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At least 721 records · Page 40

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Development and characterization of a low-density TiNbZrAlTa refractory high entropy alloy with enhanced compressive strength and plasticity

A new refractory high entropy alloy (RHEA) is designed by combining refractory elements Nb and Ta with Ti, Zr, and Al, resulting in a (TiNbZr) 89 (AlTa) 11 alloy composition with a low-density of 6.0 g/cm 3 . The novel RHEA features a BCC matrix with B2 and Zr 5 Al 3 nano precipitates, exhibiting a compressive yield strength of ~890 MPa and specific yield strength of ~148.3 MPa g -1 cm 3 . Remarkably, the alloy demonstrates excellent compressive plasticity of ~70% at 298 K. As the temperature increases to 873 K, 1073 K, and 1273 K, the yield strength of the RHEA gradually decreases to ~610 MPa, ~ 210 MPa, and ~ 60 MPa, respectively. These results indicate that this novel RHEA showcases a combination of good specific strength and plasticity. Post-compression, B2, and Zr 5 Al 3 phases are observed. In conclusion, the second-phase particles dispersed throughout the grains hinder dislocation motion and contribute to the formation of sub-grain boundaries during hot compression.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Negative linear compressibility in common materials

Negative linear compressibility (NLC) is still considered an exotic property, only observed in a few obscure crystals. The vast majority of materials compress axially in all directions when loaded in hydrostatic compression. However, a few materials have been observed which expand in one or two directions under hydrostatic compression. At present, the list of materials demonstrating this unusual behaviour is confined to a small number of relatively rare crystal phases, biological materials, and designed structures, and the lack of widespread availability hinders promising technological applications. Using improved representations of elastic properties, this study revisits existing databases of elastic constants and identifies several crystals missed by previous reviews. More importantly, several common materials-drawn polymers, certain types of paper and wood, and carbon fibre laminates-are found to display NLC. We show that NLC in these materials originates from the misalignment of polymers/fibres. Using a beam model, we propose that maximum NLC is obtained for misalignment of 26°. The existence of such widely available materials increases significantly the prospects for applications of NLC.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Optimizing Lossy Compression Rate-Distortion from Automatic Online Selection between SZ and ZFP

With ever-increasing volumes of scientific data produced by high-performance computing applications, significantly reducing data size is critical because of limited capacity of storage space and potential bottlenecks on I/O or networks in writing/reading or transferring data. SZ and ZFP are two leading BSD licensed open source C/C++ libraries for compressed floating-point arrays that support high throughput read and write random access. However, their performance is not consistent across different data sets and across different fields of some data sets, which raises the need for an automatic online (during compression) selection between SZ and ZFP, with minimal overhead. In this paper, the automatic selection optimizes the rate-distortion, an important statistical quality metric based on the signal-to-noise ratio. To optimize for rate-distortion, we investigate the principles of SZ and ZFP. We then propose an efficient online, low-overhead selection algorithm that predicts the compression quality accurately for two compressors in early processing stages and selects the best-fit compressor for each data field. Here, we implement the selection algorithm into an open-source library, and we evaluate the effectiveness of our proposed solution against plain SZ and ZFP in a parallel environment with 1,024 cores. Evaluation results on three data sets representing about 100 fields show that our selection algorithm improves the compression ratio up to 70 percent with the same level of data distortion because of very accurate selection (around 99 percent) of the bestfit compressor, with little overhead (less than 7 percent in the experiments).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Compressed glassy carbon: An ultrastrong and elastic interpenetrating graphene network

Carbon’s unique ability to have both sp 2 and sp 3 bonding states gives rise to a range of physical attributes, including excellent mechanical and electrical properties. We show that a series of lightweight, ultrastrong, hard, elastic, and conductive carbons are recovered after compressing sp 2 -hybridized glassy carbon at various temperatures. Compression induces the local buckling of graphene sheets through sp 3 nodes to form interpenetrating graphene networks with long-range disorder and short-range order on the nanometer scale. The compressed glassy carbons have extraordinary specific compressive strengths—more than two times that of commonly used ceramics—and simultaneously exhibit robust elastic recovery in response to local deformations. Finally, this type of carbon is an optimal ultralight, ultrastrong material for a wide range of multifunctional applications, and the synthesis methodology demonstrates potential to access entirely new metastable materials with exceptional properties.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Exploring the feasibility of lossy compression for PDE simulations

Checkpoint restart plays an important role in high-performance computing (HPC) applications, allowing simulation runtime to extend beyond a single job allocation and facilitating recovery from hardware failure. Yet, as machines grow in size and in complexity, traditional approaches to checkpoint restart are becoming prohibitive. Current methods store a subset of the application’s state and exploit the memory hierarchy in the machine. However, as the energy cost of data movement continues to dominate, further reductions in checkpoint size are needed. Lossy compression, which can significantly reduce checkpoint sizes, offers a potential to reduce computational cost in checkpoint restart. This article investigates the use of numerical properties of partial differential equation (PDE) simulations, such as bounds on the truncation error, to evaluate the feasibility of using lossy compression in checkpointing PDE simulations. Restart from a checkpoint with lossy compression is considered for a fail-stop error in two time-dependent HPC application codes: PlasComCM and Nek5000. Results show that error in application variables due to a restart from a lossy compressed checkpoint can be masked by the numerical error in the discretization, leading to increased efficiency in checkpoint restart without influencing overall accuracy in the simulation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

StimuFrac Compressibility as a Function of CO2 Molar Fraction

Compressibility values were obtained in a range of pressures at 250degC by employing a fixed volume view cell completely filled with PAA aqueous solution and injecting CO2 at constant flow rate (0.3mL/min). Pressure increase as a function of supercritical CO2 (scCO2) mass fraction in the mixture was monitored. The plot shows the apparent compressibility of Stimufrac as a function of scCO2 mass fraction obtained in a pressure range between 2100-7000 psi at 250degC. At small mass fractions of scCO2 the compressibility increases probably due to the dissolution/reaction of CO2 in aqueous PAA and reaches a maximum at mCO2/mH2O = 0.06. Then, compressibility decreases showing a linear relationship with scCO2 mass fraction due to the continuous increase in density of the binary fluid associated to the pressure increase.

15 GEOTHERMAL ENERGY↗

DOE OSTI2015

A Framework of Hyperspectral Image Compression using Neural Networks

Hyperspectral image analysis has gained considerable attention due to its wide range of applications. Hyperspectral images provide a vast amount of information about underlying objects in an image by using a large range of the electromagnetic spectrum for each pixel. However, since the same image is taken multiple times using distinct electromagnetic bands, the size of such images tend to be significant, which leads to greater processing requirements. The aim of this paper is to present a proposed framework for image compression and to study the possible effects of spatial compression on quality of unmixing results. Image compression allows us to reduce the dimensionality of an image while still preserving most of the original information, which could lead to faster image processing. This paper presents preliminary results of different training techniques used in Artificial Neural Network (ANN) based compression algorithm.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2022

Application of Compressive Sensing for Gravitational Microlensing Events

Compressive sensing is a novel mathematical technique for simultaneous data acquisition and compression. This technique is particularly apt for time-series photometric measurements. In this work we apply compressive sensing to time-series photometric measurements specifically obtained due to gravitational microlensing events. We show through simulation modelling the error sensitivity for detecting microlensing event parameters. Particularly, we show the relation of the amount of error and its impact on the microlensing parameters of interest. We derive statistical error bounds to apply those as a baseline for analyzing the effectiveness of compressive sensing application. Our results conclude that for single and binary microlensing events we can obtain error less than 1% over a 3-pixel radius of the center of the microlensing star by using 25% Nyquist rate measurements.

Compressive Sensing↗

DOE OSTI2017

Compression-based integral curve data reuse framework for flow visualization

Currently, by default, integral curves are repeatedly re-computed in different flow visualization applications, such as FTLE field computation, source-destination queries, etc., leading to unnecessary resource cost. We present a compression-based data reuse framework for integral curves, to greatly reduce their retrieval cost, especially in a resource-limited environment. In our design, a hierarchical and hybrid compression scheme is proposed to balance three objectives, including high compression ratio, controllable error, and low decompression cost. Specifically, we use and combine digitized curve sparse representation, floating-point data compression, and octree space partitioning to adaptively achieve the objectives. Results have shown that our data reuse framework could acquire tens of times acceleration in the resource-limited environment compared to on-the-fly particle tracing, and keep controllable information loss. Moreover, our method could provide fast integral curve retrieval for more complex data, such as unstructured mesh data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Observed compression of in situ tree stems during freezing

Freezing temperatures can influence the material properties of trees. Understanding how forest biometeorological interactions respond to freezing temperatures is important for forest science and management, as its effects can cascade through coupled hydrological and ecological processes including limitations on tree growth, changes to canopy interception, and influences on atmospheric turbulence. This short communication details the effect of sub-freezing temperatures on the mechanical displacement of three in situ Pinus contorta Douglas (lodgepole pine) stems during a winter season (2013–2014) at Niwot Ridge Mountain Research Station (CO, USA). Although previous research on harvested trees suggests longitudinal stem expansion should occur as temperatures decrease below freezing, we observed longitudinal compression in live stems below -3 °C that linearl y correlated with sub-zero air temperatures ranging down to -22 °C. Freeze-related compression of stems frequently achieved displacement magnitudes comparable to applying 55 kN of compressive force (800 μm). Hypotheses are proposed to explain the significant mechanical displacement observed due to freezing: (1) internal gas release as freezing fronts propagate through stems; (2) short-term relocation of water via sap exudation or preferential ice crystal growth. As the observed stem compression response to freezing is substantial, future work on underlying processes and consequences is merited. The measurement technique used here may prove useful to others interested in the dynamics of stem freezing.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Numerical investigation of a joint approach to thermal energy storage and compressed air energy storage in aquifers

Different from conventional compressed air energy storage (CAES) systems, the advanced adiabatic compressed air energy storage (AA-CAES) system can store the compression heat which can be used to reheat air during the electricity generation stage. Thus, AA-CAES system can achieve a higher energy storage efficiency. Similar to the AA-CAES system, a compressed air energy storage in aquifers (CAESA) system, which is integrated with an aquifer thermal energy storage (ATES) could possibly achieve the same objective. In order to investigate the impact of ATES on the performance of CAESA, different injection air temperature schemes are designed and analyzed by using numerical simulations. Key parameters relative to energy recovery efficiencies of the different injection schemes, such as pressure distribution and temperature variation within the aquifers as well as energy flow rate in the injection well, are also investigated in this study. The simulations show that, although different injection schemes have a similar overall energy recovery efficiency (~97%) as well as a thermal energy recovery efficiency (~79.2%), the higher injection air temperature has a higher energy storage capability. Our results show the total energy storage for the injection air temperature at 80 °C is about 10% greater than the base model scheme at 40 °C. Sensitivity analysis reveal that permeability of the reservoir boundary could have significant impact on the system performance. However, other hydrodynamic and thermodynamic properties, such as the storage reservoir permeability, thermal conductivity, rock grain specific heat and rock grain density, have little impact on storage capability and the energy flow rate. Our study suggests that the combination of ATES and CAESA can help keep the high efficiency of energy storage so as to make CAESA system more efficiency.

25 ENERGY STORAGE↗

DOE OSTI2026

Can classical DEM simultaneously capture compressibility and flowability of milled biomass?

Accurate prediction of the rheological behavior of biomass is essential for the design and operation of hoppers, feeders, and storage systems in biorefineries. This study examines whether the classical, coarse-grained discrete element method (DEM) formulation can simultaneously reproduce the compressibility and flowability of milled herbaceous biomass, using Miscanthus × giganteus as a representative material. The model represents particles as rigid spheres interacting through Hertz-Mindlin elastic-frictional contacts augmented with an area-dependent cohesion term. Laboratory cyclic compression and wedge-shaped hopper discharge experiments were used as calibration benchmarks. Although the model can independently reproduce each behavior by appropriately tuning particle Young's modulus E and cohesion energy density k, an extensive parametric investigation comprising more than 600 simulations reveals that the optimal parameter regions for compression and hopper flow are distinct and non-overlapping in (E, k) space. Surrogate surface analysis further shows that the corresponding objective-function valleys exhibit similar trends but are approximately parallel and spatially offset, precluding a unified calibration within the explored domain. Sensitivity analysis indicates that compressibility is governed predominantly by stiffness and cohesion, whereas the slope of the mass flow rate-opening relation in hopper discharge is primarily controlled by tangential friction. Extensions incorporating particle size distribution and clumped-sphere representations do not eliminate the incompatibility. These results systematically reveal, for the first time, the structural limitation of simplified DEM formulations in representing biomass rheological behavior, underscoring the necessity for models incorporating additional physical mechanisms, such as particle deformability or enhanced interlocking, to achieve unified predictive capability for biomass handling behavior.

09 BIOMASS FUELS↗

DOE OSTI2017

A molecular dynamics investigation of hydrostatic compression characteristics of mineral Jennite

This paper focuses on the study of mechanical behavior and deformation of mineral Jennite, under isothermal hydrostatic compression using Molecular Dynamics (MD) modeling. Atomistic structure of mineral Jennite is considered as a choice for representing calcium silicate hydrate (C-S-H), a key constituent in hydrated cement paste. The present work focuses and presents a molecular dynamics simulation approach to ascertain hydrostatic compression characteristics of mineral Jennite. The pressure–specific volume, and specific internal energy–specific volume relationships under isothermal hydrostatic compression conditions were determined. For the pressures ranging from 0.0001 GPa to 5 GPa, computational modeling results indicated that a linear relationship may be sufficient when describing the pressure–specific volume relationship. The results obtained in this study compared well with experimental and theoretical results recently published by other researchers. Additionally, a quadratic function was found to be appropriate to describe the specific energy–specific volume relationship, for the same pressure range used for studying the pressure–specific volume relationship. The pressure–specific volume, and specific internal energy-specific volume relationships reported in this paper are useful for further estimation of pressure–specific volume constitutive Hugoniot of the nanoscale mineral Jennite. Additionally, MD approach discussed and presented in this paper can be applied to understand hydrostatic compression behavior and deformation characteristics of other atomistic structures representing C-S-H. Further, the computational material modeling approach discussed in this paper provides an alternative methodology that can aid in the understanding of the deformation mechanisms and developing constitutive relationships from atomistic structures of materials.

36 MATERIALS SCIENCE↗

DOE OSTI2014

Towards efficient backward-in-time adjoint computations using data compression techniques

In the context of a posteriori error estimation for nonlinear time-dependent partial differential equations, the state-of-the-practice is to use adjoint approaches which require the solution of a backward-in-time problem defined by a linearization of the forward problem. One of the major obstacles in the practical application of these approaches, we found, is the need to store, or recompute, the forward solution to define the adjoint problem and to evaluate the error representation. Our study considers the use of data compression techniques to approximate forward solutions employed in the backward-in-time integration. The development derives an error representation that accounts for the difference between the standard-approach and the compressed approximation of the forward solution. This representation is algorithmically similar to the standard representation and only requires the computation of the quantity of interest for the forward solution and the data-compressed reconstructed solution (i.e. scalar quantities that can be evaluated as the forward problem is integrated). This approach is then compared with existing techniques, such as checkpointing and time-averaged adjoints. Lastly, we provide numerical results indicating the potential efficiency of our approach on a transient diffusion–reaction equation and on the Navier–Stokes equations. These results demonstrate memory compression ratios up to 450×450× while maintaining reasonable accuracy in the error-estimates.

42 ENGINEERING↗

DOE OSTI2018

CFD simulations of Rapid Compression Machines using detailed chemistry: Evaluation of the ‘crevice containment’ concept

The use of creviced pistons in Rapid Compression Machines (RCMs) has proven to be very effcient in making the temperature homogeneous inside the reaction chamber but has the disadvantage of inducing a mass transfer from the reaction chamber to the crevice, especially during the preliminary heat release of two- stage ignition processes. Aiming to mitigate this mass transfer, the technique of `crevice containment' (CC) has been proposed. It consists of a physical separa- tion between the reaction chamber and the crevice region that is engaged at the end of the compression, physically preventing any mass transfer between both parts of the geometry. In order to numerically assess this novel design concept across a broader range of conditions than previously investigated, reactive simu- lations using detailed chemical kinetic mechanisms are performed for n-heptane and iso-octane. For compressed temperatures outside of the NTC (negative temperature coeffcient) region, the CC approach is very effective in suppress- ing the influence of the crevice mass transfer and thus increases the validity of the widely-used 0-D model based on the adiabatic core assumption. Still, for most of the temperature cases inside the NTC region, the ignition appears to be precipitately initiated in the residual vortex region formed after the seal engagement, possibly inducing very significant differences with the 0-D model. The benefits of eliminating the post-compression crevice mass transfer appear to be counter-balanced by effects that have not been previously investigated in detail.

42 ENGINEERING↗

DOE OSTI2021

Model predictive combustion control of a Gasoline Compression Ignition engine

Gasoline Compression Ignition is a novel combustion concept that derives its superiority from the high compression ratio of a compression ignition engine as well as the properties of gasoline fuel, such as longer ignition delay and higher volatility compared to diesel. Here, this combustion concept was experimentally tested on a 12.4L Class 8 truck engine. Based on these experimental data, prior efforts by the authors focused on the development of an engine model for a heavy-duty engine operating on a low-reactivity fuel. This engine model was leveraged within this study to investigate a combustion control strategy at different engine conditions and injection methods and was augmented to incorporate cycle-to-cycle combustion variations. State estimation is performed by means of a Kalman filter which feeds into a model predictive controller. The model predictive controller chooses control actions based on a predefined cost function under consideration of bounds reflecting physical constraints. The engine model was utilized to establish a state-space model that serves the Kalman filter and model predictive controller for estimation and prediction. A comparative study investigating control actions and engine behavior was performed with and without limiting in-cylinder peak pressure as well as combustion noise, which is of particular interest for early pilot injection strategies. In addition, the proposed control architecture was investigated at two different levels of cycle-to-cycle variations and compared to the performance of a control structure with input disturbance rejection. For increased cycle-to-cycle variations, disturbance estimation reduces state fluctuations and control effort. In general, this investigation highlights control aspects specific to a compression-ignited combustion regime with low-reactivity fuel. The control algorithm is able to maintain the desired references for brake mean effective pressure and combustion phasing while controlling peak in-cylinder pressure and combustion noise.

42 ENGINEERING↗

DOE OSTI2023

Low-carbon fuels for spark-ignited engines: A comparative study of compressed natural gas and liquefied petroleum gas on a CFR engine with exhaust gas recirculation

Decades of work on low-carbon fuels have established their potential for substantial emissions reductions; however, their adoption is still limited by infrastructure concerns and engine efficiency deficits. As infrastructures have begun to evolve, research on strategies that maximize engine efficiency through interactions with fuel properties must also now take center place. This paper compares the performance, emissions, and combustion characteristics of two forefront low-carbon fuels: compressed natural gas (CNG) and liquefied petroleum gas (LPG) in a cooperative fuel research (CFR) engine over a range of compression ratios and engine loads. The effects of exhaust gas recirculation (EGR), end-gas auto-ignition, and a novel combustion control tool, the combustion intensity metric (CIM), were also evaluated at different stoichiometric engine operating conditions. In comparison to LPG, CNG operation demonstrated an extended knock-free regime, allowing engine operation at higher engine loads and compression ratios, but LPG operation exhibited enhanced combustion characteristics with higher peak pressures and faster apparent heat release rates (AHRR). LPG operation achieved higher brake thermal efficiencies and lower equivalent CO 2 emissions compared to CNG operation at the tested engine loads and compression ratios. LPG demonstrated significantly higher EGR tolerance limits compared to CNG, with a maximum of 28% EGR rate, compared to 23% for CNG. This improved EGR dilution tolerance was responsible for a 90% reduction in NOx emissions for LPG compared to a maximum of 70% with CNG. EGR dilution also exhibited more effective knock mitigation potential with LPG, suppressing knock intensity values by up to 98% and transitioning the engine operation towards normal combustion from heavy knocking conditions. As a result, the CIM was found to decrease burn durations and improve the quality of combustion by controlling the desired fraction of end-gas auto-ignition.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2021

Mechanics of vitrimer particle compression and fusion under heat press

The compression and fusion of vitrimer particles is a fundamental problem underlying the recycling of vitrimers. Incomplete particle compression can lead to voids in the fused vitrimer, thereby impacting its mechanical property. In this work, we present a two-dimensional finite element model to capture the interplay of multiple complex mechanisms including random particle packing, large deformation, inter-particle contact, thermally activated bulk stress relaxation, and interface healing. Specifically, we focus on understanding two key components of this problem: i) evolution of porosity during compression and fusion of randomly packed vitrimer particles, and ii) effective tensile modulus and strength of fused vitrimer. Using the finite element model, we first show that using smaller particles can reduce the porosity in the fused vitrimer, which only slightly increases the effective modulus, but the increase in tensile strength is more pronounced. In addition, under the same processing conditions, particles with mixed sizes can achieve better densification and higher tensile strength after fusion than uniformly sized particles with the same average radius. Regarding the effects of processing conditions, we discuss how the porosity and effective modulus of the fused vitrimer depend on the processing time, temperature and pressure, and find a general trend consistent with experimental observations, i.e. longer processing time, higher temperature or higher pressure can lead to lower porosity and higher modulus of the fused vitrimer. Finally, the theoretical insights towards the compression and fusion process as well as the simulation framework can be useful in optimizing the powder-based heat press process of vitrimer and its composites.

42 ENGINEERING↗