Unraveling Diffusion in Fusion Plasma: A Case Study of In Situ Processing and Particle Sorting

This work starts an in situ processing capability to study a certain diffusion process in magnetic confinement fusion. This diffusion process involves plasma particles that are likely to escape confinement. Such particles carry a significant amount of energy from the burning plasma inside the tokamak to the diverter and damaging the diverter plate. This study requires in situ processing because of the fast changing nature of the particle diffusion process. However, the in situ processing approach is challenging because the amount of data to be retained for the diffusion calculations increases over time, unlike in other in situ processing cases where the amount of data to be processed is constant over time. Here we report our preliminary efforts to control the memory usage while ensuring the necessary analysis tasks are completed in a timely manner. Compared with an earlier naive attempt to directly computing the same diffusion displacements in the simulation code, this in situ version reduces the memory usage from particle information by nearly 60% and computation time by about 20%

Superconducting MgB2 thin films by pulsed laser deposition

Growth of MgB2 thin films by pulsed laser deposition is examined under ex situ and in situ processing conditions. For the ex situ process, Boron films grown by PLD were annealed at 900 C with excess Mg. For the in situ process, different approaches involving ablation from a stoichiometric target under different growth conditions, as well as multilayer deposition involving interposed Mg layers were examined and analyzed. Magnetic measurements on ex situ processed films show TC of ~39 K, while the current best in situ films show a susceptibility transition at ~ 22 K.Comment: 3 pages, PD

Asynchronous In Situ Processing with Gromacs: Taking Advantage of GPUs

International audienceNumerical simulations using supercomputers are producing an ever growing amount of data. Efficient production and analysis of these data are the key to future discoveries. The in situ paradigm is emerging as a promising solution to avoid the I/O bottleneck encountered in the file system for both the simulation and the analytics by treating the data as soon as they are produced in memory. Various strategies and implementations have been proposed in the last years to support in situ treatments with a low impact on the simulation performance. Yet, little efforts have been made when it comes to perform in situ analytics with hybrid simulations supporting accelerators like GPUs. In this article, we propose a study of the in situ strategies with Gromacs, a molecular dynamic simulation code supporting multi-GPUs, as our application target. We specifically focus on the computational resources usage of the machine by the simulation and the in situ analytics. We finally extend the usual in situ placement strategies to the case of in situ analytics running on a GPU and study their impact on both Gromacs performance and the resource usage of the machine. We show in particular that running in situ analytics on the GPU can be a more efficient solution than on the CPU especially when the CPU is the bottleneck of the simulation

Neural-network-assisted in situ processing monitoring by speckle pattern observation

We propose a method to monitor the progress of laser processing using laser speckle patterns. Laser grooving and percussion drilling were performed using femtosecond laser pulses. The speckle patterns from a processing point were monitored with a high-speed camera and analyzed with a deep neural network. The deep neural network enabled us to extract multiple information from the speckle pattern without a need for analytical formulation. The trained neural network was able to predict the ablation depth with an uncertainty of 2 \micron, as well as the material under processing, which will be useful for composite material processing

Improving Performance of M-to-N Processing and Data Redistribution in In Transit Analysis and Visualization

In an in transit setting, a parallel data producer, such as a numerical simulation, runs on one set of ranks M, while a data consumer, such as a parallel visualization application, runs on a different set of ranks N. One of the central challenges in this in transit setting is to determine the mapping of data from the set of M producer ranks to the set of N consumer ranks. This is a challenging problem for several reasons, such as the producer and consumer codes potentially having different scaling characteristics and different data models. The resulting mapping from M to N ranks can have a significant impact on aggregate application performance. In this work, we present an approach for performing this M-to-N mapping in a way that has broad applicability across a diversity of data producer and consumer applications. We evaluate its design and performance with a study that runs at high concurrency on a modern HPC platform. By leveraging design characteristics, which facilitate an “intelligent” mapping from M-to-N, we observe significant performance gains are possible in terms of several different metrics, including time-to-solution and amount of data moved

In Situ Processing of PtSn Electrocatalysts for CO Tolerance in PEM Fuel Cells

Improved anode CO tolerance is a promising approach for integrating low-temperature PEM fuel cells with hydrocarbon fuel processors in cost-effective systems for portable and stationary power applications. PtSn@Pt core-shell nanoparticle electrocatalysts - created by applying cyclic potentials in the presence of CO to PtSn intermetallic nanoparticles in rotating disk electrode (RDE) experiments - have demonstrated the potential for high CO tolerance at low temperatures. This study explores the use of potential cycling with full PEM fuel cell membrane electrode assemblies (MEAs), initially with PtSn anode electrocatalysts, to produce PtSn@Pt electrocatalysts in situ for increased anode CO tolerance. Potential cycling of PtSn anodes in MEAs with various gaseous feeds consistently showed less dramatic decreases in CO oxidation overpotentials than observed in RDE studies. Although some results suggested that modified PtSn electrocatalysts outperform state-of-the-art PtRu anode electrocatalysts, PtSn@Pt electrocatalysts formed via MEA potential cycling consistently did not provide adequately low anode overpotentials with CO up to 1000 ppm to outperform commercial PtRu anode catalysts. Energy-dispersive X-ray spectroscopy of MEA cross-sections showed that Sn leached from the anode into the cathode as the number of cycles increased. Consistent formation of PtSn@Pt core-shell structures for high CO tolerance in full MEAs remains a challenge for further investigation

Harvesting Near Earth Asteroid Resources Using Solar Sail Technology

Near Earth asteroids represent a wealth of material resources to support future space ventures. These resources include water from C-type asteroids for crew logistic support; liquid propellants electrolytically cracked from water to fuel crewed vehicles and commercial platforms; and metals from M-type asteroids to support in-situ manufacturing. In this paper the role of solar sail technology will be investigated to support the future harvesting of near Earth asteroid resources. This will include surveying candidate asteroids though in-situ sensing, efficiently processing asteroid material resources and returning such resources to near-Earth space. While solar sailing can be used directly as a low cost means of transportation to and from near Earth asteroids, solar sail technology itself offers a number of dual-use applications. For example, solar sails can in principle be used as solar concentrators to sublimate material. If a metal-rich M-type asteroid is processed through solar heating, then the flow of metal resources made available could be manufactured into further reflective area. The additional thermal power generated would then accelerate the manufacturing process. Such a strategy could enable rapid in-situ processing of asteroid resources with exponential scaling laws. It is proposed that solar sailing therefore represents a key technology for harvesting near Earth asteroids, using sunlight both as heat for asteroid processing and radiation pressure for resource transportation

Изменение электропроводности горючих сланцев под влиянием плазмы электротеплового пробоя

Partial discharges with following treeing were discovered in oil shale. These phenomena are cause of breakdown like in dielectrics, but with some features. Shale conductivity increases due to carbonization under discharge plasma affect. Carbonized volume electrical resistance is decreased on 7-8 degrees to 10-100 ohm cm. This effect can be used for pyrolytic conversion of oil shale in fuel gas and synthetic oil by electrophysical heating including in-situ processing