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At least 217 records · Page 12

Evaluation and Development of Phase Array Ultrasonic Testing (PAUT) System for Additively Manufactured Parts

This research focuses on the application of advanced ultrasonic testing techniques developed by The Phased Array Company (TPAC) for inspecting defects in additive manufacturing (AM) parts. Traditionally, X-ray computed tomography is the standard for inspecting AM components. Although, the long inspection and analysis time, along with relatively high cost make implementation difficult. Thus, an alternative nondestructive evaluation (NDE) approach is necessary to support quality assurance efforts within the field of AM. TPAC is recognized as a leader in ultrasonic testing innovation, deploying sophisticated algorithms such as Total Focusing Method (TFM) and Phased Wave Imaging (PWI) for ultrasonic data processing and interpretation. This work will explore how the TFM and PWI algorithms can assist defect detection within polymer AM parts. The AM field is seeking novel NDE methods to provide support within quality control and assurance efforts. Advanced ultrasonics inspection have the potential to fulfill this need.

99 GENERAL AND MISCELLANEOUS↗

Evaluation and Development of Phase Array Ultrasonic Testing (PAUT) System for Additively Manufactured Parts

This research focuses on the application of advanced ultrasonic testing techniques developed by The Phased Array Company (TPAC) for inspecting defects in additive manufacturing (AM) parts. Traditionally, X-ray computed tomography is the standard for inspecting AM components. Although, the long inspection and analysis time, along with relatively high cost make implementation difficult. Thus, an alternative nondestructive evaluation (NDE) approach is necessary to support quality assurance efforts within the field of AM. TPAC is recognized as a leader in ultrasonic testing innovation, deploying sophisticated algorithms such as Total Focusing Method (TFM) and Phased Wave Imaging (PWI) for ultrasonic data processing and interpretation. This work will explore how the TFM and PWI algorithms can assist defect detection within polymer AM parts. The AM field is seeking novel NDE methods to provide support within quality control and assurance efforts. Advanced ultrasonics inspection have the potential to fulfill this need.

36 MATERIALS SCIENCE↗

Ship-based Observations and Climate Model Simulations of Cloud Phase over the Southern Ocean

The Southern Ocean (SO) clouds exert a significant influence on the Earth’s radiation budget. Here we analyzed ship-based remote sensing observations of SO clouds over a five-month long DOE ARM Measurements of Aerosols, Radiation and Clouds over the Southern Oceans (MARCUS) field campaign to better understand cloud phase variability. We developed a method to classify eight categories of hydrometeors (ice, liquid, mixed phase, rain, drizzle, snow, aerosols, and clear sky) based on measurements of lidar, radar and radiosondes. Cloud thermodynamic phases (liquid, ice and mixed phase) at coarser scales were further derived to compare with the DOE Energy Exascale Earth System Model version 1 (E3SMv1) simulation and the Earth Model Column Collaboratory (EMC 2 ) instrument simulator. For a scale-aware comparison with climate model simulations, we found that spatially averaging the raw remote sensing data (e.g., backscatter, reflectivity) results in increased cloud cover and cloud liquid, whereas, directly averaging cloud phase from higher to lower resolution maintains clear air regions and is thus recommended for in-cloud frequency comparisons. For cloud thermodynamic phases in stratiform clouds, the E3SM underestimates cloud ice and overestimates cloud liquid at temperatures between -40 and 0°C. When latitudes increase, both observations and simulations show a transition of dominant phase from liquid to ice for cloud tops as well as for the entire cloud columns, but the model underestimation of ice phase is more severe at higher latitudes. Such model bias is unlikely caused by spatial scale differences or lack of heterogeneity in cloud vertical structure in the simulation.

54 ENVIRONMENTAL SCIENCES↗

Effect of transport mechanism on the coarsening of bicontinuous structures: A comparison between bulk and surface diffusion

Here, coarsening of bicontinuous microstructures is observed in a variety of systems, such as nanoporous metals and mixtures that have undergone spinodal decomposition. To better understand the morphological evolution of these structures during coarsening, we compare the morphologies resulting from two different coarsening mechanisms, surface and bulk diffusion. We perform phase-field simulations of coarsening via each mechanism in a two-phase mixture at nominal volume fractions of 50%-50% and 36%-64%, and the simulated structures are characterized in terms of topology (genus density), the interfacial shape distribution, structure factor, and autocorrelations of phase and mean curvature. We observe self-similar evolution of morphology and topology and agreement with the expected power laws for dynamic scaling, in which the characteristic length scale increases over time proportionally to t 1/4 for surface diffusion and t 1/3 for bulk diffusion. While we observe the expected difference in the coarsening kinetics, we find that differences in self-similar morphology due to coarsening mechanism are relatively small, although typically they are larger at 36% volume fraction than at 50% volume fraction. In particular, we find that bicontinuous structures coarsened via surface diffusion have lower scaled genus densities than structures coarsened via bulk diffusion. We also compare the self-similar morphologies to those in literature and to two model bicontinuous structures, namely, constant-mean-curvature surfaces based on the Schoen G minimal surface and random leveled-wave structures. The average scaled mean curvatures of these model structures agree reasonably with those of the coarsened structures at both 36% and 50%, but we find substantial disagreements in the scaled genus densities and the standard deviations of mean curvature.

36 MATERIALS SCIENCE↗

Pairing Correlations across the Superfluid Phase Transition in the Unitary Fermi Gas

In the two-component Fermi gas with a contact interaction, a pseudogap regime can exist at temperatures between the superfluid critical temperature $T_c$ and a temperature $T^\ast > T_c$. This regime is characterized by pairing correlations without superfluidity. However, in the unitary limit of infinite scattering length, the existence of this regime is still debated. To help address this, we have applied finite-temperature auxiliary-field quantum Monte Carlo (AFMC) methods to study the thermodynamics of the superfluid phase transition and signatures of the pseudogap in the spin-balanced homogeneous unitary Fermi gas. We present results at finite filling factor $\textit{ν}$ ≃ 0.06 for the condensate fraction, an energy-staggering pairing gap, the spin susceptibility, and the heat capacity, and compare them to experimental data when available. In contrast to previous AFMC simulations, our model space consists of the complete first Brillouin zone of the lattice, and our calculations are performed in the canonical ensemble of fixed particle number. The canonical ensemble AFMC framework enables the calculation of a model-independent gap, providing direct information on pairing correlations without the need for numerical analytic continuation. We use finite-size scaling to estimate $T_c$ at the corresponding filling factor. In this work, we find that the energy-staggering pairing gap vanishes above $T_c$, showing no pseudogap effects, and that the spin susceptibility shows a substantially reduced signature of a spin gap compared to previously reported AFMC simulations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Correlation function distributions for O ( N ) lattice field theories in the disordered phase

Numerical computations in strongly interacting quantum field theories are often performed using Monte Carlo sampling methods. A key task in these calculations is to estimate the value of a given physical quantity from the distribution of stochastic samples that are generated using the Monte Carlo method. Typically, the sample mean and sample variance are used to define the expectation values and uncertainties of computed quantities. However, the Monte Carlo sample distribution contains more information than these basic properties, and it is useful to investigate it more generally. In this work, the exact form of the probability distributions of two-point correlation functions at zero momentum in O ( N ) lattice field theories in the disordered phase and in infinite volume are determined. These distributions allow for a robust investigation of the efficacy of the Monte Carlo sampling procedure and are shown also to allow for improved estimators of the target physical quantity to be constructed. The theoretical expectations are shown to agree with numerical calculations in the O ( 2 ) model. Published by the American Physical Society 2024

Astronomy & Astrophysics↗

Analysis of a dual-reflector antenna system using physical optics and digital computers

The application of physical-optics diffraction theory to a deployable dual-reflector geometry is discussed. The methods employed are not restricted to the Conical-Gregorian antenna, but apply in a general way to dual and even multiple reflector systems. Complex vector wave methods are used in the Fresnel and Fraunhofer regions of the reflectors. Field amplitude, phase, polarization data, and time average Poynting vectors are obtained via an IBM 360/91 digital computer. Focal region characteristics are plotted with the aid of a CalComp plotter. Comparison between the GSFC Huygens wavelet approach, JPL measurements, and JPL computer results based on the near field spherical wave expansion method are made wherever possible.

Schmidt, R. F.↗

Applying deep learning methods to develop new models of molecular charge transfer, nonadiabatic dynamics, and nonlinear spectroscopy in the condensed phase

Photon- and field-induced charge transfer has central importance in the generation and storage of electricity, the novel properties of materials, photo-induced catalysis, and electro-optic activity (e.g., photovoltaic cells, fuel cells, and organic chromophores for use in optical fibers and light-emission diodes). These non-equilibrium electronic and chemical transformations are probed by ultrafast, nonlinear spectroscopies. Accurate simulations play a crucial role in our ability to understand, optimize, and control these transformations. This project applies modern deep learning and machine learning (ML) methods to dramatically improve models of electronic dynamics, electronic-nuclear dynamics, and spectroscopic measurements for improved simulations of chemistry in complex environments, far from equilibrium phenomena, and processes in extreme environments, such as materials exposed to strong or resonant fields. This project develops accurate neural net models that go beyond predictive capability to also provide new insight into the fundamental physics underlying electron and nuclear dynamics. To achieve its objectives, this project explores and develops customized versions of high-capacity deep learning algorithms/models. These techniques are developed with an emphasis on fundamental chemical insight, not just predictive accuracy, to assist the development of the next generation of quantum simulation methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Towards inverse microstructure-centered materials design using generative phase-field modeling and deep variational autoencoders

The field of Integrated Computational Materials Engineering (ICME) combines a broad range of methods to study materials’ responses over a spectrum of length scales. A relatively unexplored aspect of microstructure-sensitive materials design is uncertainty propagation and quantification (UP/UQ) of materials’ microstructure, as well as establishing process-structure–property (PSP) relationships for inverse material design. In this study, an efficient UP technique built on the idea of changing probability measures and a deep generative unsupervised representative machine learning method for microstructure-based design of thermal conductivity of materials is proposed. Probability measures are used to represent microstructure space, and Wasserstein metrics are used to test the efficiency of the UP method. By using deep Variational AutoEncoder (VAE), we identify the correlations between the material/process parameters and the thermal conductivity of heterogeneous dual-phase microstructures. Through high-throughput screening, UP, and the deep-generative VAE method, PSP relationships that are too complex can be revealed by exploiting the materials’ design space with an emphasis on microstructures. As a last point, we demonstrate generative machine learning serves as a useful tool for inverse microstructure-centered materials design, and we demonstrate this by examining the inverse design of thermal conductivity in nano-structured materials. Here, the results reveal the effects of morphology, volume fraction, characteristic length scale, and the individual thermal diffusivity of phases on the thermal conductivity of dual-phase alloys. Our findings emphasize the advantages of high-throughput phase-field modeling and generative deep learning for linking PSP and inverse microstructure-centered materials design.

36 MATERIALS SCIENCE↗

High-Resolution Scanning Coded-Mask-Based X-ray Multi-Contrast Imaging and Tomography

Near-field X-ray speckle tracking has been used in phase-contrast imaging and tomography as an emerging technique, providing higher contrast images than traditional absorption radiography. Most reported methods use sandpaper or membrane filters as speckle generators and digital image cross-correlation for phase reconstruction, which has either limited resolution or requires a large number of position scanning steps. Recently, we have proposed a novel coded-mask-based multi-contrast imaging (CMMI) technique for single-shot measurement with superior performance in efficiency and resolution compared with other single-shot methods. We present here a scanning CMMI method for the ultimate imaging resolution and phase sensitivity by using a coded mask as a high-contrast speckle generator, the flexible scanning mode, the adaption of advanced maximum-likelihood optimization to scanning data, and the multi-resolution analysis. Scanning CMMI can outperform other speckle-based imaging methods, such as X-ray speckle vector tracking, providing higher quality absorption, phase, and dark-field images with fewer scanning steps. Scanning CMMI is also successfully demonstrated in multi-contrast tomography, showing great potentials in high-resolution full-field imaging applications, such as in vivo biomedical imaging.

Qiao, Zhi (ORCID:0000000233928732)↗

Global and local current sheet thickness estimates during the late growth phase

The thinning and intensification of the cross tail current sheet during the substorm growth phase are analyzed during the CDAW 6 substorm (22 Mar. 1979) using two complementary methods. The magnetic field and current sheet development are determined using data from two spacecraft and a global magnetic field model with several free parameters. These results are compared with the local calculation of the current sheet location and structure previously done by McPherron et al. Both methods lead to the conclusion that an extremely thin current sheet existed prior to the substorm onset, and the thicknesses estimated by the two methods at substorm onset agree relatively well. The plasma data from the ISEE 1 spacecraft at 13 R(sub E) show an anisotropy in the low energy electrons during the growth phase which disappears just before the substorm onset. The global magnetic model results suggest that the field is sufficiently stretched to scatter such low energy electrons. The strong stretching may improve the conditions for the growth of the ion tearing instability in the near Earth tail at substorm onset.

Pulkkinen, T. I.↗

The mechanics of vortex pairing in a circular jet under controlled excitation

The vortex pairing phenomenon in the near field circular jet under controlled excitation is monitored by the method of phase-averaging. Hot-wire measurements were made in a 7.62 cm diameter air at Re(D) = 32,000, excited at St(D) = 0.85 with an excitation amplitude of 3%. The trajectories of the vortex centers and their longitudinal and transverse convection velocities during the pairing program are measured. Spatial distributions of the average velocity, vorticity, coherent Reynolds numbers, streamlines and pseudo-streamfunction are presented at a particular phase of the pairing. These represent field measurements at one particular time and do not involve the use of Taylor hypothesis.

Zaman, K. B. M. Q.↗

Propagation method and planting density influence canopy developmental transition and biomass productivity in Miscanthus × giganteus

Understanding how establishment practices influence the mechanisms underlying Miscanthus × giganteus (miscanthus) productivity and canopy development is critical for optimizing management. Data was collected during the juvenile (2011–2013) and mature (2024) phases of a long-term field experiment established in Urbana, Illinois, to evaluate the effects of propagation method (plug propagation [PP] and rhizome propagation [RP]), planting density (1.0, 0.75, and 0.25 plants m⁻²), and nitrogen application (0 and 67 kg N ha⁻¹) on end-of-season biomass yield, tiller mass, tiller density, and tiller height. Linear regression models identified the dominant predictors of yield across stand ages and management regimes. Planting density, nitrogen (N) application, and propagation method significantly influenced early yield and canopy development. During the juvenile phase, biomass yield was driven by tiller density due to canopy expansion; in the mature phase, yield became driven by tiller mass. The PP plots produced higher tiller density than the RP plots, resulting in faster canopy closure and higher juvenile-phase yields. Rhizome-propagated (RP) plots produced lower tiller density, but individual tillers were 3.3–6.4 g tiller −1 heavier than PP tillers. After the canopy reached equilibrium, the PP and RP yields were similar because greater RP tiller mass compensated for its lower tiller density. Higher planting density resulted in greater yield and tiller density during the second year (2012), but this effect was absent from the third year (2013) onward. In the juvenile phase, N fertilization enhanced yield by 1.6–3.4 Mg ha −1 . Initiating fertilization in 2013 on unfertilized plots produced biomass similar to that in fertilized plots, suggesting yield recovery in the mature phase. These findings revealed that establishment strategies, including propagation method and planting density, influence juvenile miscanthus canopy development and productivity, transitioning from tiller-density- to mass-dominated yields, but not mature phase productivity.

09 BIOMASS FUELS↗

Microemulsions in the driven Widom-Rowlinson lattice gas

An investigation of the two-dimensional Widom-Rowlinson lattice gas under an applied drive uncovered a remarkable nonequilibrium steady state in which uniform stripes (reminiscent of an equilibrium lamellar phase) form perpendicular to the drive direction [R. Dickman and R. K. P. Zia, Phys. Rev. E 97, 062126 (2018)]. Here we study this model at low particle densities in two and three dimensions, where we find a disordered phase with a characteristic length scale (a “microemulsion”) along the drive direction. Further we develop a continuum theory of this disordered phase to derive a coarse-grained field-theoretic action for the nonequilibrium dynamics. The action has the form of two coupled driven diffusive systems with different characteristic velocities, generated by an interplay between the particle repulsion and the drive. We then show how fluctuation corrections in the field theory may generate the characteristic features of the microemulsion phase, including a peak in the static structure factor corresponding to the characteristic length scale. This work lays the foundation for understanding the stripe phenomenon more generally.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Multi-Scale Computational Modeling of Two-Phased Metal Using GMC Method

A multi-scale computational model for determining plastic behavior in two-phased CMSX-4 Ni-based superalloys is developed on a finite element analysis (FEA) framework employing crystal plasticity constitutive model that can capture the microstructural scale stress field. The generalized method of cells (GMC) micromechanics model is used for homogenizing the local field quantities. At first, GMC as stand-alone is validated by analyzing a repeating unit cell (RUC) as a two-phased sample with 72.9% volume fraction of gamma'-precipitate in the gamma-matrix phase and comparing the results with those predicted by finite element analysis (FEA) models incorporating the same crystal plasticity constitutive model. The global stress-strain behavior and the local field quantity distributions predicted by GMC demonstrated good agreement with FEA. High computational saving, at the expense of some accuracy in the components of local tensor field quantities, was obtained with GMC. Finally, the capability of the developed multi-scale model linking FEA and GMC to solve real life sized structures is demonstrated by analyzing an engine disc component and determining the microstructural scale details of the field quantities.

nickel alloys↗

An early solar dynamo prediction: Cycle 23 is approximately cycle 22

In this paper, we briefly review the 'dynamo' and 'geomagnetic precursor' methods of long-term solar activity forecasting. These methods depend upon the most basic aspect of dynamo theory to predict future activity, future magnetic field arises directly from the magnification of pre-existing magnetic field. We then generalize the dynamo technique, allowing the method to be used at any phase of the solar cycle, through the development of the 'Solar Dynamo Amplitude' (SODA) index. This index is sensitive to the magnetic flux trapped within the Sun's convection zone but insensitive to the phase of the solar cycle. Since magnetic fields inside the Sun can become buoyant, one may think of the acronym SODA as describing the amount of buoyant flux. Using the present value of the SODA index, we estimate that the next cycle's smoothed peak activity will be about 210 +/- 30 solar flux units for the 10.7 cm radio flux and a sunspot number of 170 +/- 25. This suggests that solar cycle #23 will be large, comparable to cycle #22. The estimated peak is expected to occur near 1999.7 +/- 1 year. Since the current approach is novel (using data prior to solar minimum), these estimates may improve when the upcoming solar minimum is reached.

Schatten, Kenneth H.↗

An Amplitude-Based Estimation Method for International Space Station (ISS) Leak Detection and Localization Using Acoustic Sensor Networks

The development of a robust and efficient leak detection and localization system within a space station environment presents a unique challenge. A plausible approach includes the implementation of an acoustic sensor network system that can successfully detect the presence of a leak and determine the location of the leak source. Traditional acoustic detection and localization schemes rely on the phase and amplitude information collected by the sensor array system. Furthermore, the acoustic source signals are assumed to be airborne and far-field. Likewise, there are similar applications in sonar. In solids, there are specialized methods for locating events that are used in geology and in acoustic emission testing that involve sensor arrays and depend on a discernable phase front to the received signal. These methods are ineffective if applied to a sensor detection system within the space station environment. In the case of acoustic signal location, there are significant baffling and structural impediments to the sound path and the source could be in the near-field of a sensor in this particular setting.

Tian, Jialin↗