Investigating evolution of voids in Al2219 using 3D characterization and crystal plasticity simulations
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This is the presentation I will give at COMPLAS 2025 conference highlighting our latest advancements in integrating deep learning for for quantification of damage.
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Commonly studied equatomic single-phase FCC high entropy alloys based on 3d transition metals like NiCoFeCr do not provide adequate strength and radiation resistance at high doses for nuclear structural applications. In the current study, the major alloying effects like lattice distortion, ordering and clustering tendencies were investigated by adding low concentration of Pd, Al, or Cu respectively to study the doping effects on the ion irradiation response of NiCoFeCr alloy. The alloys were irradiated with 3 MeV Ni 2+ ions at 500 °C to a fluence of 1 × 10 17 /cm 2 at a beam flux of approximately 2.8 × 10 12 ions/cm 2 /s. The microstructural evolution upon irradiation i.e., formation of dislocation networks, radiation induced segregation and precipitation, and void formation were studied in detail. Further, post-irradiation characterization results showed that a Pd addition leads to a high void nucleation rate but controlled void growth, which may be attributed to increased lattice distortion. In Al added HEA, our microstructural analysis indicates that radiation induced ordered L1 2 precipitates do not affect void swelling significantly. Cu addition led to Cu precipitation that drastically suppressed dislocation density and void swelling of the alloy. Additionally, a model was developed to qualitatively describe the trend in void swelling of typical FCC alloys under ion irradiation. This model was able to qualitatively explain the suppression and reappearance of void swelling in ion irradiated alloys that generally occurs near the region with peak implanted ion concentration.
Here, this study investigates the microstructural evolution of pure tungsten irradiated under thermal-neutron shielded and mixed spectrum conditions in the High Flux Isotope Reactor (HFIR). Four samples were irradiated at temperatures from 570 °C to 1130 °C up to 0.73 dpa. Neutron spectrum significantly influenced the accumulation of transmutation products, with Re+Os content estimated at ∼0.3–0.6% under thermal-neutron shielded conditions and ∼5.2% under the mixed spectrum condition. Irradiation temperature strongly influences tungsten’s microstructure, with dislocation loops and fine voids forming at lower temperatures and only larger voids and Re/Os segregation observed at higher temperature. Under thermal-neutron shielded conditions, dislocation loops and voids were observed at 570 °C and 790 °C. At the highest irradiation temperature (1130 °C), dislocation loops were no longer observed, while larger but less dense voids remained. Re and Os segregation to void surfaces was evident at 790 °C and 1130 °C, though no precipitation was observed. In contrast, under the mixed-spectrum condition, both spherical and needle-like Re/Os-rich precipitates were observed, frequently accompanied by large voids. Dislocation loops were not observed, but loop-like contrast within the precipitates suggests they may have nucleated on pre-existing loops. Irradiation-induced hardening was assessed for the shielded samples at 570 °C and 790 °C. Dispersed barrier hardening (DBH) analysis, based on TEM-resolved defects, revealed that voids were the dominant contributors to hardening, consistent with literature results. A schematic model is proposed to describe defect and precipitate evolution in tungsten under fusion-relevant transmutation-to-dpa conditions.
Low-density cosmic voids gravitationally lens the cosmic microwave background (CMB), leaving a negative imprint on the CMB convergence |$\kappa$|. This effect provides insight into the distribution of matter within voids, and can also be used to study the growth of structure. We measure this lensing imprint by cross-correlating the Planck CMB lensing convergence map with voids identified in the Dark Energy Survey Year 3 (DES Y3) data set, covering approximately 4200 deg|$^2$| of the sky. We use two distinct void-finding algorithms: a 2D void-finder that operates on the projected galaxy density field in thin redshift shells, and a new code, Voxel, which operates on the full 3D map of galaxy positions. We employ an optimal matched filtering method for cross-correlation, using the Marenostrum Institut de Ciències de l’Espai N-body simulation both to establish the template for the matched filter and to calibrate detection significances. Using the DES Y3 photometric luminous red galaxy sample, we measure |$A_\kappa$|, the amplitude of the observed lensing signal relative to the simulation template, obtaining |$A_\kappa = 1.03 \pm 0.22$| (|$4.6\sigma$| significance) for Voxel and |$A_\kappa = 1.02 \pm 0.17$| (|$5.9\sigma$| significance) for 2D voids, both consistent with Lambda cold dark matter expectations. We additionally invert the 2D void-finding process to identify superclusters in the projected density field, for which we measure |$A_\kappa = 0.87 \pm 0.15$| (|$5.9\sigma$| significance). The leading source of noise in our measurements is Planck noise, implying that data from the Atacama Cosmology Telescope, South Pole Telescope and CMB-S4 will increase sensitivity and allow for more precise measurements.
Accurate prediction of pebble packing structure is important for pebble bed reactors because the spatial distribution of void fraction directly affects coolant flow, pressure drop, heat transfer, and neutronic behavior. However, experimentally validated DEM studies that directly evaluate local void-fraction structure in reactor-relevant pebble beds remain limited. In this work, the pebble bed experiment conducted at Missouri University of Science and Technology is simulated using the graphics processing unit (GPU)-based discrete element method (DEM) code Chrono::GPU. The study focuses on evaluating the ability of Chrono::GPU to reproduce the packing arrangement and void-fraction distribution of a randomly packed spherical pebble bed. The DEM results are first verified against established radial void-fraction correlations, including the Mueller and Vortmeyer-Schuster models, to assess the predicted bulk porosity, near-wall behavior, and oscillatory packing structure. The simulation is then verified against reference DEM data and validated against gamma-ray computed tomography (CT) experimental data at three axial locations. The Chrono::GPU results reproduce the main features of the experimental packing, including the high void fraction near the wall, the first near-wall trough, and the damped oscillatory radial profile caused by wall-induced ordering. Quantitative comparison with DEM data and the CT-based radial profiles shows good agreement, with mean absolute errors on the order of 0.07 and root-mean-square errors below 0.09 for the averaged profiles. These results demonstrate that Chrono::GPU can accurately capture the void-fraction structure of spherical pebble beds and provides a reliable DEM framework for future pebble bed reactor packing, recycling, and thermal-hydraulic studies.