Inference of flow shear from reciprocating plasma potential measurements by means of Gaussian process regression

Reliable estimation of equilibrium flow shear from reciprocating probe measurements is challenging since the quantity of interest corresponds to the second derivative of the observable plasma potential. In addition, a time series of the plasma potential obtained by plunging a probe is affected by both the probe head position and plasma fluctuations, complicating the estimation of equilibrium components and their errors. We tackle this problem by employing Gaussian process regression that is able to infer even the derivatives of a spatial or temporal profile in the form of a probability distribution function. The proposed inference framework is validated by using synthetic data generated by gyrofluid simulations. While the inference result based on a single plunge is unstable in certain spatial locations, we have obtained reasonable agreement between the inference result and the true flow shear profile by combining data sets taken from several plunges.

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Publication Details

Journal
Physics of Plasmas
Published
2025-03-01
DOI
https://doi.org/10.1063/5.0254473
Citations
1
Primary Topic
Magnetic confinement fusion research
Type
article
Field-Weighted Citation Impact
1.87

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Inference of flow shear from reciprocating plasma potential measurements by means of Gaussian process regression

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Magnetic confinement fusion research
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article

Inference of flow shear from reciprocating plasma potential measurements by means of Gaussian process regression

P. Mänz, S. Inagaki, A. Fujisawa, G. Grenfell, S. Tokuda, T. Nishizawa, Yuichi Kawachi, M. Sasaki
article en
1 citations

Abstract

Reliable estimation of equilibrium flow shear from reciprocating probe measurements is challenging since the quantity of interest corresponds to the second derivative of the observable plasma potential. In addition, a time series of the plasma potential obtained by plunging a probe is affected by both the probe head position and plasma fluctuations, complicating the estimation of equilibrium components and their errors. We tackle this problem by employing Gaussian process regression that is able to infer even the derivatives of a spatial or temporal profile in the form of a probability distribution function. The proposed inference framework is validated by using synthetic data generated by gyrofluid simulations. While the inference result based on a single plunge is unstable in certain spatial locations, we have obtained reasonable agreement between the inference result and the true flow shear profile by combining data sets taken from several plunges.

Physics of PlasmasVol. 32(3)
Nihon University (JP), Kyushu University (JP), Kyoto University (JP), Universität Greifswald (DE), Advanced Energy (United States) (US), Institute of Mathematics and Informatics (BG), Czech Academy of Sciences, Institute of Mathematics (CZ), Max Planck Institute for Plasma Physics (DE), College of Industrial Technology (JP), Nagoya University (JP)
Japan Society for the Promotion of Science, Research Institute for Applied Mechanics, Kyushu University
Openalex Percentile: Top 15%
Magnetic confinement fusion research
1.87
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