Simulation has become an essential component of designing and developing scientific experiments. The conventional procedural approach to coding simulations of complex experiments is often error-prone, hard to interpret, and inflexible, making it hard to incorporate changes such as algorithm updates, experimental protocol modifications, and looping over experimental parameters. We present mmodel, a framework designed to accelerate the writing of experimental simulation packages. mmodel uses a graph-theory approach to represent the experiment steps and can rewrite its own code to implement modifications, such as adding a loop to vary simulation parameters systematically. The framework aims to avoid duplication of effort, increase code readability and testability, and decrease development time.
Bale et al. [1] perform a numerical study of droplet/aerosol transport in the air to assess the probability of airborne transmission of COVID-19 from an infected person to a nearby healthy person. In their numerical study, the air flow field is solved by an implicit large eddy simulation model, and the airborne transport of the droplets/aerosols exhaled from an infected person is solved by a Lagrangian particle model which considers droplet/aerosol evaporation. In the model used for droplets/aerosols, I found that an unrealistic assumption is used which may have a significant impact on the numerical accuracy: Droplet nuclei contained in real respiratory droplets/aerosols are neglected.
We present a method for improving the performance of nested sampling as well as its accuracy. Building on previous work by Chen et al., we show that posterior repartitioning may be used to reduce the amount of time nested sampling spends in compressing from prior to posterior if a suitable ``proposal'' distribution is supplied. We showcase this on a cosmological example with a Gaussian posterior, and release the code as an LGPL licensed, extensible Python package https://gitlab.com/a-p-petrosyan/sspr.
A popular way to accelerate the sampling of rare events in molecular dynamics simulations is to introduce a potential that increases the fluctuations of selected collective variables. For this strategy to be successful, it is critical to choose appropriate variables. Here we review some recent developments in the data-driven design of collective variables, with a focus on the combination of Fisher's discriminant analysis and neural networks. This approach allows to compress the fluctuations of metastable states into a low-dimensional representation. We illustrate through several examples the effectiveness of this method in accelerating the sampling, while also identifying the physical descriptors that undergo the most significant changes in the process.
Electro-thermal transport phenomena in semiconductors are described by the non-isothermal drift-diffusion system. The equations take a remarkably simple form when assuming the Kelvin formula for the thermopower. We present a novel, non-isothermal generalization of the Scharfetter-Gummel finite volume discretization for degenerate semiconductors obeying Fermi-Dirac statistics, which preserves numerous structural properties of the continuous model on the discrete level. The approach is demonstrated by 2D simulations of a heterojunction bipolar transistor.
We provide an introduction to molecular dynamics simulations in the context of the Kob-Andersen model of a glass. We introduce a complete set of tools for doing and analyzing the results of simulations at fixed NVE and NVT. The modular format of the paper allows readers to select sections that meet their needs. We start with an introduction to molecular dynamics independent of the programming language, followed by introductions to an implementation using Python and then the freely available open source software package LAMMPS. We also describe analysis tools for the quick testing of the program during its development and compute the radial distribution function and the mean square displacement using both Python and LAMMPS.
A variety of "pseudo-Voigt" functions, i.e. a linear combination of the Lorentz and Gauss function (occasionally augmented with a correction term), have been proposed as a closed-form approximation for the convolution of the Lorentz and Gauss function known as the Voigt function. First, a compact review of several approximations using a consistent notation is presented. The comparison with accurate reference values indicates relative errors as large as some percent.
The (py)LIon package is a set of tools to simulate the classical trajectories of ensembles of ions in electrodynamic traps. Molecular dynamics simulations are performed using LAMMPS, an efficient and feature-rich program. (py)LIon has been validated by comparison with the analytic theory describing ion trap dynamics. Notable features include GPU-accelerated force calculations, and treating collections of ions as rigid bodies to enable investigations of the rotational dynamics of large, mesoscopic charged particles.
Marija Šindik, Ayumu Sugita, Milovan Šuvakov
et al.
We numerically discovered around 100 distinct nonrelativistic collisionless periodic three-body orbits in the Coulomb potential in vacuo, with vanishing angular momentum, for equal-mass ions with equal absolute values of charges. These orbits are classified according to their symmetry and topology, and a linear relation is established between the periods, at equal energy, and the topologies of orbits. Coulombic three-body orbits can be formed in ion traps, such as the Paul, or the Penning one, where one can test the period vs topology prediction.
We present an approach to accelerate real-space electronic structure methods several fold, without loss of accuracy, by reducing the dimension of the discrete eigenproblem that must be solved. To accomplish this, we construct an efficient, systematically improvable, discontinuous basis spanning the occupied subspace and project the real-space Hamiltonian onto the span. In calculations on a range of systems, we find that accurate energies and forces are obtained with 8--25 basis functions per atom, reducing the dimension of the associated real-space eigenproblems by 1--3 orders of magnitude.
D. V. Fedorchenko, M. A. Khazhmuradov, Y. V. Rudychev
The process of $^{67}$Cu nuclide photoproduction in the zinc dioxide nanoparticles immersed in the water media was simulated. We calculated the escape fractions of $^{67}$Cu nuclei and corresponding ranges in water for nanoparticle sizes from 40 nm to 80 nm and incident photons energies from 12 MeV to 30 MeV. Usage of capturing nanoparticles for accumulation of the escaped $^{67}$Cu nuclei is also discussed.
The paper [1] by Crouseilles, Einkemmer, and Faou used an incorrect Poisson bracket for the Vlasov-Maxwell equations. If the correct Poisson bracket is used, the solution of one of the subsystems cannot be computed exactly in general. As a result, one cannot construct a symplectic scheme for the Vlasov-Maxwell equations using the splitting Hamiltonian method proposed in Ref [1].
An efficient algorithm for time propagation of the time-dependent Kohn-Sham equations is presented. The algorithm is based on dividing the Hamiltonian into small time steps and assuming that it is constant over these steps. This allows for the time-propagating Kohn-Sham wave function to be expanded in the instantaneous eigenstates of the Hamiltonian. The stability and efficiency of the algorithm are tested not just for non-magnetic but also for fully non-collinear magnetic systems. We show that even for delicate properties, like magnetization density, large time-step sizes can be used indicating the stability and efficiency of the algorithm.
Madhusudhan Kundrapu, John Loverich, Kris Beckwith
et al.
The ability to simulate a reentry vehicle plasma layer and the radio wave interaction with that layer, is crucial to the design of aerospace vehicles when the analysis of radio communication blackout is required. Results of aerothermal heating, plasma generation and electromagnetic wave propagation over a reentry vehicle are presented in this paper. Simulation of a magnetic window radio communication blackout mitigation method is successfully demonstrated.
A new molecular simulation toolkit composed of some lately developed force fields and specified models is presented to study the self-assembly, phase transition, and other properties of polymeric systems at mesoscopic scale by utilizing the computational power of GPUs. In addition, the hierarchical self-assembly of soft anisotropic particles and the problems related to polymerization can be studied by corresponding models included in this toolkit.
In this paper we consider a conservative discretization of the two-dimensional incompressible Navier--Stokes equations. We propose an extension of Arakawa's classical finite difference scheme for fluid flow in the vorticity-stream function formulation to a high order discontinuous Galerkin approximation. In addition, we show numerical simulations that demonstrate the accuracy of the scheme and verify the conservation properties, which are essential for long time integration. Furthermore, we discuss the massively parallel implementation on graphic processing units.