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The design and development of a unique coupler crossbar 20 x 20 microwave switch matrix are described. The test results of the proof of concept model that meets the requirements for a high speed satellite switched, time division multiple access (SS-TDMA) system are presented.
Low-frequency variability of large-scale atmospheric dynamics can be represented schematically by a Markov chain of multiple flow regimes. This Markov chain contains useful information for the long-range forecaster, provided that the statistical significance of the associated transition matrix can be reliably tested. Monte Carlo simulation yields a very reliable significance test for the elements of this matrix. The results of this test agree with previously used empirical formulae when each cluster of maps identified as a distinct flow regime is sufficiently large and when they all contain a comparable number of maps. Monte Carlo simulation provides a more reliable way to test the statistical significance of transitions to and from small clusters. It can determine the most likely transitions, as well as the most unlikely ones, with a prescribed level of statistical significance.
The development of durable bonded joint technology for assembling composite structures for launch vehicles is being pursued for the U.S. Space Launch System. The present work is related to the development and application of progressive damage modeling techniques to bonded joint technology applicable to a wide range of sandwich structures for a Heavy Lift Launch Vehicle. The joint designs studied in this work include a conventional composite splice joint and a NASA-patented Durable Redundant Joint. Both designs involve a honeycomb sandwich with carbon/epoxy facesheets joined with adhesively bonded doublers. Progressive damage modeling allows for the prediction of the initiation and evolution of damage. For structures that include multiple materials, the number of potential failure mechanisms that must be considered increases the complexity of the analyses. Potential failure mechanisms include fiber fracture, matrix cracking, delamination, core crushing, adhesive failure, and their interactions. The joints were modeled using Abaqus parametric finite element models, in which damage was modeled with user-written subroutines. Each ply was meshed discretely, and layers of cohesive elements were used to account for delaminations and to model the adhesive layers. Good correlation with experimental results was achieved both in terms of load-displacement history and predicted failure mechanisms.
Due to the insufficient measurements in the distribution system state estimation (DSSE), full observability and redundant measurements are difficult to achieve without using the pseudo measurements. The matrix completion state estimation (MCSE) combines the matrix completion and power system model to estimate voltage by exploring the low-rank characteristics of the matrix. This paper proposes a robust matrix completion state estimation (RMCSE) to estimate the voltage in a distribution system under a low-observability condition. Tradition state estimation weighted least squares (WLS) method requires full observability to calculate the states and needs redundant measurements to proceed a bad data detection. The proposed method improves the robustness of the MCSE to bad data by minimizing the rank of the matrix and measurements residual with different weights. It can estimate the system state in a low-observability system and has robust estimates without the bad data detection process in the face of multiple bad data. The method is numerically evaluated on the IEEE 33-node radial distribution system. The estimation performance and robustness of RMCSE are compared with the WLS with the largest normalized residual bad data identification (WLS-LNR), and the MCSE.
Infinite-order generalized impedance boundary conditions (GIBCs) and generalized sheet transition conditions (GSTCs) for planar multilayer configurations are developed via the Taylor series expansion method. The conditions are derived in a matrix product form where each matrix corresponds to a specific layer. An overall composite boundary/transition condition is obtained by making finite-order approximations to the elements of each matrix for the cases of 'low-contrast' and 'high-contrast' material layers. The accuracy of the truncated boundary conditions is examined by comparing their implied reflection and transmission coefficients with the corresponding exact coefficients. Design curves are also given which relate the maximum order of the conditions required to simulate a coating or layer of specific thickness and contrast. Expressions are then derived for the reflection and transmission coefficients of the GIBC/GSTC sheets, and these are compared to exact coefficients to demonstrate the validity of the derived GIBCs/GSTCs.
Particular combinations of solute atoms segregated to the interface of the Al and θ ' - Al 2 Cu in Al-Cu alloys can help stabilize θ ' precipitates at high temperatures. Stabilization of such precipitates is determined by a combination of thermodynamics (including driving forces for coarsening and transformation and solute segregation tendencies) and kinetic effects (including solute diffusion and interfacial mobility in the presence of interfacial solute segregation). For some alloys such as recent Al-Cu-based alloys, multiple solutes segregate in significant quantities to interfaces, and solute-solute interactions at the interface are important, with multiple types of solutes competing for similar interfacial sites. To treat this situation, we develop and apply a statistical mechanics approach to calculate the temperature-dependent equilibrium solute atoms distribution near the coherent and semicoherent interfaces between the Al matrix and the θ ' - Al 2 Cu precipitates. The developed approach is applied to the investigation of Si, Mn, and Zr segregation at the interface, as particular combinations of these elements affect the thermal stability of the θ ' precipitates. We demonstrate that because Si and Mn atoms segregate on the same semicoherent interface, the presence of Si reduces the concentration of Mn solutes at the interface. Si atoms preferably occupy the first layer of the interface and compete with Mn atoms for one type of particular sites in the layer. Mn atoms preferably occupy the second layer of the semicoherent interface, and the Mn-Mn interaction plays an important role in their distribution. Zr atoms mostly segregate on one of the two nonequivalent sites of the second layer of the coherent interface. Due to symmetry properties of the coherent interface, the calculations show that the segregation Zr of atoms to this interface will likely lead to the formation of L 1 2 ordered Al 3 Zr layer.
Detailed knowledge on the particle distribution in accelerators can strongly benefit machine tuning and understanding of deleterious effects, which degrade beam quality. Properties of the beam downstream the beamline (i.e. 4D beam matrix) depend on beam properties upstream and parameters of the beamline. Measuring 2D x-y beam profile downstream provides limited information about the beam. Multiple measurements at different settings of the beamline (quadrupole scan) may provide enough information to recover original beam profile. Essentially, this concept is similar to tomography, when we need to reconstruct a higher dimension object from multiple low dimension projections.
Proposed apparatus for optical multiplication of two matrices based on Stanford optical vector.matrix multiplier. Does not require redundant representation of one of matrices. Because apparatus performs multiplication in fully parallel manner, incorporated as subsystem into large optical-processing system. Optical matrix.matrix multiplier processes inputs in fully parallel fashion, without redundant matrix images or ancillary intermediate electronic processing.
Design procedures are developed to select both eigenvalues and eigenvectors to improve system robustness. A robustness metric based on the determinant of the normalized eigenvector matrix defined. It is shown that this metric significantly speeds up the convergence of robust eigenvector solutions close to the optimal. A procedure to blend multiple inputs to improve achievable system robustness in the lower dimensional pseudo control space has also been derived.
A reconstruction technique for reducing computation burden in the 3D image processes, wherein the reconstruction procedure comprises an inverse and a forward model. The inverse model uses a hybrid dual Fourier algorithm that combines a 2D Fourier inversion with a 1D matrix inversion to thereby provide high-speed inverse computations. The inverse algorithm uses a hybrid transfer to provide fast Fourier inversion for data of multiple sources and multiple detectors. The forward model is based on an analytical cumulant solution of a radiative transfer equation. The accurate analytical form of the solution to the radiative transfer equation provides an efficient formalism for fast computation of the forward model.
The equation of state (EOS) in the limit of infinite symmetric nuclear matter exhibits an equilibrium density, $n_0 \approx 0.16 \, \mathrm{fm}^{-3}$, at which the pressure vanishes and the energy per particle attains its minimum, $E_0 \approx -16 \, \mathrm{MeV}$. Although not directly measurable, the nuclear saturation point $(n_0,E_0)$ can be extrapolated by density functional theory (DFT), providing tight constraints for microscopic interactions derived from chiral effective field theory (EFT). However, when considering several DFT predictions for $(n_0,E_0)$ from Skyrme and Relativistic Mean Field (RMF) models together, a discrepancy between these model classes emerges at high confidence levels that each model prediction's uncertainty cannot explain. How can we leverage these DFT constraints to rigorously benchmark nuclear saturation properties of chiral interactions? To address this question, we present a Bayesian mixture model that combines multiple DFT predictions for $(n_0,E_0)$ using an efficient conjugate prior approach. The inferred posterior distribution for the saturation point's mean and covariance matrix follows a Normal-inverse-Wishart class, resulting in posterior predictives in the form of correlated, bivariate $t$-distributions. The DFT uncertainty reports are then used to mix these posteriors using an ordinary Monte Carlo approach. At the 95\% credibility level, we estimate $n_0 \approx 0.157 \pm 0.010 \, \mathrm{fm}^{-3}$ and $E_0 \approx -15.97 \pm 0.40 \, \mathrm{MeV}$ for the marginal (univariate) $t$-distributions. Combined with chiral EFT calculations of the pure neutron matter EOS, we obtain bivariate normal distributions for the nuclear symmetry energy and its slope parameter evaluated at $n_0$: $S_v \approx 32.0 \pm 1.1 \, \mathrm{MeV}$ and $L\approx 52.6\pm 8.1 \, \mathrm{MeV}$ (95\%), respectively. Furthermore, our Bayesian framework is publicly available, so practitioners can readily use and extend our results.
This presentation discusses how nuclear data evaluations can influence the accuracy of computational nuclear design calculations. It also discusses the motivation for a new evaluation of 181 Ta. The goals of the evaluations are to generate a set of resonance parameters in the Resolved Resonance Region (RRR) that extends up to 2.6 keV, to generate a set of average resonance parameters in the Unresolved Resonance Region (URR) up to 100 keV (derived from the RRR parameters), and to generate covariance information for both RRR and URR. The presentation discusses how several different experimental data sets will be fit simultaneously in the evaluation of 181 Ta. It states that the level density of 181 Ta can make R-matrix fitting a challenge as the width of the levels increase with energy (as well as decreasing experimental resolution) and the measured resonances can overlap. The presentation concludes by discussing R-matrix analysis and covariance information as the latter is important when propagated to quantify the uncertainty on output responses of nuclear design calculations such as the multiplication factor k .
In this work, we implement a tensor network-based quantum algorithm to solve unsteady, nonlinear partial differential equations (PDEs). The challenge lies in how to effectively represent, encode, process, and evolve the nonlinear system of PDEs on quantum computers. We will discuss the new techniques using the compressible 1-dimensional (1D) Burgers' equation as an example, because it represents the fundamental nonlinear feature and yet removes certain complexity in physics, allowing us to focus on the design of quantum algorithms. Previous attempts to solve nonlinear PDEs in quantum computation have often involved storing multiple copies of solutions or employing linearizations. Neither is practical due to exponential scaling with evolution time or insufficient solution accuracy. Our framework is based on matrix product states (MPSs) and matrix product operators (MPOs). For example, the velocity field is represented by MPS, whereas the linear and nonlinear spatial differential terms of the velocity field are processed by MPOs. Our primary focus herein is to verify and validate the various tensor network components of the algorithm using solutions obtained by the classical algorithms on high performance computing (HPC) architectures. We use a classical time marching method to demonstrate the functionality of the tensor network operations to model the PDE and their robustness with the time evolution of the system. Our classical simulation results demonstrate the utility of tensor network-based operations in modeling nonlinear PDEs and highlight the necessity as well as potential advantages of using quantum simulations for these techniques.
Brown rot (BR) decay mechanisms employ carbohydrate-active enzymes (CAZymes) as well as a unique non-enzymatic chelator-mediated Fenton (CMF) chemistry to deconstruct lignocellulosic materials. Unlike white rot fungi, BR fungi lack peroxidases for lignin deconstruction, and also lack some endoglucanase/cellobiohydrolase activities. The role that the CMF mechanism plays in “opening up” the wood cell wall structure in advance of enzymatic action, and any interaction between CMF constituents and the selective CAZyme suite that BRs possess, is still unclear. Expression patterns for CMF redox metabolites and lytic polysaccharide monooxygenase (LPMO–AA9 family) genes showed that some LPMO isozymes were upregulated with genes associated with CMF at early stages of brown rot by Gloeophyllum trabeum. In the structural studies, wood decayed by the G. trabeum was compared to CMF-treated wood, or CMF-treated wood followed by treatment with either the early-upregulated LPMO or a commercial CAZyme cocktail. Structural modification of decayed/treated wood was characterized using small angle neutron scattering. CMF treatment produced neutron scattering patterns similar to that of the BR decay indicating that both systems enlarged the nanopore structure of wood cell walls to permit enzyme access. Enzymatic deconstruction of cellulose or lignin in raw wood samples was not achieved via CAZyme cocktail or LPMO enzyme action alone. CMF treatment resulted in depolymerization of crystalline cellulose as attack progressed from the outer regions of individual crystallites. Multiple pulses of CMF treatment on raw wood showed a progressive increase in the spacing between the cellulose elementary fibrils (EFs), indicating the CMF eroded the matrix outside the EF bundles, leading to less tightly packed EFs. Peracetic acid delignification treatment enhanced subsequent CMF treatment effects, and allowed both enzyme systems to further increase spacing of the EFs. Moreover, even after a single pulse of CMF treatment, both enzymes were apparently able to penetrate the cell wall to further increase EF spacing. The data suggest the potential for the early-upregulated LPMO enzyme to work in association with CMF chemistry, suggesting that G. trabeum may have adopted mechanisms to integrate non-enzymatic and enzymatic chemistries together during early stages of brown rot decay.
Friction and wear tests were conducted with 3.2- and 6.4-millimeter-diameter aluminum oxide spheres sliding, in reciprocating motion, on a Fe67Co18B14Si1 metallic foil. Crystallites with a size range of 10 to 150 nanometers were produced on the wear surface of the amorphous alloy. A strong interaction between transition metals and metalloids such as silicon and boron results in strong segregation during repeated sliding, provides preferential transition metal-metalloid clustering in the amorphous alloy, and subsequently produces the diffused honeycomb structure formed by dark grey bands and primary crystals, that is, alpha-Fe in the matrix. Large plastic flow occurs on an amorphous alloy surface with sliding and the flow film of the alloy transfers to the aluminum oxide pin surface. Multiple slip bands due to shear deformation are observed on the side of the wear track. Two distinct types of wear debris were observed as a result of sliding: an alloy wear debris, and/or powdery-whiskery oxide debris.
Friction and wear tests were conducted with 3.2- and 6.4-millimeter-diameter aluminum oxide spheres sliding, in reciprocating motion, on a Fe67Co18B14Si1 metallic foil. Crystallites with a size range of 10 to 150 nanometers were produced on the wear surface of the amorphous alloy. A strong interaction between transition metals and metalloids such as silicon and boron results in strong segregation during repeated sliding, provides preferential transition metal-metalloid clustering in the amorphous alloy, and subsequently produces the diffused honeycomb structure formed by dark grey bands and primary crystals, that is, alpha-Fe in the matrix. Large plastic flow occurs on an amorphous alloy surface with sliding and the flow film of the alloy transfers to the aluminum oxide pin surface. Multiple slip bands due to shear deformation are observed on the side of the wear track. Two distinct types of wear debris were observed as a result of sliding: an alloy wear debris, and/or powdery-whiskery oxide debris.
Friction and wear tests were conducted with 3.2- and 6.4-millimeter-diameter aluminum oxide spheres sliding, in reciprocating motion, on a Fe67Co18B14Si1 metallic foil. Crystallites with a size range of 10 to 150 nanometers were produced on the wear surface of the amorphous alloy. A strong interaction between transition metals and metalloids such as silicon and boron results in strong segregation during repeated sliding, provides preferential transition metal-metalloid clustering in the amorphous alloy, and subsequently produces the diffused honeycomb structure formed by dark grey bands and primary crystals, that is, alpha-Fe in the matrix. Large plastic flow occurs on an amorphous alloy surface with sliding and the flow film of the alloy transfers to the aluminum oxide pin surface. Multiple slip bands due to shear deformation are observed on the side of the wear track. Two distinct types of wear debris were observed as a result of sliding: an alloy wear debris, and/or powdery-whiskery oxide debris.
The Mars Science Laboratory (MSL) vehicle utilized a heat shield constructed from NASA’s Phenolic-Impregnated Carbon Ablator (PICA) material to protect the main structure from the high enthalpy environment encountered during hypersonic atmospheric entry. During the vehicle’s descent through Martian atmosphere, multiple thermocouples embedded within the heat shield captured in-depth material temperature data that allow for studies to be conducted on current material response reconstruction tools. In the present work, material temperature data obtained from thermocouples within the MISP-4 plug (MEDLI (Mars Science Laboratory Entry, Descent, and Landing Instrument) Integrated Sensor Plug) are utilized in the calibration of Theoretical Ablative Composite for Open Testing (TACOT) model parameters in conjunction with NASA’s Porous material Analysis Toolbox (PATO) through Bayesian inference where uncertainty due to parametric, modeling, and experimental sources is simultaneously quantified. Prior to the study, a sensitivity analysis is performed through computation of the robust Sobol indices in an effort to study the relationship between input space and model response and to reduce the dimensionality of the statistical inverse problem. The Bayesian inference methodology necessitates an a-priori choice to be made for the uncertainty model for which numerous possibilities are available. Across most works, however, only basic additive or multiplicative models are utilized with pre-defined magnitudes of uncertainty based on a-priori knowledge or to-be-calibrated multipliers of static covariance matrix structures. The present effort explores the effects of informed uncertainty models, ones with temporal dependence that are simultaneously calibrated through Bayesian inference, on calibrated results for parameters that make up the uncertain input space.