In situ X-ray synchrotron tomographic imaging during the compression of hyper-elastic polymeric materials
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The Claridge-Culp-Liu Dehumidification Process is a novel and efficient approach to removing water vapor from air using a combination of membrane separation, vacuum compression and sub-atmospheric condensation. The basic theory of this process is to separate water vapor from moist air flowing across one side of a membrane by applying a partial vacuum to the opposite side of the membrane and then compressing the water vapor to its saturation pressure at the wet-bulb temperature of the ambient air so as to facilitate condensation. We report this process has a fundamental efficiency limit that approximates the Carnot limit, but for eight different ideal dehumidification-only cases examined herein, the process requires only 26–56% the energy required by a Carnot vapor-compression system. Furthermore, the limiting energy required by an ideal 5-stage membrane system is 16–31% that of a Carnot system for the same cases. Of special importance, the ideal Claridge-Culp-Liu Dehumidification Process requires less than 5% of the energy required by an ideal desiccant process for all cases treated. The Claridge-Culp-Liu Dehumidification Process can also be combined with evaporative cooling to provide dehumidification and sensible cooling with the temperature and humidity controlled independently. Of special importance, this system uses no HFC refrigerants, and it generates pure water as a by-product.
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Explore the source record for details and available documents.
Abstract The compression behavior of osmium metal was investigated up to 280 GPa (volume compression V/V o = 0.725) under nonhydrostatic conditions at ambient temperature using angle dispersive axial x-ray diffraction (A-XRD) with a diamond anvil cell (DAC). In addition, shear strength of osmium was measured to 170 GPa using radial x-ray diffraction (R-XRD) technique in DAC. Both diffraction techniques in DAC employed platinum as an internal pressure standard. Density functional theory (DFT) calculations were also performed, and the computed lattice parameters and volumes under compression are in good agreement with the experiments. DFT predicts a monotonous increase in axial ratio ( c/a ) with pressure and the structural anomalies of less than 1% in ( c/a ) ratio reported below 150 GPa were not reproduced in theoretical calculations and hydrostatic measurements. The measured value of shear strength of osmium ( τ ) approaches a limiting value of 6 GPa above a pressure of 50 GPa in contrast to theoretical predictions of 24 GPa and is likely due to imperfections in polycrystalline samples. DFT calculations also enable the studies of shear and tensile deformations. The theoretical ideal shear stress is found along the (001)[1–10] shear direction with the maximal shear stress ∼24 GPa at critical strain ∼0.13.
Recent advances in nanotechnology have enabled researchers to manipulate small collections of quantum-mechanical objects with unprecedented accuracy. In semiconductor quantum-dot qubits, this manipulation requires controlling the dot orbital energies, the tunnel couplings, and the electron occupations. These properties all depend on the voltages placed on the metallic electrodes that define the device, the positions of which are fixed once the device is fabricated. While there has been much success with small numbers of dots, as the number of dots grows, it will be increasingly useful to control these systems with as few electrode voltage changes as possible. Here, we introduce a protocol, which we call the "compressed optimization of device architectures" (CODA), in order both to efficiently identify sparse sets of voltage changes that control quantum systems and to introduce a metric that can be used to compare device designs. As an example of the former, we apply this method to simulated devices with up to 100 quantum dots and show that CODA automatically tunes devices more efficiently than other common nonlinear optimizers. To demonstrate the latter, we determine the optimal lateral scale for a triple quantum dot, yielding a simulated device that can be tuned with small voltage changes on a limited number of electrodes.
Room temperature compression of graphitic materials leads to interesting superhard sp 3 rich phases which are sometimes transparent. In the case of graphite itself, the sp 3 rich phase is proposed to be monoclinic M-carbon; however, for disordered materials such as glassy carbon the nature of the transformation is unknown. We compress glassy carbon at room temperature in a diamond anvil cell, examine the structure in situ using x-ray diffraction, and interpret the findings with molecular dynamics modeling. Experiment and modeling both predict a two-stage transformation. First, the isotropic glassy carbon undergoes a reversible transformation to an oriented compressed graphitic structure. This is followed by a phase transformation at ~35 GPa to an unstable, disordered sp 3 rich structure that reverts on decompression to an oriented graphitic structure. Analysis of the simulated sp 3 rich material formed at high pressure reveals a noncrystalline structure with two different sp 3 bond lengths.
Here, compressive sensing is a powerful technique for recovering sparse solutions of underdetermined linear systems, which is often encountered in uncertainty quantification analysis of expensive and high-dimensional physical models. We perform numerical investigations employing several compressive sensing solvers that target the unconstrained LASSO formulation, with a focus on linear systems that arise in the construction of polynomial chaos expansions. With core solvers l1_ls, SpaRSA, CGIST, FPC_AS, and ADMM, we develop techniques to mitigate overfitting through an automated selection of regularization constant based on cross-validation, and a heuristic strategy to guide the stop-sampling decision. Practical recommendations on parameter settings for these techniques are provided and discussed. The overall method is applied to a series of numerical examples of increasing complexity, including large eddy simulations of supersonic turbulent jet-in-crossflow involving a 24-dimensional input. Through empirical phase-transition diagrams and convergence plots, we illustrate sparse recovery performance under structures induced by polynomial chaos, accuracy, and computational trade-offs between polynomial bases of different degrees, and practicability of conducting compressive sensing for a realistic, high-dimensional physical application. Across test cases studied in this paper, we find ADMM to have demonstrated empirical advantages through consistent lower errors and faster computational times.
We experimentally demonstrate how divided-pulse nonlinear compression can be used to improve the temporal contrast of a laser pulse train while simultaneously temporally compressing the pulses. We measure a contrast improvement of almost four orders of magnitude on a nanosecond time scale and temporally compress the pulses from 1.2 ps to 187 fs. The efficiency of our method is also competitive with other contrast improvement methods, with 72% efficiency measured for the main pulse. We expect the method will be useful in the continuing development of high-power, Yb regenerative amplifiers, which suffer from both significant prepulses and relatively long pulse durations.
As the pioneer semiconductor in transistor, germanium (Ge) has been widely applied in information technology for over half a century. Although many phase transitions in Ge have been reported, the complicated phenomena of the phase structures in amorphous Ge under extreme conditions are still not fully investigated. Here, we report the different routes of phase transition in amorphous Ge under different compression conditions utilizing diamond anvil cell (DAC) combined with synchrotron-based X-ray diffraction (XRD) and Raman spectroscopy techniques. Upon non-hydrostatic compression of amorphous Ge, we observed that shear stress facilitates a reversible pressure-induced phase transformation, in contrast to the pressure-quenchable structure under a hydrostatic compression. These findings afford better understanding of the structural behaviors of Ge under extreme conditions, which contributes to more potential applications in the semiconductor field.
By using new invariants in the theory of finite elasticity an expression is obtained for the stored energy function of slightly compressible materials in which the effects of the distortional change (change of shape) and of the volume change are clearly separated. The volume-related terms are expressed as a function of the third invariant, the classical compressibility, and an induced anisotropy of the effective compressibility which is due to the large deformations. After evaluating the terms, using data on pressure, volume, uniaxial strain, and fractional volume change vs strain data on natural rubber from the literature, it is shown that the volume change contribution to the total stress observed in a simple tensile experiment can be clearly separated from the distortional contribution, even at finite strains.
The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.
The paper introduces a failure model for laminated composite plates with a cutout under combined compressive and shear loads. The model is based on kinking failure of the load-carrying fibers around a cutout, and includes the effect of local shearing and compressive stresses. Comparison of predictions of the model with available experimental results for quasi-isotropic and orthotropic plates with a circular hole indicated a good agreement. Predictions for orthotropic plates under combined loading are compared with the predictions of a point-stress model. The present model indicates significant reductions in axial load-carrying capacity due to shearing loads for plates with principal axis of orthotropy oriented along the axial load direction. A gain in strength is achieved by rotating the axis of orthotropy to counteract the shearing stress, or by eliminating the compressive-shear deformation coupling.
Failure models for the prediction of compressive strength of plates with a hole are investigated. One of the models is based on the strength failure of the fibers that leads to fiber kinking failure. A different version is developed for cases where shear failure of the fibers is expected to be a dominate failure mode. Both models are capable of including the effects of combined shearing and compressive stresses around a hole in a plate and, therefore, are expected to be applicable to plates under combined shearing and compressive loadings, as well as anisotropic plates.
Stitching through-the-thickness (TTT) of composite materials produces a surface loop of yarn between successive penetrations. The surface loop is pressed into the surface layers of the composite material during the curing of the laminate, kinking the in-plane fibers near the surface of the material. The compression strength and compression-after-impact (CAI) strengths of carbon-epoxy specimens were measured with and without the surface loop. Removal of the surface loop had no influence on failure mode or failure mechanism, but did significantly increase the compression and CAI strengths.
A preloading device was used to examine the effects of compressive prestress on the compression-after-impact (CAI) strength of 16-ply, quasi-isotropic carbon epoxy test coupons. T300/934 material was evaluated at preloads from 200 to 4000 lb at impact energies from 1 to 9 joules. IM7/8551-7 material was evaluated at preloads from 4000 to 10,000 lb at impact energies from 4 to 16 joules. Advanced design of experiments methodology was used to design and evaluate the test matrices. The results showed that no statistically significant change in CAI strength could be contributed to the amount of compressive preload applied to the specimen.
Forward swept sidewall compression inlets have been tested in the Mach 4 Blowdown Facility at the NASA Langley Research Center to study the effects of bodyside compression surfaces on inlet performance in the presence of an incoming turbulent boundary layer. The measurements include mass flow capture and mean surface pressure distributions obtained during simulated combustion pressure increases downstream of the inlet. The kerosene-lampblack surface tracer technique has been used to obtain patterns of the local wall shear stress direction. Inlet performance is evaluated using starting and unstarting characteristics, mass capture, mean surface pressure distributions and permissible back pressure limits. The results indicate that inlet performance can be improved with selected bodyside compression surfaces placed between the inlet sidewalls.