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This is a general information article of the ICFA Beam Dynamics Newsletter No. 81. Thus, it contains two forewords from the Editor-in-Chief and the issue editor, a workshop and conference report section, a recent Ph.D. thesis section, a forthcoming beam dynamics events section, and a section of announcements from the beam dynamics panel.
Coherent Synchrotron Radiation (CSR) is an important and often detrimental effect in particle accelerators. While one-dimensional models have been successfully used to design and explain the behavior of modern machines, questions remain about their domain of validity. In recent years, two- and three-dimensional models have been developed that are amenable to efficient numerical computation. This article gives an overview of CSR computation from its discovery through the present state of the art.
Here, 132 Sn + 124 Sn collisions at a beam energy of 270 MeV/nucleon were performed at the Radioactive Isotope Beam Factory (RIBF) in RIKEN to investigate the nuclear equation of state. Reconstructing the impact parameter is one of the important tasks in the experiment as it relates to many observable. In this work, we employ three commonly used algorithms in machine learning, the artificial neural network (ANN), the convolutional neural network (CNN), and the light gradient boosting machine (LightGBM), to determine the impact parameter by analyzing either the charged particle spectra or several features simulated with events from the ultrarelativistic quantum molecular dynamics (UrQMD) model. To closely imitate experimental data and investigate the generalizability of the trained machine learning algorithms, incompressibility of nuclear equation of state and the in-medium nucleon-nucleon cross sections are varied in the UrQMD model to generate the training data. The mean absolute error Δb between the true and the predicted impact parameter is smaller than 0.45 fm if training and testing sets are sampled from the UrQMD model with the same parameter set. However, if training and testing sets are sampled with different parameter sets, Δb would increase to 0.8 fm. The generalizability of the trained machine learning algorithms suggests that these machine learning algorithms can be used reliably to reconstruct the impact parameter in experiment.
This paper reports on ongoing research in the area of numerical simulations of hybrid plume dynamics, and describes the basic mechanisms for energy and particle transport. The solution approach assumes cylindrical geometry and includes a multiplicity of atomic reactions, as well as the presence of a strong magnetic field. The principal reactions are electron impact ionization, and resonant charge exchange between hot ions and cold neutrals. Radial particle and energy transport is mainly by diffusion. The present description includes a modified Bohm diffusion model for plasma in the core of the plume, and classical neutral particle diffusion in the cooler regions of the flow. Neutrals are allowed to 'free stream' in the low density regions, where the collision mean-free-path becomes comparable, or even larger than the characteristic dimensions of the system. A parameteric scan of the various regimes of interest has been completed and the most significant results are presented.
Recent studies on counterstreaming plasma expansions are summarized. The basic phenomenon of plasma expansion is reviewed, and results from one-dimensional simulations of counterstreaming plasma expansion are discussed. Results from simulations based on an electrostatic particle-in-cell code, in which the dynamics of both the electrons and ions are exactly followed, are discussed. The formation of electrostatic shocks is addressed. Finally, results are presented on the ionospheric plasma expansion along the geomagnetic flux tubes by solving the hydrodynamic equations.
When two bodies of liquid merge, their interfaces must also rupture and rearrange into one. Virtually no information is available concerning the small-scale dynamics of this process. Molecular dynamics simulations of coalescence in systems of about 10,000 Lennard-Jones particles have been performed, arranged so as to mimic laboratory experiments on dense liquids. The coalescence event begins when molecules near the boundary of one liquid body thermally fluctuate into the range of attraction of the other, forming a string of mutually attracting molecules. These molecules gradually thicken into a tendril, which continues to thicken as the bodies smoothly combine in a zipper-like merger.
Both Saturn's rings and planetesimal disks are made up of particles in Keplerian orbits. Inelastic collisions between these particles regulate their dynamical evolution and possible aggregation. We present an experiment to simulate glancing collisions in Saturn's rings and in planetesimal disks and thus measure contributions to the energy loss for both normal and tangential velocity components. In this experiment, a spherical iceball mounted on a long-period, two dimensional pendulum is made to impact a flat ice surface in a low-temperature environment. This paper describes the experimental apparatus in detail and presents results for smooth unfrosted surfaces. The energy loss for tangential motion is suprisingly low, indicating that very little friction is present at low impact speeds for relatively smooth ice surfaces and temperatures near 100 K. We have also investigated room-temperature collisions of a rubber ball on a rough surface to understand the energy loss in situations where the tangential friction force is not small. In this analogous case, the energy loss is maximum for impact angles in the range 45 deg-60 deg.
Because of the importance and complexity of dynamical, microphysical, and radiative processes taking place near cloud edges, the transition zone between clouds and cloud free air has been the subject of intense research both in the ASR program and in the wider community. One challenge in this research is that the one-dimensional (1D) radiative models widely used in both remote sensing and dynamical simulations become less accurate near cloud edges: The large horizontal gradients in particle concentrations imply that accurate radiative calculations need to consider multi-dimensional radiative interactions among areas that have widely different optical properties. This study examines the way the importance of multidimensional shortwave radiative interactions changes as we approach cloud edges. For this, the study relies on radiative simulations performed for a multiyear dataset of clouds observed over the NSA, SGP, and TWP sites. This dataset is based on Microbase cloud profiles as well as wind measurements and ARM cloud classification products. The study analyzes the way the difference between 1D and 2D simulation results increases near cloud edges. It considers both monochromatic radiances and broadband radiative heating, and it also examines the influence of factors such as cloud type and height, and solar elevation. The results provide insights into the workings of radiative processes and may help better interpret radiance measurements and better estimate the radiative impacts of this critical region.
Electrical transport in semiconducting and metallic particle suspensions is an enabling feature of emerging grid-scale battery technologies. Although the physics of the transport process plays a key role in these technologies, no universal framework has yet emerged. Here, we examine the important contribution of shear flow to the electrical transport of non-Brownian suspensions. We find that these suspensions exhibit a strong dependence of the transport rate on the particle volume fraction and applied shear rate, which enables the conductivity to be dynamically changed by over 10 7 decades based on the applied shear rate. We combine experiments and simulations to conclude that the transport process relies on a combination of charge and particle diffusion with a rate that can be predicted using a quantitative physical model that incorporates the self-diffusion of the particles.
An increasing interest in high quality and high current electron beams necessitates a thorough understanding and prediction of coherent synchrotron radiation effects. The self-interaction of charged particles in a beam undergoing synchrotron motion is a physically significant process that is all too often computationally intensive with very little analytical results to rely on for the general case. The coherent spectrum of this interaction is of utmost importance to the design of free electron lasers (FELs) and an accurate assessment is imperative for their design. This work presents a novel implementation to the numerical simulation of charged particle beams. The simulation is a self-consistent approach including the self-fields generated by the beam of which coherent synchrotron radiation effects are of primary interest. A particle-in-cell model is used where a planar beam sampled by point particles is deposited on an encompassing grid at each timestep. The electromagnetic fields are calculated on the grid using the retarded potentials according to causality. The electromagnetic forces from the fields are interpolated on each particle which in turn advance in time. The simulation is benchmarked against well-established results for coherent synchrotron radiation effects. In addition, studies are provided that show the convergence of simulation results for increasing resolution. A study into the transverse beam size effects on beam dynamics is performed as well as a proof of concept where the simulation is used by a genetic algorithm to optimize the design parameters of a beam lattice. The results of these studies in tandem verify the efficacy of the simulation for its practical use in accelerator design or the study of synchrotron radiation effects
As the nuclear industry explores new fuel designs to accommodate increased burnup, studying fuel behavior during loss-of-coolant accidents is essential for ensuring the safe operation of light-water reactors. Here, this study presents a computational model to simulate the intricate three-phase flow dynamics of fuel dispersal following cladding breach, where solid fuel particles and carrier fission gases interact with the surrounding liquid or gas medium. The model utilizes a Eulerian three-fluid framework to depict the phase interaction within interpenetrating continua, treating fragmented fuel as granular material. Closure relationships for bulk-flow properties of the solid phase and detailed interfacial exchange terms in high-temperature, high-speed gas-solid-liquid flow scenarios are proposed. The model was validated using adiabatic experimental results in the literature, focusing on characterizing fuel dispersal behavior during loss-of-coolant accidents conditions. Settlement characteristics of the solids, quantified by the probability distribution of equivalent particles, closely matched probability density functions reported in experimental studies. Key highlights of this study include the theoretical description of a coupled three-phase flow with phase change, interpretation of the high-pressure boundary conditions and insights into the transient behavior of gas-solid-liquid phase dynamics during a simulated high-speed dispersal event.
A fast-mode shock can form in the front of reconnection outflows and has been suggested as a promising site for particle acceleration in solar flares. Recent developments in the study of magnetic reconnection have shown that numerous plasmoids can be produced in a large-scale current layer. Here we investigate the dynamical modulation of electron acceleration in the looptop region when plasmoids intermittently arrive at the shock by combining magnetohydrodynamics simulations with a particle kinetic model. As plasmoids interact with the shock, the looptop region exhibits various compressible structures that modulate the production of energetic electrons. The energetic electron population varies rapidly in both time and space. The number of 5–10 keV electrons correlates well with the compression area, while that of >50 keV electrons shows good correlation with the strong compression area but only moderate correlation with shock parameters. We further examine the impacts of the first plasmoid, which marks the transition from a quasi-steady shock front to a distorted and dynamical shock. The number of energetic electrons is reduced by ~20% at 15–25 keV and nearly 40% for 25–50 keV, while the number of 5–10 keV electrons increases. In addition, the electron energy spectrum above 10 keV evolves softer with time. We also find that double or even multiple distinct sources can develop in the looptop region when the plasmoids move across the shock. Our simulations have strong implications to the interpretation of nonthermal looptop sources, as well as the commonly observed fast temporal variations in flare emissions, including the quasi-periodic pulsations.
Abstract. A key constraint of particle-based methods for modeling cloud microphysics is the conservation of total particle number, which is required for computational tractability. The process of collisional breakup poses a particular challenge to this framework, as breakup events often produce many droplet fragments of varying sizes, which would require creating new particles in the system. This work introduces a representation of collisional breakup in the so-called “superdroplet” method which conserves the total number of superdroplets in the system. This representation extends an existing stochastic collisional-coalescence scheme and samples from a fragment size distribution in an additional Monte Carlo step. This method is demonstrated in a set of idealized box model and single-column warm-rain simulations. We further discuss the effects of the breakup dynamic and fragment size distribution on the particle size distribution, hydrometeor population, and microphysical process rates. Box model experiments serve to characterize the impacts of properties such as coalescence efficiency and fragmentation function on the relative roles of collisional breakup and coalescence. The results demonstrate that this representation of collisional breakup can produce a stationary particle size distribution, in which breakup and coalescence rates are approximately equal, and that it recovers expected behavior such as a reduction in precipitate-sized particles in the column model. The breakup algorithm presented here contributes to an open-source pythonic implementation of the superdroplet method, PySDM, which will facilitate future research using particle-based microphysics.
The structure and resulting physical properties of aerosol deposited particulate films are governed by the size distribution and morphology of the depositing particles as well as the physics governing particle deposition. While particulate film deposition processes are qualitatively understood, the link between particle characteristics and deposition physics to film properties has not been probed systematically. Here we apply a combination of Langevin dynamics deposition simulations, Monte Carlo pore size distribution calculations, and predictions of thermal conductivities to better establish such process parameter-film property relationships. Furthermore, we account for partial coalescence, polydispersity, and aggregate deposition. We establish that the deposition of polydisperse and aggregated particles leads to broadening of the pore size distribution function and an increase in the mode pore size. Via a non-continuum gas conductivity model, we show that aerosol deposited films can achieve porosities and thermal conductivities similar to conventional aerogels.
A comprehensive model is presented for mathematically describing the isothermal, non-reactive, fluid dynamics of a mixture of particles in a gas.
Abstract Shocked particle-laden flows are important to many natural and industrial processes. When simulating these systems, artificial viscosity is often required to prevent numerical artifacts, such as ringing, from arising in the pressure and density fields. The linear and quadratic coefficients of the artificial viscosity determine the amount of smoothing that occurs in these fields. For particle-laden flows, however, many of the fluid–particle interaction forces, for example, the pressure gradient force and unsteady forces, depend on gradients in the fluid fields. Furthermore, while the shock passes over a particle, these forces can be more dominant than drag. This means that the artificial viscosity coefficients affect how a particle and fluid interact when simulating shocked particle systems. Here this effect is investigated for isolated particles and for a particle curtain using a staggered grid Lagrangian approach. The artificial viscosity coefficients have a significant impact on the maximum force that a fluid imparts to a particle, which is important for determining whether a particle will break up in response to the shock. Furthermore, it is found that the density ratio between the particle and the fluid is important in determining whether the artificial viscosity coefficients have a significant impact on the particle’s motion.
Studying high-energy collisions of composite particles, such as hadrons and nuclei, is an outstanding goal for quantum simulators. However, preparation of hadronic wave packets has posed a significant challenge, due to the complexity of hadrons and the precise structure of wave packets. This has limited demonstrations of hadron scattering on quantum simulators to date. Observations of confinement and composite excitations in quantum spin systems have opened up the possibility to explore scattering dynamics in spin models. In this article, we develop two methods to create entangled spin states corresponding to wave packets of composite particles in analog quantum simulators of Ising spin Hamiltonians. One wave-packet preparation method uses the blockade effect enabled by beyond-nearest-neighbor Ising spin interactions. The other method utilizes a quantum-bus-mediated exchange, such as the native spin-phonon coupling in trapped-ion arrays. With a focus on trapped-ion simulators, we numerically benchmark both methods and show that high-fidelity wave packets can be achieved in near-term experiments. We numerically study scattering of wave packets for experimentally realizable parameters in the Ising model and find inelastic-scattering regimes, corresponding to particle production in the scattering event, with prominent and distinct experimental signals. Our proposal, therefore, demonstrates the potential of observing inelastic scattering in near-term quantum simulators.
Here, we developed the core components of the AI-aided multiple time stepping algorithm for multiscale modeling of cell dynamics. This algorithm was implemented and analyzed on two supercomputer architectures with an application of simulating the aggregation of 250 platelets, or 102 million particles. To scale on these computers with complex memory and network architectures with GPUs, we devised a biomechanics-informed task mapping scheme to optimize load imbalance, communications, and memory utilization. Our simulations, scaling well up to 192 nodes on a Summit-like supercomputer with a peak speed of 11 petaflops, achieved a rate of 423 μs/day which is 500 times faster than the conventional algorithm using static time step and this has enabled studies of record size blood clots at record spatial–temporal resolutions. Additionally, we discovered the sensitive dependence of the scalability and execution time on the methods of decomposition, CPU–GPU coupling, and task mapping.