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Extensive statistical analysis of ELMs on JET with a carbon wall

机译:带有碳壁的JET上ELM的广泛统计分析

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Edge localized modes (ELMs) are bursts of instabilities which deteriorate the confinement of H mode plasmas and can cause damage to the divertor of next generation of devices. On JET individual discharges can exhibit hundreds of ELMs but typically in the literature, mainly due to the lack of automatic analysis tools, single papers investigate only the behaviour of tens of individual ELMs. In this paper, an original tool, the universal event locator (UMEL), is applied to the problem of automatically identifying the time location of ELMs. With this approach, databases of hundreds of thousands of ELMs can be built with reasonable effort. The analysis has then been focused on the investigation of the statistical distribution of the inter-ELM intervals at steady state for type I ELMs. Numerous probability distributions have been tested to perform the data analysis and different distributions provide a best fit for sets of data from different experiments. This result constitutes robust experimental confirmation that type I ELMs are not all necessarily the same type of instability. Moreover, the most likely distributions are not memoryless, meaning that the waiting time, from a particular instant until the next ELM, does depend on the time elapsed from the previous event. These properties, confirmed by this investigation on JET, pose important constraints on the models aimed at describing the ELM dynamics. This work also demonstrates the widespread applicability of the UMEL tool.
机译:边缘局部模式(ELM)是不稳定性的突发事件,会使H模式等离子体的局限性恶化,并可能损坏下一代器件的偏滤器。在JET上,单个放电可显示数百个ELM,但通常在文献中,主要是由于缺乏自动分析工具,单篇论文仅研究了数十个单个ELM的行为。在本文中,原始工具通用事件定位器(UMEL)用于自动识别ELM的时间位置的问题。通过这种方法,可以通过合理的努力来构建成千上万个ELM的数据库。然后,分析集中于研究I型ELM稳态时ELM间间隔的统计分布。已对许多概率分布进行了测试以执行数据分析,并且不同的分布为来自不同实验的数据集提供了最佳拟合。该结果构成了可靠的实验确认,即I型ELM不一定都属于同一类型的不稳定性。而且,最可能的分布并不是没有记忆的,这意味着从特定时刻到下一个ELM的等待时间确实取决于从上一个事件经过的时间。通过对JET的调查证实了这些特性,这些特性对旨在描述ELM动力学的模型构成了重要的约束。这项工作还证明了UMEL工具的广泛适用性。

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