Using Java for plasma PIC simulations

Plasma particle-in-cell (PIC) simulations model the interactions of charged particles with the surrounding fields. This application has been recognized as one of the grand challenge problems facing the high-performance computing community due to its huge computational requirements. Recently, with the explosive development of Internet, Java is receiving increasing attention and is thought as a potential candidate for high-performance computing. In this paper, we present our approach to developing 2- and 3-dimensional parallel PIC simulations in Java. We also report the execution times for both versions from performance experiments on a symmetric multi-processor (Sun E6500) and a Linux cluster of Pentium III machines. Those results are also compared with benchmark measurements of the corresponding Fortran version of the same algorithm

Mixed language high-performance computing for plasma simulations

Java is receiving increasing attention as the most popular platform for distributed computing. However, programmers are still reluctant to embrace Java as a tool for writing scientific and engineering applications due to its still noticeable performance drawbacks compared with other programming languages such as Fortran or C. In this paper, we present a hybrid Java/Fortran implementation of a parallel particle-in-cell (PIC) algorithm for plasma simulations. In our approach, the time-consuming components of this application are designed and implemented as Fortran subroutines, while less calculation-intensive components usually involved in building the user interface are written in Java. The two types of software modules have been glued together using the Java native interface (JNI). Our mixed-language PIC code was tested and its performance compared with pure Java and Fortran versions of the same algorithm on a Sun E6500 SMP system and a Linux cluster of Pentium III machines

Overcoming Challenges in Predictive Modeling of Laser-Plasma Interaction Scenarios. The Sinuous Route from Advanced Machine Learning to Deep Learning

The interaction of ultrashort and intense laser pulses with solid targets and dense plasmas is a rapidly developing area of physics, this being mostly due to the significant advancements in laser technology. There is, thus, a growing interest in diagnosing as accurately as possible the numerous phenomena related to the absorption and reflection of laser radiation. At the same time, envisaged experiments are in high demand of increased accuracy simulation software. As laser-plasma interaction modelings are experiencing a transition from computationally-intensive to data-intensive problems, traditional codes employed so far are starting to show their limitations. It is in this context that predictive modelings of laser-plasma interaction experiments are bound to reshape the definition of simulation software. This chapter focuses an entire class of predictive systems incorporating big data, advanced machine learning algorithms and deep learning, with improved accuracy and speed. Making use of terabytes of already available information (literature as well as simulation and experimental data) these systems enable the discovery and understanding of various physical phenomena occurring during interaction, hence allowing researchers to set up controlled experiments at optimal parameters. A comparative discussion in terms of challenges, advantages, bottlenecks, performances and suitability of laser-plasma interaction predictive systems is ultimately provided

Realistic simulation of the ion cyclotron resonance mass spectrometer using a distributed three-dimensional particle-in-cell code

AbstractThis work describes an Internet accessible three-dimensional particle-in-cell simulation code, which is capable of near first principles modeling of complete experimental sequences in Fourier transform ion cyclotron resonance mass spectrometers. The graphical user interface is a Java client that communicates via a socket stream connection over the Internet to the computational engine, a server that executes the simulation and sends real-time particle data back to the client for display. As a first demonstration, this code is applied to the problem of the cyclotron motion of two very close mass to charge ratios at high ion density. The ion populations in these simulations range from 50,000 to 350,000 coulombically interacting particles confined in a cubic trap, which are followed for 100,000 time-steps. Image charge, coherent cyclotron positions, and snapshots of the ion population are recorded at selected time-steps. At each time-step in the simulation the potential (coulomb + image + trap) is found by the direct solution of Poisson’s equation on a 64 × 64 × 64 computational grid. Cyclotron phase locking is demonstrated at high number density. Simulations at different magnetic fields confirm a B2 dependence for the minimum number density required to lock cyclotron modes

Numerical Investigation of Ion Transport in the MOMA Ion Mass Spectrometer

The Mars Organic Molecule Analyzer (MOMA) is a miniature ion trap mass spectrometer designed for the upcoming ExoMars Rover mission. The spectrometer uses laser desorption to ionize a Martian soil sample within an instrument internal clean zone maintained at ambient Martian pressure. A high-speed aperture valve transiently opens to allow ionized constituents, along with the ambient gas, to enter a vacuum cavity containing a linear ion trap mass spectrometer. The ambient clean zone and the vacuum cavity are connected via a few centimeter long aperture valve ion guide tube. In this paper, we present results from a recently completed numerical investigation of ion transport from the ion source across the ion guide. Specifically, we focus on collisional coupling between ions and the neutral molecules flowing into the vacuum cavity. The simulation domain contains the ambient region, and we consider the variation in ion conductance with ambient pressure. We also analyze the impact of a fixed potential bias applied to the aperture valve. Simulations are performed with a two-dimensional axisymmetric PIC / DSMC code Starfish. Numerical results are compared to experimental data

Cubesat Wakes in the Earth’s Ionosphere

Space exploration is currently being revolutionized by the advent of the CubeSat: 10cm cubed satellites that typically deploy in constellations for less than $100,000. Spacecraft instrumentation design must be redefined to abide by the compact CubeSat payload. Furthermore, the CubeSat dimension must be considered with respect to characteristic length scales of the space plasma environment, namely, the Debye length. Although spacecraft-plasma interactions- surface charging, plasma sheaths and wakes- have been well-studied for larger satellites, they are less understood for CubeSats. The Dynamic Ionosphere CubeSat Experiment (DICE) is a 1.5U CubeSat which recently ended its mission. DICE carried two fixed-bias Langmuir probes operating in the ion saturation regime that extended 180_ apart from each other on scissor booms. Since the CubeSat was tumbling without proper attitude control, the plasma density measurements rendered by the probes were spin-modulated– as the probes moved in and out of the spacecraft wake, the relative density detected was modulated. Researchers who analyze such spin-modulated data routinely discard the lower density measurements from the spin-cycle attributing it to a density depletion in the spacecraft wake. It is traditionally assumed that the higher density measurement within a spin-cycle is being made outside the wake and thus is a better proxy of the ambient plasma density. While such assumptions might be true for larger spacecraft, this thesis investigates if it holds true for CubeSats in Earth’s ionosphere. The Spacecraft Plasma Interaction System (SPIS) is a widely recognized and powerful particle-in-cell (PIC) simulation tool. In this thesis, SPIS is employed to investigate the manner that a CubeSat interacts with the plasma environment when electron thermal velocities vastly exceed the spacecraft velocity which, in turn, vastly exceeds ion thermal velocities. These are so-called meso-thermal conditions which are typical of plasmas in Earth’s ionosphere. Analysis of surface charging shows that the CubeSat abides by the thick-sheath model of Langmuir probes– the CubeSat dimension of 10 cm is comparable to the sheath thickness. More importantly, it is shown that, contrary to popular belief, there is a density enhancement in the CubeSat wake. SPIS results show that a negatively charged CubeSat in meso-thermal conditions creates an ion focus region in the far-wake. Finally, an independent code, written in MATLAB, is developed which verifies that this feature is a direct result of the CubeSat behaving like a Langmuir probe in the thick-sheath model. The work performed towards this thesis cautions the community towards assuming Cube- Sats to have density depletions in their wakes, and stresses the necessity of having an accurate attitude solution to derive ambient plasma densities from spin-modulated Langmuir probe measurements. Ultimately, this work may inspire new perspectives in Langmuir probe development and data analysis for CubeSats