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At least 271 records · Page 15

A corrected method for Coulomb scattering in arbitrarily weighted particle-in-cell plasma simulations

A physically and statistically accurate method to perform binary, pairwise Coulomb collisions for arbitrarily-weighted macroparticles in particle-in-cell simulations is described. The method is shown to conserve global energy and momentum on average and to produce physically accurate scattering frequencies. This is justified using the statistically expected values of the method and through a series of numerical tests showing that the method is valid independently of macroparticle weight. This novel method is required due to an error in previous work on binary scattering, initially by Nanbu and Yonemura [J. Comp. Phys. 145, 639 (1998)] and applied to subsequent work, that causes inaccuracies in the scattering frequencies for unequally weighted simulations; a problem that occurs both for scattering within the group and between different groups.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Noise and error analysis and optimization in particle-based kinetic plasma simulations

In this paper we analyze the noise in macro-particle methods used in plasma physics and fluid dynamics, leading to approaches for minimizing the total error, focusing on electrostatic models in one dimension. We begin by describing kernel density estimation for continuous values of the spatial variable x, expressing the kernel in a form in which its shape and width are represented separately. The covariance matrix of the noise in the density is computed, first for uniform true density. The bandwidth of the covariance matrix C(x,y) is related to the width of the kernel. A feature that stands out is the presence of constant negative terms in the elements of the covariance matrix both on and off-diagonal. These negative correlations are related to the fact that the total number of particles is fixed at each time step; they also lead to the property ∫C(x,y)dy = 0. We investigate the effect of these negative correlations on the electric field computed by Gauss's law, finding that the noise in the electric field is related to a process called the Ornstein-Uhlenbeck bridge, leading to a covariance matrix of the electric field with variance significantly reduced relative to that of a Brownian process. For non-constant density, p(x), still with continuous x, we analyze the total error in the density estimation and discuss it in terms of bias-variance optimization (BVO). For some characteristic length l, determined by the density and its second derivative, and kernel width h, having too few particles within h leads to too much variance; for h that is large relative to l, there is too much smoothing of the density. The optimum between these two limits is found by BVO. For kernels of the same width, it is shown that this optimum (minimum) is weakly sensitive to the kernel shape. Next, we repeat the analysis for x discretized on a grid. In this case the charge deposition rule is determined by a particle shape. An important property to be respected in the discrete system is the exact preservation of total charge on the grid; this property is necessary to ensure that the electric field is equal at both ends, consistent with periodic boundary conditions. We find that if the particle shapes satisfy a partition of unity property, the particle charge deposited on the grid is conserved exactly. Further, if the particle shape is expressed as the convolution of a kernel with another kernel that satisfies the partition of unity, then the particle shape obeys the partition of unity. This property holds for kernels of arbitrary width, including widths that are not integer multiples of the grid spacing. Furthermore, we show results relaxing the approximations used to do BVO optimization analytically, by doing numerical computations of the total error as a function of the kernel width, on a grid in x. The comparison between numerical and analytical results shows good agreement over a range of particle shapes. We discuss the practical implications of our results, including the criteria for design and implementation of computationally efficient particle shapes that take advantage of the developed theory.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Simulating Electron Impact Ionization using a General Particle Tracer (GPT) Custom Element

A new C++ custom element has been developed with the framework of General Particle Tracer (GPT) to simulate electron impact ionization of residual gas molecules. The custom element uses Monte-Carlo routines to determine both the ion production rate and the secondary electron kinetic energy based on user-defined gas densities and theoretical values for the ionization cross section and the secondary electron differential cross section. It then uses relativistic kinematics to track the secondary electron, the scattered electron, and the newly formed ion after ionization. The ion production rate and the secondary electron energy distribution determined by the custom element have been benchmarked against theoretical calculations and against simulations made using the simulation package IBSimu. While the custom element was originally built for particle accelerator simulations, it is readily extensible to other applications. The custom element will be described in detail and examples of applications at the Thomas Jefferson National Accelerator Facility will be presented for ion production in a DC high voltage photo-gun.

Yoskowitz, J. T.↗

Quantifying the structural uncertainty of the aerosol mixing state representation in a modal model

Abstract. Aerosol mixing state is an important emergent property that affects aerosol radiative forcing and aerosol–cloud interactions, but it has not been easy to constrain this property globally. This study aims to verify the global distribution of aerosol mixing state represented by modal models. To quantify the aerosol mixing state, we used the aerosol mixing state indices for submicron aerosol based on the mixing of optically absorbing and non-absorbing species (χo), the mixing of primary carbonaceous and non-primary carbonaceous species (χc), and the mixing of hygroscopic and non-hygroscopic species (χh). To achieve a spatiotemporal comparison, we calculated the mixing state indices using output from the Community Earth System Model with the four-mode version of the Modal Aerosol Module (MAM4) and compared the results with the mixing state indices from a benchmark machine-learned model trained on high-detail particle-resolved simulations from the particle-resolved stochastic aerosol model PartMC-MOSAIC. The two methods yielded very different spatial patterns of the mixing state indices. In some regions, the yearly averaged χ value computed by the MAM4 model differed by up to 70 percentage points from the benchmark values. These errors tended to be zonally structured, with the MAM4 model predicting a more internally mixed aerosol at low latitudes and a more externally mixed aerosol at high latitudes compared to the benchmark. Our study quantifies potential model bias in simulating mixing state in different regions and provides insights into potential improvements to model process representation for a more realistic simulation of aerosols towards better quantification of radiative forcing and aerosol–cloud interactions.

54 ENVIRONMENTAL SCIENCES↗

Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation

This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolved direct numerical simulations (PR-DNS) of flow over a random array of stationary particles for eight combinations of particle Reynolds number (Re) and volume fraction (φ) are used in the development of the models. The first approach follows a two-step process. In the first flow prediction step, the perturbation flow due to a particle is obtained as an axisymmetric superposable wake using linear regression. In the second force prediction step, the force on a particle is evaluated in terms of the perturbation flow induced by all its neighbors using the generalized Faxén form of the force expression. In the second approach, the force data on all the particles from the PR-DNS simulations are used to develop an artificial neural network (ANN) model for direct prediction of force on a particle. Due to the unavoidable limitation on the number of fully resolved particles in the PR-DNS simulations, direct force prediction with the ANN model tends to over-fit the data and performs poorly in the prediction of test data. In contrast, due to the millions of grid points used in the PR-DNS simulations, accurate flow prediction is possible, which then allows accurate prediction of particle force. This hybridization of multiphase physics and machine learning is particularly important, since it blends the strength of each, and the resulting pairwise interaction extended point-particle model cannot be developed by either physics or machine learning alone.

42 ENGINEERING↗

Particle-In-Cell Computer Simulations of Beam Spill in the Scorpius Injector

Computer simulations of beam transport in the Scorpius injector were performed using the particle-in-cell computer code LSP. The simulations were used to predict spot size growth due to electron stimulated emission of ions from beam spill on the anode stalk and the entrance transport region.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Assessment of a Detailed Biomass Pyrolysis Kinetic Scheme in Multiscale Simulations of a Single-Particle Pyrolyzer and a Pilot-Scale Entrained Flow Pyrolyzer

A detailed biomass pyrolysis kinetic scheme was assessed in the multiscale simulations of a single-particle pyrolyzer with slow pyrolysis and a pilot-scale entrained flow pyrolyzer with fast pyrolysis. The detailed kinetic scheme of biomass pyrolysis developed by the CRECK group consists of 32 reactions and 58 species. A multiscale simulation model was developed, where the CRECK kinetics was employed to simulate biomass pyrolysis reactions, a one-dimensional particle model was utilized to simulate the intraparticle transport phenomena, and the particle-in-cell (PIC) model was employed to simulate the hydrodynamics. The multiscale model was first applied to simulate a single-particle pyrolysis experiment. The simulation with nonisothermal particles matched the experimental data better than the simulation with isothermal particles. Then the multiscale model was applied to simulate the pilot-scale entrained flow pyrolyzer. In this case, the simulation with isothermal particles matched the experimental data better than the simulation with nonisothermal particles. The reason for this difference might be that the kinetics itself already partially included the intraparticle transport effect as it was fitted using both TGA data (slow pyrolysis of small size biomass) and fluidized bed data (fast pyrolysis of relatively large size biomass). This study provides some insights into biomass pyrolysis kinetics development and pyrolyzer multiscale simulation for a future study.

09 BIOMASS FUELS↗

Progressive Tree-Based Compression of Large-Scale Particle Data

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. In conclusion, the improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

97 MATHEMATICS AND COMPUTING↗

Search for Lightly Ionizing Particles in SuperCDMS and simulation of neutron backgrounds

The Super Cryogenic Dark Matter Search (SuperCDMS) is a direct-detection dark matter search experiment that primarily aims to search for Weakly Interacting Massive Particles (WIMPs) using state-of-the-art solid-state detection technology. During its operation at the Soudan underground laboratory, germanium detectors were operated with high bias voltage mode known as the CDMS low ionization threshold experiment (CDMSlite) to achieve below-keV thresholds. CDMSlite, for being able to measure small energy depositions in detectors, also provides sensitivity to Lightly Ionizing Particles (LIPs) with very small fractional charges. This thesis will discuss an analysis to search LIPs with the data acquired in CDMSlite mode. An important component for LIPs search is the expected energy-deposition distributions for LIPs falling on the CDMSlite detector. In this thesis, a simulation framework to calculate the energy-deposition distributions is developed. This thesis presents first direct-detection limits on the intensity of cosmogenic LIPs with electric charges smaller than e /(3 × 10 5 ) as well as the strongest limits for charges ≤ e /160, with a minimum intensity of 1.36 × 10 -7 cm -2 s -1 sr -1 at charge e /160.In any rare-event search experiment, understanding background is crucial. Neutrons capable of mimicking dark matter signals are a major background for any dark matter search experiment. A simulation study to estimate the neutron background for an India based dark matter search experiment at Jaduguda Underground Science Laboratory (JUSL) is performed. The experiment at JUSL will be the first phase of a proposed Dark matter search at India-based Neutrino Observatory (DINO). It will be a direct detection experiment with primary aims to search for WIMPs as dark matter candidates. In this thesis, we discuss the methodology of estimating neutron flux at JUSL and report the results. The total neutron flux reaching the laboratory above 1 MeV energy threshold is found to be 5.76(±0.69) × 10 -6 cm -2 s -1 . The impact of neutron background on the sensitivity of the experiment to detect dark matter at JUSL is also discussed. The thesis is organized as follows. Chapter 1 provides a brief introduction to the Lightly Ionizing Particle (LIPs). The analysis to search LIPs in SuperCDMS is briefly outlined in the chapter. This chapter also discusses the importance of neutron background estimates in a dark matter search experiment, more specifically, in the context of a proposed India-based dark matter search experiment at Jaduguda Underground Science Laboratory. In Chapter 2, the SuperCDMS experiment is introduced. In Chapter 3, the framework developed to perform simulations for Lightly Ionizing Particles is presented. In Chapter 4, the LIPs search analysis with the CDMSlite data and the results are discussed. In Chapter 5, the simulation of neutron background and the feasibility of dark-matter search at JUSL is discussed. Finally, conclusions from all the results discussed in this thesis are presented in Chapter 6.

Banik, Samir↗

Comparison of Eulerian Bin and Lagrangian Particle-Based Schemes in Simulations of Pi Chamber Dynamics and Microphysics

This paper presents a comparison of simulations applying either a traditional Eulerian bin microphysics or a novel particle-based Lagrangian approach to represent CCN activation and cloud droplet growth. The Eulerian microphysics solve the evolution equation for the spectral density function, whereas the Lagrangian approach follows computational particles referred to as superdroplets. Each superdroplet represents a multiplicity of natural droplets that makes the Lagrangian approach computationally feasible. The two schemes apply identical representation of CCN activation and use the same droplet growth equation; these make direct comparison between the two schemes practical. The comparison, the first of its kind, applies an idealized simulation setup motivated by laboratory experiments with the Pi Chamber and previous model simulations of the Pi Chamber dynamics and microphysics. The Pi Chamber laboratory apparatus considers interactions between turbulence, CCN activation, and cloud droplet growth in moist Rayleigh–Bénard convection. Simulated steady-state droplet spectra averaged over the entire chamber are similar, with the mean droplet concentration, mean radius, and spectral width close in Eulerian and Lagrangian simulations. Small differences that do exist are explained by the inherent differences between the two schemes and their numerical implementation. The local droplet spectra differ substantially, again in agreement with the inherent limitations of the theoretical foundation behind each approach. There is a general agreement between simulations and Pi Chamber observations, with simplifications of the CCN activation and droplet growth equation used in the simulations likely explaining specific differences.

54 ENVIRONMENTAL SCIENCES↗

Search for lightly ionizing particles in superCDMS and simulation of neutron backgrounds

The Super Cryogenic Dark Matter Search (SuperCDMS) is a direct-detection dark matter search experiment that primarily aims to search for Weakly Interacting Massive Particles (WIMPs) using state-of-the-art solid-state detection technology. During its operation at the Soudan underground laboratory, germanium detectors were operated with high bias voltage mode known as the CDMS low ionization threshold experiment (CDMSlite) to achieve below-keV thresholds. CDMSlite, for being able to measure small energy depositions in detectors, also provides sensitivity to Lightly Ionizing Particles (LIPs) with very small fractional charges. This thesis will discuss an analysis to search LIPs with the data acquired in CDMSlite mode. An important component for LIPs search is the expected energy-deposition distributions for LIPs falling on the CDMSlite detector. In this thesis, a simulation framework to calculate the energy-deposition distributions is developed. This thesis presents first direct-detection limits on the intensity of cosmogenic LIPs with electric charges smaller than e/(3 × 105) as well as the strongest limits for charges ≤ e/160, with a minimum intensity of 1.36 × 10-7 cm-2s-1sr-1 at charge e/160.In any rare-event search experiment, understanding background is crucial. Neutrons capable of mimicking dark matter signals are a major background for any dark matter search experiment. A simulationmore » study to estimate the neutron background for an India based dark matter search experiment at Jaduguda Underground Science Laboratory (JUSL) is performed. The experiment at JUSL will be the first phase of a proposed Dark matter search at India-based Neutrino Observatory (DINO). It will be a direct detection experiment with primary aims to search for WIMPs as dark matter candidates. In this thesis, we discuss the methodology of estimating neutron flux at JUSL and report the results. The total neutron flux reaching the laboratory above 1 MeV energy threshold is found to be 5.76(±0.69) × 10-6 cm-2s-1. The impact of neutron background on the sensitivity of the experiment to detect dark matter at JUSL is also discussed. The thesis is organized as follows. Chapter 1 provides a brief introduction to the Lightly Ionizing Particle (LIPs). The analysis to search LIPs in SuperCDMS is briefly outlined in the chapter. This chapter also discusses the importance of neutron background estimates in a dark matter search experiment, more specifically, in the context of a proposed India-based dark matter search experiment at Jaduguda Underground Science Laboratory. In Chapter 2, the SuperCDMS experiment is introduced. In Chapter 3, the framework developed to perform simulations for Lightly Ionizing Particles is presented. In Chapter 4, the LIPs search analysis with the CDMSlite data and the results are discussed. In Chapter 5, the simulation of neutron background and the feasibility of dark-matter search at JUSL is discussed. Finally, conclusions from all the results discussed in this thesis are presented in Chapter 6.« less

Banik, Samir↗

Isogeometric large-eddy simulations of turbulent particle-laden flows

In recent years, isogeometric analysis (IGA) has attracted significant attention from the computational mechanics community due to its ability to integrate design and analysis. Besides, IGA is also a higher-order discretization technique for solving partial differential equations, showing high approximation capability per degree of freedom. In this paper, we extend the application realm of IGA to particle-laden flows based on Eulerian–Eulerian description that couples Navier–Stokes equations with a density transport equation through a Boussinesq approximation. The coupled systems are solved by using quadratic non-uniform rational B-spline (NURBS) functions and a recently developed residual-based variational multiscale (VMS) formulation, which introduces coupling between the fine velocity scales and density equation residuals. We deploy the proposed approach to perform large-eddy simulations (LES) of dilute particle-laden flows over a flat surface at Reynolds number = 10,000. We compare the simulation results against direct numerical simulation (DNS) results from the literature. We find that combining VMS and IGA, the proposed approach enables accurate prediction of a wide range of flow/particle statistics with a relatively lower mesh resolution.

Mathematics↗

Self‐consistent transport simulation of boron dust particle injection in the peripheral plasma in Large Helical Device

Abstract The trajectories and the ablation positions of boron dust particles dropped from an impurity powder dropper in the peripheral plasma in the Large Helical Device (LHD) were calculated using a three‐dimensional edge plasma simulation code (EMC3‐EIRENE) and a dust transport simulation code (DUSTT). The simulation shows that the trajectory of the boron dust particles is deflected at the upper divertor leg due to the effect of the hydrogen plasma flow, and the ablation positions of the dust particles in an ergodic layer change toward the outboard side of the torus for higher plasma densities. The effect of the boron ion flow in the divertor leg on the deflection is investigated by coupling the two codes self‐consistently. The simulation predicts that the boron ions in the divertor leg, which are produced by sputtering on the divertor plates, which do not affect the change in the ablation positions. It also shows that the ablation positions move toward the inboard side and approach the Last Closed Flux Surface (LCFS) in case of increased boron dust drop rates, which is caused by the lowered plasma flow in the upper divertor leg due to the lowered electron temperature by radiation cooling by the dropped dust particles.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A projection method for particle resampling

Particle discretizations of partial differential equations are advantageous for high-dimensional kinetic models in phase-space due to their better scalability than continuum approaches with respect to dimension. Complex processes collectively referred to as particle noise hamper long time simulations with particle methods. One approach to address this problem is particle mesh adaptivity, or remapping, known as particle resampling and remeshing. Here, this work introduces a resampling method that projects particles to and from a (finite element) function space. The method is simple, using standard sparse linear algebra and finite element techniques, and it preserves all moments up to the order of a polynomial represented exactly by the continuum function space. It is distinguished from most other mesh-based methods in that new particle positions and number are decoupled from the mesh, allowing particle and continuum meshes to be adapted relatively independently. While this work is developed with structured particle and continuum phase-space grids on 1X + 1V Vlasov-Poisson models of Landau damping and two-stream instability, the method is well-suited to unstructured grids. Stable long time dynamics are demonstrated up to time T = 500. Reproducibility artifacts and data are publicly available.

Kinetic methods↗

Statistical Learning for Nonlinear Model Reduction from Local Simulations of Stochastic and Particle- and Agent-Based Systems

Stochastic physical systems across the sciences that have very high-dimensional state spaces, with a large number of fast degrees of freedom that force direct simulators to proceed by integration steps that are orders of magnitude smaller than events of interests (e.g., particle collisions). Examples range from molecular motion to dynamics of large populations of cells. A grand challenge in the simulation and understanding of such systems is the systematic construction of accurate, interpretable, reduced models, enabling faster simulations, revealing fundamental properties of the dynamics, and predicting phenomena of interest that the original simulator could not reached with sufficient accuracy or within a given computational budget. In this projected we developed novel statistical estimation/machine learning techniques for analyzing and building empirical reduced models for important families of high-dimensional stochastic systems, in particular: - we developed techniques for estimating interaction kernels in interacting particle- and agent-based systems, which are ubiquitous in Physics, Biology and many other sciences, given observed trajectories of the system; - we developed techniques for nonlinear model reduction for high-dimensional stochastic systems that have a small number of unknown, nonlinear slow variables, and a large number of fast modes, that are possibly of large magnitude, given observed short trajectories of the system in the form of bursts of trajectories from different initial conditions; - we developed novel techniques for estimating linear dynamical systems on graphs when both the dynamics and the underlying graph are unknown, and we have a sparse set of space-time observations; - we considered the problem of estimating an unknown nonlinear observation function of a standard process (e.g. Brownian motion), so that we can recognized if an observed dynamics is "just" a nonlinear version of a known dynamics; we also developed benchmarks for learning algorithms aimed at learning and classifying diffusion processes.

97 MATHEMATICS AND COMPUTING↗

Lagrangian particle model for 3D simulation of pellets and SPI fragments in tokamaks

A 3D numerical model for the ablation of pellets and shattered pellet injection (SPI) fragments in tokamaks in the plasma disruption mitigation and fueling parameter space has been developed based on the Lagrangian particle code [R. Samulyak, X. Wang, H.-S. Chen, Lagrangian Particle Method for Compressible Fluid Dynamics, J. Comput. Phys., 362 (2018), 1-19]. The pellet code implements the low magnetic Reynolds number MHD equations, kinetic models for the electronic heating, a pellet surface ablation model, an equation of state that supports multiple ionization states, radiation, and a model for grad-B drift of the ablated material across the magnetic field. The Lagrangian particle algorithm is highly adaptive, capable of simulating a large number of fragments in 3D while eliminating numerical difficulties of dealing with the tokamak background plasma. The code has achieved good agreement with theory for spherically symmetric ablation flows. Axisymmetric simulations of neon and deuterium pellets in magnetic fields ranging from 1 to 6 Tesla have been compared with previous simulations using the FronTier code, and very good agreement has also been obtained. Furthermore, the main physics contribution of the paper is a detailed study of the influence of 3D effects, in particular grad-B drift, on pellet ablation rates and properties of ablation clouds. Smaller reductions of ablation rates in magnetic fields compared to axially symmetric simulations have been demonstrated because the ablated material is not confined to narrowing channels in the presence of grad-B drift. Contribution of various factors in the grad-B drift model has also been quantified.

97 MATHEMATICS AND COMPUTING↗

Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments

Within the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project Prioritization Panel (P5), including the upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and Intensity Frontiers will require an unprecedented amount of computational capacity on many fronts including Monte Carlo (MC) detector simulation. In order to alleviate this impending computational bottleneck, the Celeritas MC particle transport code is designed to leverage the new generation of heterogeneous computer architectures, including the exascale computing power of U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to model targeted HEP detector problems at the full fidelity of Geant4. This paper presents the planned roadmap for Celeritas, including its proposed code architecture, physics capabilities, and strategies for integrating it with existing and future experimental HEP computing workflows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Characteristics of flow through randomly packed impermeable and permeable particles using pore resolved simulations

Pore resolved simulations are performed to study the mean flow characteristics in porous media formed by random distribution of impermeable and permeable particles. Here, permeability of the medium and reactive surface areas are analyzed for a range of solid fractions, packing patterns and flow rates. The results are compared with models presented in the literature and with simulations of flow through ordered packings. Permeability of a randomly packed porous medium is found to depend on the heterogeneous distribution of porosity which results from particle agglomeration and is typically ignored by simpler models. The reactive surface area is dependent on the flow field at pore scale in addition to geometry. Moreover, the wake formed downstream of the particles at moderately high flow rates also affects the reactive surface area. Similar analysis is also performed by assuming particles to be permeable to account for dual scale porosity relevant for various engineering applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗