A neural network for the analysis of Langmuir-probe characteristics

IF 2.1 2区物理与天体物理 Q2 PHYSICS, FLUIDS & PLASMAS

Plasma Physics and Controlled Fusion Pub Date : 2024-09-06 DOI:10.1088/1361-6587/ad7289

Jasmin Joshi-Thompson, Mirko Ramisch

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引用次数: 0

Abstract

Langmuir probes have been widely used in the field of plasma diagnostics for the characterisation of plasma properties. These probes play a crucial role in understanding the behaviour of a diverse range of plasmas, e.g. edge plasmas in fusion experiments. The measurement of electron density (n_e) and electron temperature (T_e) provides valuable insights into the plasma’s state, stability, and confinement properties. Conventionally, this analysis involves post-experiment fitting methods to calculate plasma properties from the measured current–voltage curves obtained from Langmuir probes. This work introduces a neural-network approach for analysing probe data from the TJ-K stellarator, allowing for fast associative plasma characterisation. The results show a reliable performance on test data within the domain of the training set, predicting both n_e and T_e within the 10 % intrinsic error. Performance on unseen data outside the domain of the training set was on average within a 26 % and 21 % error on n_e and T_e, respectively. The network’s further abilities, including the identification of low-quality and falsely-labelled data, were also explored. The use of neural networks (NNs) offers fast predictions, enabling further research into real-time applications and live feedback control. This paper highlights the promising role of NNs in enhancing the analysis of Langmuir-probe characteristics.

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用于分析朗缪尔探针特性的神经网络

朗缪尔探针已广泛应用于等离子体诊断领域，用于描述等离子体的特性。这些探针在了解各种等离子体（如核聚变实验中的边缘等离子体）的行为方面发挥着至关重要的作用。通过测量电子密度（ne）和电子温度（Te），可以深入了解等离子体的状态、稳定性和约束特性。传统的分析方法是在实验后采用拟合方法，根据朗缪尔探针测得的电流-电压曲线计算等离子体特性。这项工作引入了一种神经网络方法，用于分析来自 TJ-K 恒星仪的探针数据，从而实现快速关联等离子体特性分析。结果表明，在训练集域内的测试数据上，神经网络具有可靠的性能，对 ne 和 Te 的预测误差在 10% 的固有误差范围内。在训练集范围之外的未见数据上，氖和碲的平均误差分别为 26% 和 21%。此外，还探讨了该网络的其他能力，包括识别低质量数据和虚假标签数据的能力。神经网络（NN）的使用提供了快速预测，使实时应用和实时反馈控制方面的研究得以深入。本文强调了神经网络在增强朗缪尔探针特性分析中的重要作用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Plasma Physics and Controlled Fusion 物理-物理：核物理

CiteScore

4.50

自引率

13.60%

发文量

224

审稿时长

4.5 months

期刊介绍： Plasma Physics and Controlled Fusion covers all aspects of the physics of hot, highly ionised plasmas. This includes results of current experimental and theoretical research on all aspects of the physics of high-temperature plasmas and of controlled nuclear fusion, including the basic phenomena in highly-ionised gases in the laboratory, in the ionosphere and in space, in magnetic-confinement and inertial-confinement fusion as well as related diagnostic methods. Papers with a technological emphasis, for example in such topics as plasma control, fusion technology and diagnostics, are welcomed when the plasma physics is an integral part of the paper or when the technology is unique to plasma applications or new to the field of plasma physics. Papers on dusty plasma physics are welcome when there is a clear relevance to fusion.