基于元胞非线性网络的托卡马克热负荷分析与实时热点识别

F. Battaglia, A. Buscarino, C. Corradino, L. Fortuna, M. Frasca, M. Apicella, G. Mazzitelli
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引用次数: 6

摘要

近年来,在核聚变实验中等离子体-壁相互作用领域的一项创新技术是液态锂限流器(LLL),它是一种基于液态锂的冷却系统的限流器。由于其性能取决于空间温度分布,因此热负荷分析对于长期开发非常重要。此外,温度往往不均匀分布,导致热点的形成,这应该实时检测,以避免任何等离子体中断。本文在定义合适的元胞非线性网络算法的基础上,介绍了一种用于等离子体实验热图像实时图像处理的方法。它既可以绘制下限温度图,也可以检测限制器表面上的热点。离线测试表明了该方法的有效性,为限制器表面温度的建模铺平了道路,提供了可靠的信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Thermal load analysis and real time hot spots recognition in TOKAMAK using cellular nonlinear networks
A recent innovative technology in the field of plasma-wall interaction in nuclear fusion experiments is represented by the Liquid Lithium Limiter (LLL), a Limiter with a cooling system based on Liquid Lithium. Since its performance depends on the spatial temperature distribution, a thermal load analysis is important for long term developments. Furthermore, temperature is often not uniformly distributed leading to hot spots formation, that should be detected in real time to avoid any plasma disruptions. In this paper, an approach based on the definition of a suitable Cellular Nonlinear Network algorithm for the real-time image processing of thermal images taken during a plasma experiment is introduced. It allows both to map the LLL temperature and to detect hot spots over the limiter surface. Offline testing of the proposed procedure reveals the effectiveness of the approach paving the way to the modeling of the limiter surface temperature providing reliable information.
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