algorithm and, when the compressed cache line includes at least one byte predicted not to be accessed, drop the at least one byte from the compressed cache line based on whether the compression algorithm is determined to successfully compress the cache line according to a compression parameter.

Das, Shomit N.↗
DOE OSTI2021

Memory hierarchy using page-based compression

A system includes a device coupleable to a first memory. The device includes a second memory to cache data from the first memory. The second memory is to store a set of compressed pages of the first memory and a set of page descriptors. Each compressed page includes a set of compressed data blocks. Each page descriptor represents a corresponding page and includes a set of location identifiers that identify the locations of the compressed data blocks of the corresponding page in the second memory. The device further includes compression logic to compress data blocks of a page to be stored to the second memory and decompression logic to decompress compressed data blocks of a page accessed from the second memory.

Loh, Gabriel H.↗
DOE OSTI2024

Development of a near-isothermal transcritical CO 2 compression system with a liquid piston compressor

Compressors are critical components in vapor compression cycle systems, significantly contributing to energy consumption. As global demand for HVAC&R systems rises, enhancing compressor efficiency becomes increasingly vital. This paper introduces a novel liquid piston compressor integrated with a gas cooler for the transcritical CO 2 refrigeration cycle. The liquid piston enables CO 2 refrigerant compression within various types of heat exchangers, facilitating the transfer of compression heat to the heat transfer fluid. By releasing significant heat, this compressor allows for removing or downsizing the traditional gas cooler in the refrigeration system. This paper presents the experimental performance of the first near-isothermal compressor utilizing a liquid piston in a vapor compression cycle. The critical parameters affecting heat transfer are analyzed by using a 1-D simulation model to achieve a near-isothermal compression process. The results show that the developed prototype successfully reduced the compression temperature increase from 95 K to 10 K, achieving 90 % isothermal efficiency. Furthermore, the 1-D simulation results suggest the smaller internal diameter tubes benefit the isothermal efficiency the most.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

A review on nearly isothermal compression technology

Compressors can be described as the heart of a vapor compression refrigeration system, circulating the working fluid in the system. Significant benefits can be gained from improving its efficiency since energy consumption is responsible for almost the entire system's power input. An effort has been made to achieve isothermal compression through liquid refrigerant injection or inter-stage cooling in refrigeration systems. In recent years, much effort has been invested into isothermal compression technology for air compressors or compressed air energy storage systems with the rise of renewable energy. Here, this work has explored the advancements made in this area and categorized them to discuss the benefits, limitations, and tradeoffs. Evidently, increasing heat transfer area is a significant aid in improving heat transfer, resulting in better isothermal compression. However, liquid pistons open more avenues for this to be pursued, and therefore, are limited to air compression applications. In addition, water injection proved to be the most effective among the methods. Finally, there is a need for more experimental research on refrigeration applications and the field is still in its infancy.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Lossy Compression: An Online Multi-Stage Technology for High-Fidelity Synchro- Waveform Measurements

Effective real-time monitoring and analysis of distributed grids necessitate the use of synchro-waveform measurements, which capture almost all high-frequency disturbances and transient phenomena. However, due to limitations in high-speed measurements and network bandwidth, it is challenging to transfer all high-fidelity synchro-waveforms losslessly and successfully. To cope with these challenges, a hybrid-based online multi-stage compression algorithm is proposed to significantly improve the compression efficiency for synchro-waveform measurements. Initially, the multiple discrete Wavelet transformation is deployed to deconstruct the waveform components. The delta encoding is further developed to decrease the magnitude. In conjunction with the Lempel-Ziv-Markov chain, the hybrid compression algorithm is implemented to achieve real-time compression for the synchro-waveform measurements. Moreover, an innovative error index that synergizes the time and frequency domain error and correlation is formulated to evaluate the waveform distortion. By integrating compression ratio, suitable parameters can be optimally selected. Finally, the simulation, laboratory experiments, as well as field tests across a spectrum of sampling frequencies and time intervals are conducted to substantiate the efficacy of the proposed method. Here, the outcomes demonstrated that a compression ratio of approximately 15.5 and 17.83 can be reached for 0.5 s and 1 s data under both offline and online scenarios, which equates to a substantial 93.5% to 94.39% reduction in data storage requirements.

High-fidelity synchro-waveform measurements↗
DOE OSTI2026

Enhancing ZFP: A Statistical Approach to Understanding and Reducing Error Bias in a Lossy Floating-Point Compression Algorithm

The amount of data generated and gathered in scientific simulations and data collection applications is continuously growing, putting mounting pressure on storage and bandwidth concerns. A means of reducing such issues is data compression; but, lossless data compression is typically ineffective when applied to floating-point data. Thus, users tend to apply a lossy data compressor, which allows for small deviations from the original data. It is essential to understand how the error from lossy compression impacts the accuracy of the data analytics. Thus, we must analyze not only the compression properties but the error as well. In this paper, we provide a statistical analysis of the error caused by ZFP compression, a state-of-the-art, lossy compression algorithm explicitly designed for floating-point data. We show that the error is indeed biased and propose simple modifications to the algorithm to neutralize the bias and further reduce the resulting error.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quasi-brittle porous material: Simulated effect of stochastic air void structure on compressive strength

Highlights: • A simulation procedure to study the effect of porosity on the compressive strength of a quasi-brittle material is introduced • Based on the simulated results, the relationship between compressive strength and air void fraction (porosity) is presented. • A linear relationship was achieved between the cubic root of porosity and the simulated compressive strength. The effect of porosity comprised of spherical air voids on the compressive strength of quasi-brittle material was studied via simulations. The simulated porous structures were based on pore size distributions of two mortar samples measured by X-ray microtomography. While the simulation method set practical limits on the size of sample, the base of the statistics was established by simulating 128 small structures generated by sampling from pore structures of two mortars. By studying the application of the classical strength-porosity formulas to the simulated data, a new simple model was formed. A linear relationship was achieved between the cubic root of air void fraction (porosity) and the simulated compressive strength. The reasons for scattering of simulated strength around fitted trend remained unresolved in this study; no clear dependence on pore number or other distribution properties was observed. With the presented simulation approach, the dependence of compressive strength on porosity is achieved independently of disturbances that occur in experimental studies creating understanding of compressional behavior of low porosity materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Effect of moderate temperatures on compressive strength of ultra-high-performance concrete: A microstructural analysis

Highlights: • All coarse aggregates and steel fibres were surrounded by the binder. • X-ray diffraction is useful for explaining the increase in compressive strength with increasing temperature. • Rosenhahnite and/or quartz Dauphiné-twinned phases improved the compressive strength. • Polypropylene fibres prevented spalling and preserved the compressive strength. • Concrete with steel and polypropylene fibres did not exhibit spalling at 300 °C. Concrete with two types of steel fibres and a polypropylene fibre prevented spalling and preserved the compressive strength at 300 °C, which makes these concretes suitable for long-term applications up to 300 °C, such as for steam collectors or thermal energy storage systems. The compressive strength behaviour of three types of ultra-high-performance fibre-reinforced concrete manufactured with the same matrix was investigated. For this purpose, a complete characterisation of all the raw materials and the three types of fibres used was performed. The morphology of all concrete mixtures at room temperature was analysed using scanning electron microscopy–energy-dispersive X-ray spectroscopy. From the results, it was ascertained that the steel fibres and coarse siliceous aggregates were not in contact (being separated by ≥3.41 μm) and were surrounded by the binder (of ≥1 μm in thickness) for all the mixtures studied. Rosenhahnite and/or quartz Dauphiné-twinned phases improved the compressive strength (as determined by X-ray diffraction).

36 MATERIALS SCIENCE↗
DOE OSTI2020

Pass-efficient methods for compression of high-dimensional turbulent flow data

The future of high-performance computing, specifically on future Exascale computers, will presumably see memory capacity and bandwidth fail to keep pace with data generated, for instance, from massively parallel partial differential equation (PDE) systems. Current strategies proposed to address this bottleneck entail the omission of large fractions of data, as well as the incorporation of in situ compression algorithms to avoid overuse of memory. To ensure that post-processing operations are successful, this must be done in a way that a sufficiently accurate representation of the solution is stored. Moreover, in situations where the input/output system becomes a bottleneck in analysis, visualization, etc., or the execution of the PDE solver is expensive, the number of passes made over the data must be minimized. In the interest of addressing this problem, this work focuses on the utility of pass-efficient, parallelizable, low-rank, matrix decomposition methods in compressing high-dimensional simulation data from turbulent flows. Additionally, a particular emphasis is placed on using coarse representation of the data – compatible with the PDE discretization grid – to accelerate the construction of the low-rank factorization. This includes the presentation of a novel single-pass matrix decomposition algorithm for computing the so-called interpolative decomposition. The methods are described extensively and numerical experiments on two turbulent channel flow data are performed. In the first (unladen) channel flow case, compression factors exceeding 400 are achieved while maintaining accuracy with respect to first- and second-order flow statistics. In the particle-laden case, compression factors of 100 are achieved and the compressed data is used to recover particle velocities. These results show that these compression methods can enable efficient computation of various quantities of interest in both the carrier and disperse phases.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Variable rate neural compression for sparse detector data

Particle colliders produce data at extraordinary rates, posing major challenges for transmission and storage. High-throughput compression algorithms are therefore essential. In the sPHENIX experiment taking data at the Relativistic Heavy Ion Collider, a time projection chamber records three-dimensional (3D) particle trajectories that are highly sparse, making conventional learning-free lossy compression ineffective. Convolutional neural networks have surpassed traditional methods in compression ratio and accuracy. However, they fail to exploit sparsity for efficiency. To address these gaps, we present BCAE-VS, a bicephalous convolutional autoencoder with variable compression ratio for sparse data, which adapts compression to input complexity through key-point identification and sparse convolution. BCAE-VS achieves higher accuracy and compression ratios than prior neural approaches while being orders of magnitude smaller. Moreover, its throughput increases with sparsity—a property not observed in other methods. Although it was developed for collider experiments, BCAE-VS readily extends to other sparse data domains, such as light detection and ranging (LiDAR) sensing and 3D microscopy.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Structural Transformation and Chemical Stability of a Shock-Compressed Insensitive High Explosive Single Crystal: Time-Resolved Raman Spectroscopy

Despite the considerable interest in insensitive high explosives (IHE) as a safer alternative to conventional high explosives, a good understanding of the low sensitivity of IHEs to shock initiation is lacking. In particular, real-time measurements to directly probe the molecular-level response of shock compressed IHE single crystals constitute an important need. To address this need, plate impact experiments were conducted to determine time-resolved changes in the Raman spectra of 1,1-diamino-2,2-dinitroethene (FOX-7) single crystals – a representative IHE crystal – shock compressed up to 20 GPa longitudinal stress. The Raman measurements examined vibrational frequencies from 800 cm-1 to 1500 cm-1 with 15 nanosecond time resolution and were conducted at several peak stresses. At 4 – 6 GPa, two new Raman peaks appeared, in addition to the original peaks, consistent with onset of the α' – ε structural transformation reported previously in static compression work. The measured spectra indicated completion of the transformation at 10 GPa. Raman data to 20 GPa showed neither additional transformations nor any indication of chemical decomposition. This finding, though consistent with recent continuum measurements, is in marked contrast to the chemical decomposition observed at lower stresses in shock compressed conventional high explosive single crystals. Our Raman results support the previous suggestion that strengthening of intra- and inter-molecular bonds, due to the α' – ε structural transformation, plays a significant role in the insensitivity of FOX-7 single crystals to shock initiation. In conclusion, the present work, in conjunction with previous static compression studies, provides the first experimental insight into the molecular-level response of a shock compressed IHE single crystal and can serve as a bench mark for theoretical studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Apparatus for measuring strength in biaxial compression

We report most measurements of compressive strength of ductile materials have involved Hopkinson–Kolsky bars or Taylor anvils placing samples in uniaxial compression. In these geometries, strain is limited by the tendency of the sample to petal, in analogy to necking in uniaxial tension. Estimation of strength for any other form of the stress tensor requires assuming a shape of the yield surface; because data exist only for uniaxial compression, these assumptions are untested. In an imploding spherical shell, compression is biaxial, the plastic strain may not be small, and the material behavior may be nonlinear as a result of work hardening and heating by plastic work. We outline a method of measuring the strengths of materials in biaxial compression, both quasistatically and dynamically, using the compression of thin spherical shells. We suggest surrounding the shell with an annulus filled with a mixture of H 2 and Cl 2 gases whose homogeneous ignition is initiated by a flash of blue and near-ultraviolet light. Less promising approaches are described in Appendixes A–C .

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

Effect of pressure cycling and compression rate on the bcc-hcp transition in an FeNi alloy

Here, we investigate the body-centered cubic (bcc) to hexagonal close-packed (hcp) phase transition in Fe-10wt. %Ni alloy, combining pressure cycling and fast compression with time-resolved synchrotron x-ray diffraction in a dynamic diamond anvil cell. Three pressure cycles were conducted with compression rates ranging from 0.1 to nearly 10 3 GPa/s. During the first cycle with the slowest compression, the observed orientations in the bcc and hcp phases are consistent with the Burgers mechanism, followed by c-axis rotation of the hcp phase consistent with {$10$ $\bar{1}2$} twinning. During the following cycles with fast compression at 10 2 –10 3 GPa/s, the hcp phase exhibits negligible c-axis rotation with a nearly constant c/a ratio of ∼1.61 up to ∼30 GPa, indicating suppression of plastic deformation (especially, twinning) due to sample confinement from the gasket. Notably, the onset pressure of the transition decreases with additional pressure cycling and faster compression, which normally leads to over-pressurization. This suggests that defects or shear induced from the pressure cycling reduces the transition kinetics even during fast compression. These insights into the deformation and transition behavior in an FeNi alloy under multiple dynamic loading cycles can offer guidance for future design of advanced structural alloys and improve our understanding of planetary core processes.

FeNi alloy↗
DOE OSTI2020

Deep learning for in situ data compression of large turbulent flow simulations

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.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A MHz X-ray diffraction set-up for dynamic compression experiments in the diamond anvil cell

An experimental platform for dynamic diamond anvil cell (dDAC) research has been developed at the High Energy Density (HED) Instrument at the European X-ray Free Electron Laser (European XFEL). Advantage was taken of the high repetition rate of the European XFEL (up to 4.5 MHz) to collect pulse-resolved MHz X-ray diffraction data from samples as they are dynamically compressed at intermediate strain rates (≤10 3 s −1 ), where up to 352 diffraction images can be collected from a single pulse train. The set-up employs piezo-driven dDACs capable of compressing samples in ≥340 µs, compatible with the maximum length of the pulse train (550 µs). Results from rapid compression experiments on a wide range of sample systems with different X-ray scattering powers are presented. A maximum compression rate of 87 TPa s −1 was observed during the fast compression of Au, while a strain rate of ∼1100 s −1 was achieved during the rapid compression of N 2 at 23 TPa s −1 .

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Spaced Orthocyclic Winding Pattern for Improved Die Compressed Coils

Winding fill, coil fill, or packing factor, is critical to the performance of many types of electromagnetic devices from electric machines to actuators, transformers, inductors, etc. One technique to increase the packing of conductors in the winding area is to compress round magnet wire conductors in a die, so that they deform and air voids between coil turns are removed. The winding layout of the coil turns on a bobbin before compression has a direct impact on the achievable coil fill and the damage to the coil insulation or conductors after compression. It is thought that winding the turns in an orthocyclic pattern on the bobbin before compression would minimize damage to the turns. This however causes turns to displace during the winding compression process. A new pre-compression winding pattern is proposed, a spaced orthocyclic winding, to minimize turn displacement and damage. A design of experiments study has been carried out to optimize this winding method and to compare it to the traditional orthocyclic winding.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Efficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder

Real-time data collection and analysis in large experimental facilities present a great challenge across multiple domains, including high energy physics, nuclear physics, and cosmology. To address this, machine learning (ML)-based methods for real-time data compression have drawn significant attention. However, unlike natural image data, such as CIFAR and ImageNet that are relatively small-sized and continuous, scientific data often come in as three-dimensional 3D data volumes at high rates with high sparsity (many zeros) and non-Gaussian value distribution. This makes direct application of popular ML compression methods, as well as conventional data compression methods, suboptimal. To address these obstacles, this work introduces a dual-head autoencoder to resolve sparsity and regression simultaneously, called Bicephalous Convolutional AutoEncoder (BCAE). This method shows advantages both in compression fidelity and ratio compared to traditional data compression methods, such as MGARD, SZ, and ZFP. To achieve similar fidelity, the best performer among the traditional methods can reach only half the compression ratio of BCAE. Moreover, a thorough ablation study of the BCAE method shows that a dedicated segmentation decoder improves the reconstruction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗