Formation of the streamer zone of a leader is an outstanding problem in the physics of electric discharges which is relevant to laboratory leaders, as well as to the leaders formed by lightning. Despite substantial progress in the theoretical understanding of this complicated phenomenon, significant puzzles, such as the low propagation velocity of a leader compared to the fast streamers, remain. The objective of this paper is to present 2-D plasma simulations of the formation and propagation of the streamer zone of a leader. In these simulations we will generate a group of streamers that propagate in a discharge gap while interacting with each other. It is shown that interaction between the streamers significantly reduces their propagation velocity. This explains why the leader, which consists of many streamers, is much slower than a single streamer formed in the same discharge gap. It is shown that the mean velocity suppression of the group of streamers is determined by the inter-streamer distance. The critical value of the packing factor of the streamers at which the interactions between them can be neglected, and thus the discussed process can be treated as caused by a single streamer, is obtained.
The dataflow-model of computation is widely used in design and implementation of signal processing systems. In dataflow-based design processes, scheduling—the assignment and coordination of computational modules across processing resources—is a critical task that affects practical measures of performance, including latency, throughput, energy consumption, and memory requirements. Dataflow schedule graphs (DSGs) provide a formal abstraction for representing schedules in dataflow-based design processes. The DSG abstraction allows designers to model a schedule as a separate dataflow graph, thereby providing a formal, abstract (platform- and language-independent) representation for the schedule. In this paper, we introduce a design methodology that is based on explicit specifications of application graphs and schedules as cooperating dataflow models. We also develop new techniques and tools for automatically synthesizing efficient implementations on multicore platforms from these coupled application and schedule models. We demonstrate the proposed methodology and synthesis techniques through a case study involving real-time detection of people and vehicles using acoustic and seismic sensors.
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