Continuous observation of filaments from the confined region to the far scrape-off layer

Griener, M.; Wolfrum, E.; Birkenmeier, G.; Faitsch, M.; Fischer, R.; Fuchert, G.; Gil, L.; Harrer, G.; Mänz, P.; Wendler, D.; Stroth, U.

doi:10.1016/j.nme.2020.100854

Cited by 25 publications

(27 citation statements)

References 23 publications

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“…Fusion plasma diagnostic measurements are inherently noisy and limited in their spatiotemporal scope (e.g. 1-or 2-dimensional profiles of electron density and temperature [76,13,138,142]) and resultantly require suitable analysis techniques. To this end, Chapter 2 demonstrates a physics-informed deep learning framework to diagnose unknown turbulent field fluctuations consistent with drift-reduced Braginskii theory from limited electron pressure observations.…”

Section: Oliver Heavisidementioning

confidence: 99%

“…With the availability of measurements often sparse in fusion experiments, designing diagnostic techniques for validating turbulence theories with limited information is important. On this point, it is noteworthy that this framework can potentially be adapted to experimental measurements of electron density and temperature [76,13,138,142]. To handle the particular case of 2-dimensional turbulence data, one essentially assumes slow variation of dynamics in the 𝑧-coordinate and effectively set all parallel derivatives to zero.…”

Section: Machine Learning Fluid Theory (Again)mentioning

confidence: 99%

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Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment

Mathews¹

2022

Preprint

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Section: Oliver Heavisidementioning

confidence: 99%

Section: Machine Learning Fluid Theory (Again)mentioning

confidence: 99%

Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment

Mathews¹

2022

Preprint

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“…Nevertheless, such small ELMs are associated to degrading confinement and even to an H-mode density limit (n e n GW ). In such cases as the separatrix density is increased (by increasing the gas puff rate), filamentary transport also increases [32,33] and can lead to a flattening of the pressure gradient. This, in turn, causes a reduction of the edge radial electric field 1 which is what causes the back transition to L-mode, i.e., the H-mode density limit (HDL) [32].…”

Section: Small Elms and Eda Hmode At Asdex Upgradementioning

confidence: 99%

MHD simulations of small ELMs at low triangularity in ASDEX Upgrade

Cathey,

Hoelzl,

Harrer

et al. 2021

Preprint

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The development of small-and no-ELM regimes for ITER is a high priority topic due to the risks associated to type-I ELMs. By considering non-linear extended MHD simulations of the ASDEX Upgrade tokamak with the JOREK code, we probe a regime that avoids type-I ELMs completely provided that the separatrix density is high enough. The dynamics of the pedestal in this regime are observed to be qualitatively similar to the so-called quasi-continuous exhaust (QCE) regime in several ways. Repetitive type-I ELMs are substituted by roughly constant levels of outwards transport caused by peelingballooning modes (with dominant ballooning characteristics) which are localised in the last 5% of the confined region (in normalised poloidal flux). The simulated low triangularity plasma transitions to a type-I ELMy H-mode if the separatrix density is sufficiently reduced or if the input heating power is sufficiently increased. The stabilising factors that play a role in the suppression of the small ELMs are also investigated by analysing the simulations, and the importance of including diamagnetic effects in the simulations is highlighted. By considering a scan in the pedestal resistivity and by measuring the poloidal velocity of the modes (and comparing to theoretical estimates for ideal and resistive modes), we identify the underlying instabilities as resistive peeling-ballooning modes. Decreasing the resistivity below experimentally-relevant conditions (i.e., going towards ideal MHD), the peeling-ballooning modes that constrain the pedestal below the type-I ELM stability boundary display sharply decreasing growth rates.

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“…With sparse availability of measurements in fusion experiments, designing diagnostic techniques for validating turbulence theories with limited information is crucial. On this point, we note that our framework can potentially be adapted to experimental measurements of electron pressure [70][71][72][73]. To handle the particular case of 2-dimensional turbulence data, we essentially assume slow variation of dynamics in the z-coordinate and effectively set all parallel derivatives to zero.…”

Section: Machine Learning Fluid Theorymentioning

confidence: 99%

Turbulent field fluctuations in gyrokinetic and fluid plasmas

Mathews,

Mandell,

Francisquez

et al. 2021

Preprint

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A key uncertainty in the design and development of magnetic confinement fusion energy reactors is predicting edge plasma turbulence. An essential step in overcoming this uncertainty is the validation in accuracy of reduced turbulent transport models. Drift-reduced Braginskii two-fluid theory is one such set of reduced equations that has for decades simulated boundary plasmas in experiment, but significant questions exist regarding its predictive ability. To this end, using a novel physics-informed deep learning framework, we demonstrate the first ever direct quantitative comparisons of turbulent field fluctuations between electrostatic two-fluid theory and electromagnetic gyrokinetic modelling with good overall agreement found in magnetized helical plasmas at low normalized pressure. This framework is readily adaptable to experimental and astrophysical environments, and presents a new technique for the numerical validation and discovery of reduced global plasma turbulence models.

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Continuous observation of filaments from the confined region to the far scrape-off layer

Cited by 25 publications

References 23 publications

Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment

Physics-informed machine learning techniques for edge plasma turbulence modelling in computational theory and experiment

MHD simulations of small ELMs at low triangularity in ASDEX Upgrade

Turbulent field fluctuations in gyrokinetic and fluid plasmas

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