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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,477 records · Page 82
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Compression–tension cell with sample manipulator for in situ X‐ray nanotomography experiments

In situ X-ray nanotomography experiments where tensile or compressive force is applied on the sample require specialized equipment. A compression-tension device with fluid flow-through capability has been designed for X-ray nanotomography beamlines. The compression-tension cell is equipped with a triaxial stage for sample alignment and a high sensitivity loadcell for measurement of applied force. To handle the <100 µm samples used for X-ray nanotomography imaging and for loading samples on the compression-tension cell a sample manipulator has been built. The sample manipulator is capable of selecting a single <100 µm particle for nanotomography scanning while viewing multiple samples under an optical microscope. To test the functionality of these two devices an initial compression experiment involving two glass beads was performed. To demonstrate instrument stability two spherical glass beads were compressed from a no load condition until one of the beads fractured. Nanotomography data were collected at each step of increasing compressive force. The experimentally observed contact area of the spherical glass beads was compared with the theoretical estimate using the Hertz analysis. To demonstrate the fluid flow capability, two calcite grains were compressed against each other under a calcite saturated solution. Surface topological changes were observed for the stressed grain contact area.

X-ray tomography↗
NASA NTRS1990

Postbuckling behavior of curved panels under combined compression and shear loads

A validated semiempirical design procedure and fatigue data were developed for curved, stiffened composite panels operating in the postbuckled regime under the action of combined compression and shear loading. A previously developed design methodology for composite panels under pure shear or pure compression loading was used as the starting point for the program. Initially, the well established interaction rules for metal panels were adopted to predict buckling under combined loading. Test data were then developed to verify these rules and suggest modifications where necessary. Postbuckling failure envelopes were developed by accounting for the failure modes possible under shear loading only, and under pure compression loading. Static failure predictions under combined loading were based on test verified interaction criteria. Fatigue tests were conducted under combined loading to determine strength degradation and the possible failure modes.

Deo, Ravi B.↗
NASA NTRS1980

The Infrared Astronomical Satellite /IRAS/ Scientific Data Analysis System /SDAS/ sky flux subsystem

The sky flux subsystem of the Infrared Astronomical Satellite Scientific Data Analysis System is described. Its major output capabilities are (1) the all-sky lune maps (8-arcminute pixel size), (2) galactic plane maps (2-arcminute pixel size) and (3) regional maps of small areas such as extended sources greater than 1-degree in extent. The major processing functions are to (1) merge the CRDD and pointing data, (2) phase the detector streams, (3) compress the detector streams in the in-scan and cross-scan directions, and (4) extract data. Functional diagrams of the various capabilities of the subsystem are given. Although this device is inherently nonimaging, various calibrated and geometrically controlled imaging products are created, suitable for quantitative and qualitative scientific interpretation.

Stagner, J. R.↗
NASA NTRS2010

A New Compression Method for FITS Tables

As the size and number of FITS binary tables generated by astronomical observatories increases, so does the need for a more efficient compression method to reduce the amount disk space and network bandwidth required to archive and down1oad the data tables. We have developed a new compression method for FITS binary tables that is modeled after the FITS tiled-image compression compression convention that has been in use for the past decade. Tests of this new method on a sample of FITS binary tables from a variety of current missions show that on average this new compression technique saves about 50% more disk space than when simply compressing the whole FITS file with gzip. Other advantages of this method are (1) the compressed FITS table is itself a valid FITS table, (2) the FITS headers remain uncompressed, thus allowing rapid read and write access to the keyword values, and (3) in the common case where the FITS file contains multiple tables, each table is compressed separately and may be accessed without having to uncompress the whole file.

Pence, William↗
DOE OSTI2022

Physics-informed graph neural networks for predicting cetane number with systematic data quality analysis

Designing alternative fuels for advanced compression ignition engines necessitates a predictive model for cetane number (CN). In this study, the physics-informed graph neural networks are introduced for a reliable CN prediction by considering molecular features pertinent to the physical properties of molecules that affect CN. The reliability of measured data is another key factor to consider for improving the predictive model. Various experimental instruments for measuring CN exist, including standard and non-standard methods. In this regard, a systematic data quality analysis was carried out for the total 630 CNs collected from literature and new measurements in this study using Advanced Fuel Ignition Delay Analyzer (AFIDA). The results from this data curation process were reflected in the model by imposing lower sample weights on the data coming from less reliable measurement techniques. This approach effectively maximized the prediction accuracy while incorporating data from all available sources. Using the sample weights decreased the mean absolute error (MAE) up to 0.8 CN units. The accuracy was also improved by introducing the CN-related physical properties (the number of hydrogen bond donors and acceptors); the test set MAE is 5.74 and 7.01 for the model with and without such properties, respectively. Investigating molecular structural effects on CN was also carried out to gain chemical insights into factors used to design new fuel candidates. The dimensionality reduction analysis of feature vectors showed a clear clustering in terms of functional groups and CN and the structural effect derived from the model was consistent with the physicochemical insights. Finally, this physics-informed model and data curation would be helpful for accurate CN prediction and inform rational fuel design.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

EBSD Study on Processing Domain Parameters of Oxide Dispersion Strengthened 18Cr Ferritic Steel

This paper presents the results of an experimental study aimed to identify hot working domains in oxide dispersion strengthened (ODS) 18Cr ferritic steel over a wide range of temperatures (1323-1473 K) and strain rates (0.01-10 s{sup −1}). The experimental data were obtained by uniaxial compression test using the Gleeble-1500D simulator in this range of temperature and strain rate. An inverse relationship with temperature and positive strain rate sensitivity associated with dynamic recovery and recrystallization, which is influenced by temperature and strain rate, was derived from the flow stress. Based on the processing map generated at 0.5 true strain, using rate dynamic material model (DMM) approach and the calculated instability parameter (ξ(ϵ́))>0, the optimum processing domain has been determined for this steel. The most favorable processing parameters are found in the temperature ranges of 1350-1450 K with a strain rate of 0.01 s{sup −1} and 1473 K with a strain rate 0.1 s{sup −1} with peak efficiency of 30 and 35%, respectively. The material flow behavior was studied using scanning electron microscopy (SEM)-based EBSD microstructural characterization. The steel subjected to 1323 K at high strain rate 10 s{sup −1} in the low-efficiency workability region showed low aspect ratio as compared to the elongated bamboo-like initial microstructure; however, minimum strain rate (0.01 s{sup −1}) showing that localized slip/shearing is the key mechanism and fiber texture studied from the intensity distribution of inverse pole figure showed the presence of significant amount of θ-fibers. In contrast, dynamic recrystallization dominated at higher efficiency region in the safe domain of processing map and γ-fiber texture was evident in the specimen deformed at 1373 and 1473 K with strain rate of 0.01 and 0.1 s{sup −1}, respectively, which is responsible for the change in the initial 〈1 1 0〉//ED α-fiber texture.

36 MATERIALS SCIENCE↗
DOE OSTI2019

A Systematic Assessment of Various Stability/Instability Criteria in Predicting the Hot Deformation-Related Instabilities in Super-304H Stainless Steels

In this study, different existing stability/instability criteria (viz Prasad, Murty, Gegel, Alexander, Jonas, and Semiatin) are used to characterize the hot deformation instabilities of super-304H stainless steel. The instability maps are developed based on flow stress data acquired from uniaxial hot compression tests performed at various temperatures (1173-1423 K) and strain rates (0.001-10 s{sup −1}). The deformation instabilities are further validated through detailed microstructural observations. Flow localization and cracking have been identified as the signature of deformation instabilities in this alloy. The microcracks have been found to be associated with the grain boundary precipitation of coarse (~ 1- to 5-µm-sized) Nb-rich particles (mainly NbC). The instability criteria developed based on dynamic material modeling (i.e., Prasad’s and Murty’s criteria) could accurately predict the formation of microcracks in the entire hot deformation range employed in this study. Albeit the Gegel’s and Alexander’s stability criteria could correctly predict the flow localization, it has overestimated the deformation instabilities at high temperatures (> 1250 K) and low strain rates (≤ 0.1 s{sup −1}). In contrast, Jonas’ and Semiatin’s instability criteria have under-predicted the unstable domains in the studied alloy.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Anelasticity and Phase Transition During Ramp-Release in Tin

This study examines the qualitative features of an anelasticity model associated with the bowing of dislocations in the presence of phase transition. A simple physically plausible mechanism is introduced to describe the interaction of anelasticity and the transformation. Varying the anelastic parameters results in strong differences in the deviatoric stress response. The model is applied to study the behavior of tin (Sn) and compared to data from ramp driven compression-release experiments. Tin exhibits a complex phase diagram within a relatively accessible range of temperature and pressures and the characterization of its phases is considered an open problem with significant scientific merit. The coupling between anelasticity, plasticity, and phase transformation contributes to release wave features traditionally associated with the phase transition effect alone suggesting the importance of accounting for the effects jointly. Posterior distributions of the plastic and anelastic parameters are computed using Bayesian-inference-based methods, further highlighting the importance of anelasticity in this regime.

36 MATERIALS SCIENCE↗
DOE OSTI2018

The Influence of Intake Pressure and Ethanol Addition to Gasoline on Single- and Dual-Stage Autoignition in an HCCI Engine

Autoignition in HCCI engines is known to be controlled by the combustion kinetics of the in-cylinder fuel/air mixture which is highly influenced by the amount of low-temperature and intermediate-temperature heat release (LTHR and ITHR) that occurs. At lower intake pressures (typically <1.4 bar absolute), it has been observed that gasoline behaves as a single-stage heat release fuel, while at higher intake pressures (typically >1.8 bar absolute) gasoline behaves as a two-stage heat release fuel. Furthermore, ethanol blending into gasoline strongly affects heat release characteristics, and this warrants further investigation. This paper experimentally investigates the conditions under which gasoline transitions from a single-stage heat release fuel to a two-stage heat release fuel as intake pressure is increased. Experiments were performed in single-cylinder HCCI engine fueled with two research-grade gasolines, FACE A and FACE C. These gasolines were tested neat, and with 10% and 20% (by volume) ethanol addition. In addition, these results were compared to results previously obtained for PRF 85, and new results for PRF 84 with 10% and 20% ethanol addition. Moreover, the engine experiments were supported by rapid compression machine (RCM) ignition delay data for the same fuels. The engine experiments revealed that there were minimal differences between the heat release profiles of the two gasolines, FACE A and FACE C, a result which was supported by the RCM experiments that showed similar ignition delay times for the two FACE fuels and PRF 84. On the other hand, with ethanol addition to these gasolines and PRF 84, the occurrence of LTHR shifted to higher intake pressures compared to ethanol-free cases, from 1.4 bar intake pressure for neat fuel to 2.2 bar with 20% ethanol. Consequently, the intake temperatures required to achieve constant combustion phasing for all mixtures were drastically altered. Simulations using a detailed chemical kinetic model were utilized to understand the effects of ethanol blending on the ignition characteristics of PRF 84. The addition of ethanol was found to act as a radical sink where it inhibits the radical pool formation during the low (<850 K) and intermediate (850–1050 K) temperature chemistry regimes resulting in lower reactivity. These results help explain ethanol’s significant antiknock qualities under boosted conditions in spark-ignition engines.

42 ENGINEERING↗
DOE OSTI2026

Direct observation of diamond formation in a shock-compressed high explosive

Understanding the formation timescale and structure of carbonaceous reaction products is critical for modeling the high-pressure equation-of-state of organic materials. We use the National Ignition Facility to shock-compress polycrystalline TATB (C 6 H 6 N 6 O 6 ) samples to ~70–130 GPa and ~4000–5500 K, employing in situ nanosecond X-ray diffraction to probe reaction products and velocimetry to measure transmitted compression wave profiles. Our diffraction data is consistent with the formation of diamond over timescales less than ~60 ns. This represents carbon condensation from a molecular explosive on timescales three times faster than previously reported and the earliest observation of diamond produced from reacting TATB. Reactive flow simulations with explicit chemistry reproduce the observed temporal structure within wave profiles to inform the distribution of P-T states. These findings provide direct evidence of ultrafast diamond formation in a reactive system at extreme conditions and provide new constraints for models of shock and detonation chemistry.

Clarke, Samantha M. [Lawrence Livermore National L↗
DOE OSTI2023

BEYONDPLANCK III. Commander3

We describe the computational infrastructure for end-to-end Bayesian cosmic microwave background (CMB) analysis implemented by the BeyondPlanck Collaboration. The code is called Commander3. It provides a statistically consistent framework for global analysis of CMB and microwave observations and may be useful for a wide range of legacy, current, and future experiments. The paper has three main goals. Firstly, we provide a high-level overview of the existing code base, aiming to guide readers who wish to extend and adapt the code according to their own needs or re-implement it from scratch in a different programming language. Secondly, we discuss some critical computational challenges that arise within any global CMB analysis framework, for instance in-memory compression of time-ordered data, fast Fourier transform optimization, and parallelization and load-balancing. Thirdly, we quantify the CPU and RAM requirements for the current BEYONDPLANCK analysis, finding that a total of 1.5 TB of RAM is required for efficient analysis and that the total cost of a full Gibbs sample for LFI is 170 CPU-hrs, including both low-level processing and high-level component separation, which is well within the capabilities of current low-cost computing facilities. The existing code base is made publicly available under a GNU General Public Library (GPL) license.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Integration of RNTuple in ATLAS Athena

After using ROOT’s TTree I/O subsystem for over two decades and storing more than an exabyte of compressed High Energy Physics (HEP) data, advances in technology have motivated a complete redesign, RNTuple, which breaks backward-compatibility to take better advantage of these storage options. The RNTuple I/O subsystem has been designed to address performance bottlenecks and other shortcomings of TTree. Specifically, RNTuple comes with an updated, more compact binary data format that can be stored both in ROOT files and natively in object stores. It is designed for modern storage hardware (e.g. high-throughput low-latency NVMe SSDs), and provides robust and easy to use interfaces. The binary format of RNTuple is scheduled to become production grade in 2024, and recently has become mature enough to start exploring the integration into software used by HEP experiments. In this contribution, we discuss the developments to support the features as required by the ATLAS analysis Event Data Model (EDM) in RNTuple, which will enable its integration into the Athena software framework. With these developments in place, we evaluate the performance of the current most recent versions of RNTuple-based ATLAS data sets and compare this to that of TTree.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2018

The high pressure phase transformation behavior of silicon nanowires

Si nanowires of 80–150 nm and 200–250 nm diameter are pressurized up to 22 GPa using a diamond anvil cell. Raman and x-ray diffraction data were collected during both compression and decompression. Electron microscopy images reveal that the nanowires retain a nanowire-like morphology (after high pressure treatment). On compression, dc-Si was observed to persist at pressures up to 19 GPa compared to ~11 GPa for bulk-Si. On decompression, the metallic β-Sn phase was found to be more stable for Si nanowires compared with bulk-Si when lowering the pressure and was observed as low as 6 GPa. For the smallest nanowires studied (80–150 nm), predominately a-Si was obtained on decompression, whereas for larger nanowires (200–250 nm), clear evidence for the r8/bc8-Si phase was obtained. We suggest that the small volume of the individual Si nanowires compared with bulk-Si inhibits the nucleation of the r8-Si phase on decompression. This study shows that there is a size dependence in the high pressure behavior of Si nanowires during both compression and decompression.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Automated Fitting of a Semi-empirical Multiphase Equation of State for Carbon

The equation of state (EOS) of carbon is important in high explosive, geophysical, and inertial confinement fusion applications. Within the semi-empirical Sesame framework, the EOS of each phase is represented by a sum of cold, vibrational, and thermal electronic Helmholtz free energy contributions. Each phase has ~5-10 independent parameters that are adjusted to reproduce single-phase data (e.g., thermal expansion, isothermal compression) as well as experimentally- and computationally- derived phase boundaries. Manual calibration of the full multiphase EOS is arduous. We present our progress in development of automated EOS parameterization based on minimization of an objective function. Here, this function encodes deviation of model EOS results from experimental/computational benchmarks. Optimization is implemented as a combination of global (particle swarm) and local optimization techniques.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

CMB distance priors revisited: effects of dark energy dynamics, spatial curvature, primordial power spectrum, and neutrino parameters

As a physical and sufficient compression of the full CMB data, the CMB distance priors, or shift parameters, have been widely used and provide a convenient way to include CMB data when obtaining cosmological constraints. In this paper, we revisit this data vector and examine its stability under different cosmological models. We find that the CMB distance priors are an accurate substitute for the full CMB data when probing dark energy dynamics. This is true when the primordial power spectrum model is directly generalized from the power spectrum of the model used in the derivation of the distance priors from the CMB data. We discover a difference when a non-flat model with the untilted primordial inflation power spectrum is used to measure the distance priors. This power spectrum is a radical change from the more conventional tilted primordial power spectrum and violates fundamental assumptions for the reliability of the CMB shift parameters. We also investigate the performance of CMB distance priors when the sum of neutrino masses Σm ν and the effective number of relativistic species N eff are allowed to vary. Our findings are consistent with earlier results: the neutrino parameters can change the measurement of the sound horizon from CMB data, and thus the CMB distance priors. We find that when the neutrino model is allowed to vary, the cold dark matter density ω c and N eff need to be included in the set of parameters that summarize CMB data, in order to reproduce the constraints from the full CMB data. As a result, we present an updated and expanded set of CMB distance priors which can reproduce constraints from the full CMB data within 1σ, and are applicable to models with massive neutrinos, as well as non-standard cosmologies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Accelerating Ultrafast Spectroscopy with Compressive Sensing

We report ultrafast spectroscopy is an important tool for studying photoinduced dynamical processes in atoms, molecules, and nanostructures. Typically, the time to perform these experiments ranges from several minutes to hours depending on the choice of spectroscopic method. It is desirable to reduce this time overhead not only to shorten time and laboratory resources, but also to make it possible to examine fragile specimens that quickly degrade during long experiments. In this article, we motivate using compressive sensing to significantly shorten data acquisition time by reducing the total number of measurements in ultrafast spectroscopy. We apply this technique to experimental data from ultrafast transient absorption spectroscopy and ultrafast terahertz spectroscopy and show that good estimates can be obtained with as low as 15% of the total measurements, implying a sixfold reduction in the data acquisition time.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

BCSR on GPU: A Way Forward Extreme-scale Graph Processing on Accelerator-enabled Frontier Supercomputer

Handling large graphs in a distributed environment requires effective partitioning across processors and efficient management of local partitions. In 2D partitioning, local graphs often become too sparse, making memory-efficient data structures crucial. Using the Compressed Sparse Row (CSR) format wastes space, especially for > 83% of vertices with empty edges for the sparse graphs. This study explores bit-CSR (BCSR), a modified CSR representation, on GPUs to reduce memory usage in graph computations. We achieved 16.67% memory savings on a sparse rmat dataset with 268 million vertices and 357 million edges, without performance degradation, supported by both theoretical and experimental storage savings of 33%. However, we observed a 1.7× slowdown in degree lookup times due to bitwise operations on AMD CPUs. This analysis highlights the potential of BCSR on GPUs for improving Graph500 benchmark performance on GPU-accelerated systems, such as the Frontier supercomputer.

Sattar, Naw Safrin↗
DOE OSTI2023

Accelerating Random Forest Classification on GPU and FPGA

Random Forests (RFs) are a commonly used machine learning method for classification and regression tasks spanning a variety of application domains, including bioinformatics, business analytics, and software optimization. While prior work has focused primarily on improving performance of the training of RFs, many applications, such as malware identification, cancer prediction, and banking fraud detection, require fast RF classification. In this work, we accelerate RF classification on GPU and FPGA. In order to provide efficient support for large datasets, we propose a hierarchical memory layout suitable to the GPU/FPGA memory hierarchy. We design three RF classification code variants based on that layout, and we investigate GPU- and FPGA-specific considerations for these kernels. Our experimental evaluation, performed on an Nvidia Xp GPU and on a Xilinx Alveo U250 FPGA accelerator card using publicly available datasets on the scale of millions of samples and tens of features, covers various aspects. First, we evaluate the performance benefits of our hierarchical data structure over the standard compressed sparse row (CSR) format. Second, we compare our GPU implementation with cuML, a machine learning library targeting Nvidia GPUs. Third, we explore the performance/accuracy tradeoff resulting from the use of different tree depths in the RF. Finally, we perform a comparative performance analysis of our GPU and FPGA implementations. Our evaluation shows that for high accuracy targets, our GPU implementation yields 5-9x speedup over CSR, and up to a 2x speedup over cuML.

FPGA, Xilinx FPGA, GPU, Random Forest classificati↗