3D Simulations Research Articles

Heavy rainfall and moisture susceptibility of pavement foundation: A case study coupling finite element method and MnROAD moisture monitoring data

The Midwest region, including Minnesota, has been experiencing increased heavy precipitation events due to climate change, and the Minnesota Department of Transportation (MnDOT) is currently investigating the effect of climate change on pavement foundation and other transportation assets. As part of this effort, a study was conducted to investigate the impact of heavy rainfall on pavement foundation performance by focusing on moisture dynamics and resilient modulus changes in the pavement base layer. This study is aimed at understanding the adverse effects of heavy rainfall on moisture fluxes within pavement foundation and corresponding stiffness of the base aggregate layer. A two-step approach was adopted for predicting changes in saturation when estimating corresponding resilient modulus values using the resilient modulus prediction equation employed in AASHTOWare Pavement Mechanistic-Empirical (ME) Design. PLAXIS 3D, a finite-element analysis tool, was utilized to simulate the movement of moisture within the pavement layers under varying heavy rainfall scenarios. By incorporating predicted saturation from PLAXIS 3D simulations into the Pavement ME equation, corresponding resilient modulus values were estimated for the base layer. To ensure its accuracy and reliability, the model was validated using field sensor data from the MnROAD facility. Multiple linear regression models were developed to provide a means for estimating resilient modulus changes due to heavy rainfall. This study highlights the importance of considering moisture effects in pavement design and maintenance in regions prone to heavy rainfall events, and findings can be used by transportation agencies as part of their transportation/geotechnical asset management programs.

LES of Premixed Turbulent Combustion Using Filtered Tabulated Chemistry

AbstractThe filtered tabulated chemistry (FTACLES) approach utilizes data from pre-tabulated explicitly filtered 1D flame profiles for closure of the LES-filtered transport terms. Different methodologies are discussed to obtain a suitable progress variable c from detailed chemistry calculations of a methane/air flame. In this context, special focus is placed on the analytical modeling of the reaction source term using series of parameterized Gaussians. For increasing effective filter sizes in LES (i.e. including the flame thickening) the precise shape of the reaction rate profile becomes less and less relevant. In particular, it is shown that for one-step chemistry, a single Gaussian is sufficient to derive an explicitly expressible 1D flame profile with a prescribed laminar flame speed and thermal flame thickness. The resulting artificial flame profile is shown to have similarities with profiles based on carbon chemistry and detailed reaction mechanisms. Next, the behavior of the filtered c-transport equation is analyzed and several possible closure methods are compared for a wide range of filter widths. It is shown that the unclosed contribution of the filtered diffusion term can be combined with the subgrid convection term, thus simplifying the FTACLES formulation. The model is implemented in OpenFOAM and validated in 1D for a variety of LES filter sizes in combination with artificial flame thickening. A power-law-based wrinkling model is modified for use with artificial flame thickening and combined with the FTACLES model to enable 3D simulations of a premixed turbulent Bunsen burner. The comparison of 3D Large Eddy Bunsen flame simulations at increasing levels of turbulence intensity shows a good match to experimental results for most investigated cases. In addition, the results are mostly insensitive to the variation of the mesh size.

Thermal regulation enhancement in multi-story office buildings: Integrating phase change materials into inter-floor void formers

Addressing the thermal loss from building envelopes is a crucial challenge and requires the exploration of various mitigation strategies. This study employs comprehensive 3D computational fluid dynamics simulations to investigate the incorporation of various bio-based Phase Change Materials (PCMs) including CrodaTherm (CT) 19, CT21, and CT24W, within inter-floor void formers between the roof-floor boundary in multi-story office buildings to optimize energy management during inactive periods in one of the adjacent units. Aside from the PCM type, the void former shape, its placement, and the combinations of PCMs and insulating layers are examined through heat flux and solid fraction plots as well as liquid fraction contours. Results highlight the exceptional performance of CT24W-integrated void formers, achieving a remarkable 36.74 % reduction in the 48-h average heat flux in comparison with the case without void formers, and offering lower heat flux values than the scenario with insulating layers for 89.38 h (endurance time). Based on the findings, with identical PCM volumes, the semi-ellipsoid PCM-integrated void former provides 11.07 % lower heat flux and 27.56 % longer endurance time compared to the one with a spherical shape. The results underscore PCM integration as a superior strategy over standalone insulation for managing thermal loss between apartment floors.

Device modeling of high performance and eco-friendly FAMASnI3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {FAMASnI}}_{3}$$\\end{document} based perovskite solar cell

Developing environmentally friendly and highly efficient inverted perovskite solar cells (PSCs) encounters significant challenges, specifically the potential toxicity and degradation of thin films in hybrid organic-inorganic photovoltaics (PV). We employed theoretical design strategies that produce hysteresis-reduced, efficient, and stable PSCs based on composition and interface engineering. The devices include a mixed-organic-cation perovskite formamidinium methylammonium tin iodide (FAMASnI3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {FAMASnI}}_{3}$$\\end{document}) as an absorber layer and zinc oxide (ZnO) together with a passivation film phenyl-C61-butyric acid methyl ester (PC61BM\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext {PC}}_{61}\ ext {BM}$$\\end{document}) as a double-electron transport layer (DETL). Furthermore, a nickel oxide (NiO) layer and a trap-free junction copper iodide (CuI) are used as a double-hole transport layer (DHTL). The optoelectronic characterization measurements were carried out to understand the physical mechanisms that govern the operation of the devices. The high power conversion efficiencies (PCEs) of 24.27% and 23.50% were achieved in 1D and 2D simulations, respectively. This study illustrates that composition and interface engineering enable eco-friendly perovskite solar cells, improving performance and advancing clean energy.

Predictive Placement of IC Chips using ANN-GA Approach for Efficient Thermal Cooling

In this research, numerical modelling is used to explore the heat transfer through natural convection capabilities of nine aluminum integrated circuit chips that are installed on substrate board. The goal is to figure out where on the substrate board these IC chips would be best placed if they were arranged differently. The dimensionless parameter (λ) plays a very essential role, and by applying a hybrid technique consisting of ANN and GA. ANSYS Icepack calculates IC chip temperature distributions in 3D steady state numerical simulations. It has been shown that the form, dimensions, and IC chips' substrate board positioning affects their operating temperature. In comparison to the strategies that have been used in the past, hybrid optimization is the strategy that has shown to be the most reliable in properly predicting how the IC chips would be arranged on the substrate board. It has been observed that higher values of one of these parameters lead to a reduction in the maximum temperature surplus. A correlation has been established to illustrate this relationship as it increases. The most favorable simulation outcomes are utilized to drive a genetic algorithm (GA), which identifies the optimal configuration ensuring that the temperatures of the heat sources remain well below their specified maximum operating conditions, as outlined in the data sheets. The maximum temperature variation between the lowest and highest extreme configurations ranges between 4 - 8%. The smallest size IC chip, U2 with high heat dissipation rate attains the maximum temperature in the configuration, however, the temperature variation for the low powered IC chips U3, U4 and U7 are very small. Found good agreement of both the data with an error band of 10%, and thus confirms the accuracy of the network.

Effect of baffles on efficiency of darrieus vertical axis wind turbines equipped with J-type blades

This study investigates the effects of incorporating baffles in J-type bladed vertical axis wind turbines. The objectives are to analyze the behavior of vortices and their impact on turbine performance and to explore various locations for placing the baffles along the J-type blades. The 3D numerical simulations of the current study applies the sliding mesh techniques to better model the rotational motion of blades about the turbine axis with respect to the wind. The results show that mounting zero-thickness baffles over the tips of the blades effectively reduces vortex leakage, minimizes tip vortices, and increases the pressure differential across the sides of J-type blades leading to a significant improvement of the turbine performance. At low tip speed ratios (TSRs) of 0.5, 0.75 and 1, in particular, the torque generation in comparison with conventional NACA0021 straight bladed turbines has been improved by up to 49.5 %, 38.2 %, and 28 %, respectively. Considering a wide range of TSRs, on average, the implementation of baffles results in a 17.5 % increase in torque generation. The effect of the baffle thickness on generated vortices and their evolution along the course of flow is also investigated to show how the zero-thickness baffles improves the blade aerodynamics.

Non-uniform knot (NUK) SIAC post-processing of flow fields produced through unstructured grid adaptation and optimization

As the finite element method (FEM) and the finite volume method (FVM), both their traditional and high-order variants, continue their proliferation into various applied engineering disciplines, adaptive mesh refinement and optimization strategies have increased in their importance when solving real-world computational fluid mechanics applications. The post-processing and visualization of the resulting flow fields present two significant analysis and visualization challenges. The first challenge is the handling of elemental continuity, which is often only C0 continuous (in continuous Galerkin methods) or piecewise discontinuous (in discontinuous Galerkin methods). The second challenge is that, depending on the flow regime and the geometric configurations for which adaptive meshing strategies are used, the meshes generated are often highly anisotropic. The (uniform knot) line-SIAC (L-SIAC) filter has been proposed as a way of handling elemental continuity issues in an accuracy-conserving manner with the added benefit of casting the data in a smooth context even if the representation is element discontinuous. In this paper, we demonstrate that the state-of-the-art adaptive L-SIAC filter, designed for mildly anisotropic meshes, suffers degradation in the quality of the post-processed solution when applied to the types of highly anisotropic meshes produced through adaptive mesh refinement and optimization. Hence, a new Non-Uniform Knot (NUK) L-SIAC filter is proposed that automatically conforms to the underlying mesh anisotropy. We demonstrate that the new filter behaves similarly to the adaptive L-SIAC filter when applied to uniform and mildly anisotropic meshes, and furthermore we show the superiority of the NUK L-SIAC filter when applied to highly anisotropic meshes. The newly formulated filter is applied to 2D canonical scalar fields and used to visualize 2D and 3D fluid flow simulation results.

Optimized design of planar solid oxide fuel cell interconnectors.

Solid oxide fuel cells (SOFCs) are vital for alternative energy, powering motors effi-ciently. They offer fuel versatility and waste heat recovery, making them ideal for various applications. Optimizing interconnector structures is crucial for SOFC advancement. This paper introduces a novel 2D simulation model for interconnector SOFCs, aiming to enhance their performance. We initially construct a single half-cell model for a conventional interconnector SOFC, ensuring model accuracy. Subsequently, we propose an innovative interconnector SOFC model, which outperforms the conventional counterpart in various aspects.

Physics-informed neural network simulation of thermal cavity flow

Physics-informed neural networks (PINNs) are an emerging technology that can be used both in place of and in conjunction with conventional simulation methods. In this paper, we used PINNs to perform a forward simulation without leveraging known data. Our simulation was of a 2D natural convection-driven cavity using the vorticity-stream function formulation of the Navier-Stokes equations. We used both 2D simulations across the x and z domains at constant Rayleigh (Ra) numbers and 3D simulations across the x, z and Ra domains. The 3D simulation was tested for a PINN’s ability to learn solutions in a higher-dimensional space than standard simulations. The results were validated against published solutions at Ra values of 103\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{3}$$\\end{document}, 104\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{4}$$\\end{document}, 105\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{5}$$\\end{document}, and 106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{6}$$\\end{document}. Both the 2D simulations and 3D simulations successfully matched the expected results. For the 2D cases, more training iterations were needed for the model to converge at higher Ra values (105\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^5$$\\end{document} and 106\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^6$$\\end{document}) than at lower Ra (103\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^3$$\\end{document} and 104\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^4$$\\end{document}) indicating increased nonlinear fluid-thermal coupling. The 3D case was also able to converge but, but it required more training than any of the 2D cases due to the curse of dimensionality. These results showed the validity of standard simulations via PINNs and the feasibility of higher-order parameter space solutions that are not possible using conventional methods. They also showcased the additional computational demand associated with increasing the dimensionality of the learned parameter space.

Analytical solutions and physical interpretation of a predator–prey system with Allee effect using fractional derivative operators

This study investigates the dynamics of a predator–prey system by incorporating the Allee effect. The exact analytical solutions of the system are explored using the generalized exponential function method and the Bäcklund transformation approach. The system is discussed using Conformable, M-truncated, and Beta time fractional derivative operators to provide a physical interpretation. The effects of these derivative operators on the solutions are demonstrated through various 2D and 3D graphical simulations. Furthermore, the impacts of physical parameters on the model are analysed, providing valuable insights into the dynamics of Predator–prey systems with the Allee effect. Our obtained solutions differ from those in the literature, owing to the methodologies employed and the choice of differential operators utilized. Nevertheless, we believe that the comparisons presented for the differential operators are effective and beneficial.

The emission mechanism of gamma-ray bursts from supernovae

The connection between gamma ray bursts and supernovae is studied using a temperature-dependent vacuum model. A harmonically bound particle–antiparticle system is consistent with both Hawking radiation and Casimir effect, therefore, the Maxwell–Sellmeier model correlates the speed of light to temperature. According to quantum field theory, Lorentz invariance is violated only for temperatures larger than [Formula: see text][Formula: see text]K. Introducing in the temperature distribution of a 2D simulation for a type Ia supernova the speed of light temperature dependence proposed in this paper, results a speed of light distribution. A theoretical snapshot of this distribution at an arbitrary distance is consistent with photon photo finish resulted from experiments. Variable speed of light shows that supernova could be accompanied by gamma-ray bursts.

Dimensionality and scales of preferential flow in soils of Shale Hills hillslope simulated using HYDRUS

AbstractPreferential flow (PF) processes are governed by subsurface soil structures at various scales. Still, model validation and mechanistic understanding of PF are very lacking. We hypothesize that PF at hillslope and larger scales cannot be described and quantified when neglecting small‐scaled spatially variable processes and simplifying the model dimensionality. The objective was to learn from comparing simulation results of multidimensional (1D, 2D, and 3D) and multiscale (pedon, catena, and catchment) modeling approaches with comprehensive datasets, and so as to evaluate PF simulations based on the Richards’ equation (solved by the HYDRUS software). Results showed limited alignment between 1D simulations and soil moisture data, mainly affected by vertical changes in porosity, permeability, and precipitation features. 2D and 3D simulations outperformed 1D models. 3D simulations provided satisfactory description of PF dynamics at the pedon scale, considering accurate representations of soil and bedrock structures for three dimensions (vertical, horizontal, and surrounding area). In 2D simulations at the pedon scale, models incorporating dual‐porosity and anisotropy of soils yielded more accurate predictions of water dynamics than single‐porosity and isotropic models. Furthermore, the application of 2D simulation at the catena scale identify PF pathways owing to the enhanced representation of the hydraulic connectivity between different locations along the slope. The results confirmed the significance of multidimensional and multiscale modeling approaches for PF simulations in hillslope hydrology. Considering the complexity and parameterization of 2D and 3D “bottom‐up” physically based models in representing spatial variability within and between soil profiles and/or underlying bedrock geology, the results contribute to creating a modeling framework applicable to identify the PF processes and thus their implications in managing water resources.

Supernova Explosions of the Lowest-mass Massive Star Progenitors

We here focus on the behavior of supernovae that technically explode in 1D (spherical symmetry). When simulated in 3D, however, the outcomes of representative progenitors of this class are quite different in almost all relevant quantities. In 3D, the explosion energies can be 2 to 10 times higher, and there are correspondingly large differences in the 56Ni yields. These differences between the 3D and 1D simulations reflect in part the relative delay to explosion of the latter and in the former the presence of protoneutron star convection that boosts the driving neutrino luminosities by as much as ∼50% at later times. In addition, we find that the ejecta in 3D models are more neutron-rich, resulting in significant weak r-process and 48Ca yields. Furthermore, we find that in 3D the core is an interesting, though subdominant, source of acoustic power. In summary, we find that though a model might be found theoretically to explode in 1D, one must perform supernova simulations in 3D to capture most of the associated observables. The differences between 1D and 3D models are just too large to ignore.

Acceleration of particle-in-cell simulations using sparse grid algorithms. I. Application to dual frequency capacitive discharges

The use of sparse particle-in-cell (PIC) algorithms to accelerate the standard explicit PIC scheme has recently been successfully applied in the context of single-frequency capacitively coupled plasma discharges [Garrigues et al., J. Appl. Phys. 129, 153303 (2021)]. We have extended the sparse PIC scheme to model dual-frequency capacitive discharges. Comparisons between standard and sparse PIC algorithms show that the plasma properties as well as the electron and ion distribution functions can be retrieved with a maximum error of 2%. This work opens the interest of using the sparse PIC algorithm to perform 2D and 3D simulations under real operating conditions of capacitively coupled plasma discharges.

Integrating Augmented Reality Technology in Magnetic Field Learning: A Systematic Literature Review

The aim of this study was to uncover research trends in the use of augmented reality (AR) in magnetic field learning from 2012 to 2023. The review in this study followed the guidelines of Kitchenham, B. and Charters, which consisted of three stages: planning, conducting, and reporting the review. During the planning stages of the review, 107 articles were identified using keywords. The remaining articles were examined according to inclusion and exclusion criteria, so that 21 articles were further processed. A review stage is then carried out, followed by reporting of the review results. The results of this study suggest that the use of augmented reality (AR) to teach magnetic fields is primarily focused on teaching high school students. The aspects that have attracted the most attention when analyzing the implementation of AR in magnetic field education are learning/academic performance, perception and attitude. The most commonly used forms of AR in the study of magnetic fields are mobile applications, AR-based learning experiments, 3D simulations, and real-time simulations, all of which aim to explain abstract concepts and facilitate experimental explorations. Quantitative research design is the most commonly used approach in this field. The primary data collection tools used in this study include achievement tests, surveys, and questionnaires.

Three-Dimensional Modeling of Anion Exchange Membrane Electrolysis: A Two-Phase Flow Approach

Anion exchange membrane water electrolyzers (AEMWEs) are attracting growing interest as a green hydrogen production technology. Unlike proton exchange membrane (PEM) systems, AEMWEs operate in an alkaline environment, allowing one to use less expensive, non-noble materials as catalysts for the reactions and non-fluorinated anion exchange polymer membranes. However, the performance and stability of AEMWEs strongly depend on the alkaline electrolyte concentration. In this work, a three-dimensional multi-physics model considering two-phase flow effects is applied to understand the impact of KOH electrolyte concentration and its flow rate on AEMWE performance, as well as on the current and gas volume fraction distributions. The numerical results were compared to experimental data published in the literature. For current densities above 1 A/cm2, a strongly non-uniform H2 and O2 gas volume distribution could be evidenced by the 3D simulations. Increasing the KOH electrolyte flow rate from 10 to 100 mL/min noticeably improves cell performance for current densities above 1 A/cm2. These results show the importance of accounting for the three-dimensional geometry of an AEMWE and two-phase flow effects to accurately describe its operation and performance.

Learned 1D Passive Scalar Advection to Accelerate Chemical Transport Modeling: A Case Study with GEOS-FP Horizontal Wind Fields

Abstract We developed and applied a machine-learned discretization for one-dimensional (1D) horizontal passive scalar advection, which is an operator component common to all chemical transport models (CTMs). Our learned advection scheme resembles a second-order accurate, three-stencil numerical solver but differs from a traditional solver in that coefficients for each equation term are output by a neural network rather than being theoretically derived constants. We subsampled higher-resolution simulation results—resulting in up to 16× larger grid size and 64× larger time step—and trained our neural-network-based scheme to match the subsampled integration data. In this way, we created an operator that has low resolution (in time or space) but can reproduce the behavior of a high-resolution traditional solver. Our model shows high fidelity in reproducing its training dataset (a single 10-day 1D simulation) and is similarly accurate in simulations with unseen initial conditions, wind fields, and grid spacing. In many cases, our learned solver is more accurate than a low-resolution version of the reference solver, but the low-resolution reference solver achieves greater computational speedup (500× acceleration) over the high-resolution simulation than the learned solver is able to (18× acceleration). Surprisingly, our learned 1D scheme—when combined with a splitting technique—can be used to predict 2D advection and is in some cases more stable and accurate than the low-resolution reference solver in 2D. Overall, our results suggest that learned advection operators may offer a higher-accuracy method for accelerating CTM simulations as compared to simply running a traditional integrator at low resolution. Significance Statement Chemical transport modeling (CTM) is an essential tool for studying air pollution. CTM simulations take a long computing time. Modeling pollutant transport (advection) is the second most computationally intensive part of the model. Decreasing the resolution not only reduces the advection computing time but also decreases accuracy. We employed machine learning to reduce the resolution of advection while keeping the accuracy. We verified the robustness of our solver with several generalization testing scenarios. In our 2D simulation, our solver showed up to 100 times faster simulation with fair accuracy. Integrating our approach to existing CTMs will allow broadened participation in the study of air pollution and related solutions.

Sidewall influence of varying free stream Mach number in ramp induced shock wave boundary layer interactions

This study investigates the three-dimensional (3D) effects introduced by the end walls for an aspect ratio of 1 in ramp-induced shock wave boundary layer interactions. The simulations are performed using a symmetry boundary condition in the spanwise direction at free-stream Mach numbers in 3D. The simulations are performed using an in-house compressible supersonic solver “OpenSBLIFVM”. Two free stream Mach numbers 2.5, and 3 are used in the current work, and the simulated results are compared with the aspect ratio 1 simulations by Mangalagiri and Jammy. The inflow is initialized with a similarity solution; its Reynolds number based on the boundary layer thickness is adjusted such that the Reynolds number at the start of the ramp is kept at 3×105 for all simulations. From the results, it is evident that the introduction of sidewalls resulted in a shorter centerline separation length when compared with the two-dimensional (2D) simulations. This contradicts the results at Mach 2 by Mangalgiri and Jammy where the vortex observed at Mach 2 in the central separation region disappeared with increasing free-stream Mach number. Additionally, the topology of interaction shifted from owl-like separation of the second kind to the first kind when the freestream Mach number increased from 2 to 2.5. It can be concluded that the interaction topology is crucial to the increase or decrease of the central separation length when compared to 2D simulations.

On the role of geometrically necessary dislocations in void formation and growth in response to shock loading conditions in wrought and additively manufactured Ta

This study investigates the role of geometrically necessary dislocations (GNDs) and microstructure on void nucleation and growth in wrought and additively manufactured (AM) tantalum subjected to high-strain rate loading. Multi-modal 3D data was collected using TriBeam tomography to calculate GND densities and their spatial relationship to voids. A microstructural comparison between the wrought and AM samples identified distinct void shapes and locations, with intragranular voids and more spherical voids frequently observed in the AM dataset. Results indicate that voids preferentially form at both high-angle grain boundaries and low-angle subgrain boundaries, the latter of which are frequently observed in the AM material. Through a radial distribution analysis of all voids in the datasets, significant GND localization to near-void-surface regions was observed in both samples. 3D crystal plasticity simulations were employed to extend the experimental observations, revealing higher void growth rates in [111] oriented grains when compared to [001] grains. The simulations also suggest that GNDs can be generated as part of the void growth process, with more GND accumulation for growth in a [111] grain than a [001] grain. These findings provide valuable insights into the links between nanoscale void nucleation, mesoscale void growth, and microstructural effects in dynamically loaded tantalum.

Streaming Instability and Turbulence: Conditions for Planetesimal Formation

The streaming instability (SI) is a leading candidate for planetesimal formation, which can concentrate solids through two-way aerodynamic interactions with the gas. The resulting concentrations can become sufficiently dense to collapse under particle self-gravity, forming planetesimals. Previous studies have carried out large parameter surveys to establish the critical particle to gas surface density ratio (Z), above which SI-induced concentration triggers planetesimal formation. The threshold Z depends on the dimensionless stopping time (τ s , a proxy for dust size). However, these studies neglected both particle self-gravity and external turbulence. Here, we perform 3D stratified shearing box simulations with both particle self-gravity and turbulent forcing, which we characterize via a turbulent diffusion parameter, α D. We find that forced turbulence, at amplitudes plausibly present in some protoplanetary disks, can increase the threshold Z by up to an order of magnitude. For example, for τ s = 0.01, planetesimal formation occurs when Z ≳ 0.06, ≳0.1, and ≳0.2 at α D = 10−4, 10−3.5, and 10−3, respectively. We provide a single fit to the critical Z required for the SI to work as a function of α D and τ s (although limited to the range τ s = 0.01–0.1). Our simulations also show that planetesimal formation requires a mid-plane particle-to-gas density ratio that exceeds unity, with the critical value being largely insensitive to α D. Finally, we provide an estimation of particle scale height that accounts for both particle feedback and external turbulence.