Hao Wu, Axel Jardin, Didier Mazon, Geert Verdoolaege, The WEST Team
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引用次数: 0
Abstract
The accumulation of heavy impurities like tungsten in the plasma core of fusion devices can cause significant radiative power losses or even lead to a disruption. It is therefore crucial to monitor the tungsten impurity concentration. In this paper, we follow the integrated data analysis approach using Bayesian probability theory to jointly estimate tungsten concentration profiles and kinetic profiles from soft X-ray, interferometry and electron cyclotron emission measurements. As the full Bayesian inference using Markov chain Monte Carlo sampling is time-consuming, we also discuss emulation of the inference process using neural networks, with a view to real-time implementation.
核聚变装置等离子体核心中钨等重杂质的积累会造成严重的辐射功率损失,甚至导致中断。因此,监测钨杂质浓度至关重要。在本文中,我们采用贝叶斯概率论的综合数据分析方法,从软 X 射线、干涉测量和电子回旋发射测量中联合估算钨浓度曲线和动力学曲线。由于使用马尔科夫链蒙特卡洛采样进行完整的贝叶斯推理非常耗时,因此我们还讨论了使用神经网络模拟推理过程,以便实时实施。
期刊介绍:
The Journal of Fusion Energy features original research contributions and review papers examining and the development and enhancing the knowledge base of thermonuclear fusion as a potential power source. It is designed to serve as a journal of record for the publication of original research results in fundamental and applied physics, applied science and technological development. The journal publishes qualified papers based on peer reviews.
This journal also provides a forum for discussing broader policies and strategies that have played, and will continue to play, a crucial role in fusion programs. In keeping with this theme, readers will find articles covering an array of important matters concerning strategy and program direction.