17 research outputs found

    Towards Forecasting Capability in Solar Energetic Particle Modeling

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    Solar Energetic Particles (SEPs) are an important aspect of space weather. SEP events posses a high destructive potential, since they may cause disruptions of communication systems on Earth and be fatal to crew members onboard spacecrafts and, in extreme cases, harmful to people onboard high altitude flights. However, currently the research community lacks efficient tools to predict such hazardous threat and its potential impacts. Such a tool is a first step for humanity to improve its preparedness for SEP events and ultimately to be able to mitigate their effects. The main goal of this project is to develop a computational tool that will have the forecasting capability and can be the basis for operational system that will provide live information on the current potential threats posed by SEP based on the observations of the Sun. The complexity of the problem is reflected in the level of sophistication of the product of development. The tool comprises several numerical models, which are designed to simulate different physical aspects of SEPs. The background conditions in the interplanetary medium, the magnetic field in particular, play a defining role in the transport and acceleration of SEPs, and are simulated with the state-of-the-art MHD solver, BATS-R-US. The newly developed particle code, M-FLAMPA, simulates the actual transport and acceleration of SEPs and is coupled to the MHD code. The special property of SEPs, the tendency to follow magnetic lines of force, is fully taken advantage of in the computational model, which substitutes a complicated 3-D model with a multitude of 1-D models. This approach significantly simplifies computations and improves the time performance of the overall model. Another aspect of SEP physics, the production of energetic particles during solar flares is also explored.PHDAtmospheric, Oceanic & Space ScienceUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/138463/1/dborovik_1.pd

    Eruptive Event Generator Based on the Gibson-Low Magnetic Configuration

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    Coronal Mass Ejections (CMEs), a kind of energetic solar eruptions, are an integral subject of space weather research. Numerical magnetohydrodynamic (MHD) modeling, which requires powerful computational resources, is one of the primary means of studying the phenomenon. With increasing accessibility of such resources, grows the demand for user-friendly tools that would facilitate the process of simulating CMEs for scientific and operational purposes. The Eruptive Event Generator based on Gibson-Low flux rope (EEGGL), a new publicly available computational model presented in this paper, is an effort to meet this demand. EEGGL allows one to compute the parameters of a model flux rope driving a CME via an intuitive graphical user interface (GUI). We provide a brief overview of the physical principles behind EEGGL and its functionality. Ways towards future improvements of the tool are outlined

    Extended magnetohydrodynamics with embedded particle‐in‐cell simulation of Ganymede’s magnetosphere

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    We have recently developed a new modeling capability to embed the implicit particle‐in‐cell (PIC) model iPIC3D into the Block‐Adaptive‐Tree‐Solarwind‐Roe‐Upwind‐Scheme magnetohydrodynamic (MHD) model. The MHD with embedded PIC domains (MHD‐EPIC) algorithm is a two‐way coupled kinetic‐fluid model. As one of the very first applications of the MHD‐EPIC algorithm, we simulate the interaction between Jupiter’s magnetospheric plasma and Ganymede’s magnetosphere. We compare the MHD‐EPIC simulations with pure Hall MHD simulations and compare both model results with Galileo observations to assess the importance of kinetic effects in controlling the configuration and dynamics of Ganymede’s magnetosphere. We find that the Hall MHD and MHD‐EPIC solutions are qualitatively similar, but there are significant quantitative differences. In particular, the density and pressure inside the magnetosphere show different distributions. For our baseline grid resolution the PIC solution is more dynamic than the Hall MHD simulation and it compares significantly better with the Galileo magnetic measurements than the Hall MHD solution. The power spectra of the observed and simulated magnetic field fluctuations agree extremely well for the MHD‐EPIC model. The MHD‐EPIC simulation also produced a few flux transfer events (FTEs) that have magnetic signatures very similar to an observed event. The simulation shows that the FTEs often exhibit complex 3‐D structures with their orientations changing substantially between the equatorial plane and the Galileo trajectory, which explains the magnetic signatures observed during the magnetopause crossings. The computational cost of the MHD‐EPIC simulation was only about 4 times more than that of the Hall MHD simulation.Key PointsFirst particle‐in‐cell simulation of Ganymede’s magnetosphereThe MHD‐EPIC algorithm makes global kinetic simulations affordableMHD‐EPIC simulation suggests that Galileo observed a flux transfer event during the G8 flybyPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135161/1/jgra52397.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/135161/2/jgra52397_am.pd

    Selection of A Suitable Algorithm for the Implementation of Rate-Limiter Based on Bucket4j

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     In shared network services, rate limiting is essential as it controls the requests of the users or requesters in a specific amount of time. Due to rate limiting, the service or API stays protected from overuse, malicious attack, DDoS attack, data traffic spikes, etc. Bucket4j is a java library that has been demonstrated to be effective in rate limiting. While Bucket4j is mainly based on token bucket algorithm, rate limiting processes can be based on various effective algorithms. Selecting the most suitable algorithm for rate limiting is an essential problem. To address it, we have done a detailed analysis of rate-limiting algorithms based on various factors. The factors we have considered are easy implementation, proper handling of data traffic, data starvation, memory usage, etc. We have found out that for different set of requirements, different algorithms are  preferable

    Application of the Monte Carlo Method in Modeling Dusty Gas, Dust in Plasma, and Energetic Ions in Planetary, Magnetospheric, and Heliospheric Environments

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    Typical planetary and planetary satellite exospheres are in nonequilibrium conditions, which means that a distribution function that describes these environments is far from Maxwellian. It is even more true when considering transportation of energetic ions in planetary magnetospheres, making it necessary to solve the Boltzmann equation in order to capture kinetic effects when modeling evolution of the distribution function describing such environments. Among various numerical methods, the Monte Carlo approach is one of the most used one for solving kinetic equations. That is because of the relative simplicity of implementing and a high degree of flexibility in including new physical processes specific to a particular simulated environment. Adaptive Mesh Particle Simulator (AMPS) was developed as a general‐purpose code for solving the Boltzmann equation in conditions typical for planetary and planetary satellite exospheres. Later, the code was generalized for modeling dusty gas, dust, and plasma, and for simulating transportation of solar energetic particles and galactic cosmic rays in planetary magnetospheres. Here, we present a brief overview of the design, list the implemented physics models, and outline the modeling capabilities of AMPS. The latter is supported by several examples of prior applications of the code.Key PointsKinetic modeling is necessary for understanding various phenomena of planetary and space physicsAMPS is a versatile and well‐tested code with a long track record of application to simulate various planetary and heliophysics phenomenaThe paper demonstrates the modeling capabilities of AMPS by presenting several examples of the code’s prior applicationPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166403/1/jgra56190.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/166403/2/jgra56190_am.pd
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