Edge plasma measurements on the OP 1.2a divertor plasmas at W7-X using the combined probe

Drews, P.; Killer, C.; Cosfeld, J.; Knieps, A.; Brezinsek, S.; Jakubowski, M.; Brandt, C.; Bozhenkov, S.; Dinklage, A.; Cai, Jianqing; Endler, M.; Hammond, K. C.; Henkel, Malte; Gao, Yu; Geiger, J.; Grulke, O.; Höschen, D.; König, R.; Krämer‐Flecken, A.; Liang, Yun-Feng; Li, Y.; Liu, Shaocheng; Niemann, H.; Nicolai, D.; Neubauer, O.; Neuner, U.; Rack, M.; Rudischhauser, L.; Sandri, N.; Satheeswaran, G.; Schilling, Sabine; Thomsen, H.; Windisch, T.; Sereda, S.

doi:10.1016/j.nme.2019.02.012

Cited by 16 publications

(25 citation statements)

References 13 publications

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“…due to toroidal plasma currents. This issue is, however, outside the scope of this paper and is discussed elsewhere [15]. In the low iota configuration (Figure 1 b)), the MPM path is located slightly above the X-point.…”

Section: Probe Headmentioning

confidence: 83%

“…Operation Phase Description FZJ-COMB1 [13,14] OP1. 1 9 Electric probes, magnetic pick-up probe FZJ-COMB2 [15,16] OP1.2a/b 9 Electric probes, magnetic pick-up probe, ion sensitive probe, material exposition, gas pipe IPP-FLUC1 OP1.2a/b 28 Electric probes (poloidal array, parallel + poloidal Mach probe) FZJ-MACH1 [17] OP1.2a Polar (Gundestrup) + radial Mach probe array (28 electrodes) FZJ-RFA1 [18] OP1.2a 6 retarding field analyzers, 2 electric probe pins FZJ-GAS1 OP1.2a 4 electric probe pins, gas pipe FZJ-GAS2 OP1.2b 4 electric probe pins, piezo valve for gas injection FZJ-MACH2 OP1.2b Polar (Gundestrup) + radial Mach probe array (28 electrodes) FZJ-RFA2 OP1.2b 6 retarding field analyzers, 4 electric probe pins, gas pipe RFX-HRP1 [19] OP1.2b 3 magnetic pick-up probes, 8 electric probe pins, 3 Mach probes NIFS-FILD1 [20] OP1.2b 8 Faraday films for fast ion loss detection FZJ-MAT1…”

Section: Probe Headmentioning

confidence: 99%

“…In both cases the underlying actuator is not easily determined, as plasma beta effects are not expected to be too important (β < 1%) and the toroidal bootstrap current is small (I tor (T 1) = 1 kA, I tor (T 2) = 2.5 kA). Field line tracing calculations incorporating such plasma currents do not reveal significant effects on the position or size of the magnetic island but might hint at a change in the connection length distribution [15]. To investigate the corresponding downstream response, the poloidal heat load profiles [40] on the two divertor modules that are magnetically connected to the MPM are shown in Figure 12.…”

Section: Sol Profiles and Island Dynamics In The Magnetic Standard Comentioning

confidence: 99%

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Characterization of the W7-X scrape-off layer using reciprocating probes

et al. 2019

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Section: Probe Headmentioning

confidence: 83%

Section: Probe Headmentioning

confidence: 99%

Section: Sol Profiles and Island Dynamics In The Magnetic Standard Comentioning

confidence: 99%

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Characterization of the W7-X scrape-off layer using reciprocating probes

et al. 2019

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“…It is interesting to note that particularly in the standard configuration with I cc = 0 differences in the SOL profiles across the magnetic islands have been reported. Observations include nonmonotonic T e profiles [15,20,25,26], a poloidal plasma rotation around the islands due to an 'island radial electric field' [20,27], and a still unexplained fast correlation of plasma fluctuations between inboard and outboard side of an island [13]. The large scatter in the I cc = 0 data in figure 5 further emphasizes the additional complexity that magnetic islands can entail.…”

Section: Turbulent Transport Vs Local Gradientsmentioning

confidence: 93%

Turbulent transport in the scrape-off layer of Wendelstein 7-X

2021

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Turbulent transport is widely considered to be the main driver for cross-field transport in the scrape-off layer (SOL) of toroidal magnetized plasmas. Here, reciprocating Langmuir probes are employed to measure both the plasma profiles and the turbulent particle transport in the SOL of the Wendelstein 7-X stellarator. The relation between turbulent radial particle flux Γ r and the local pressure gradient is often approximately linear across the entire SOL width, indicating that radial turbulence spreading is absent. This observation holds across a wide range of magnetic configurations and different plasma heating and density scenarios. The magnitude of the turbulent transport for a given gradient reveals a dependence on the magnetic configuration and the position in the SOL, which we relate to the cross-spectral characteristics of multi-tip floating potential measurements. Magnetic islands can add further complexity due to non-monotonic SOL profiles and the breaking of the transport-gradient relation. Finally, anomalous diffusion coefficients are determined from the probe measurements.

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“…The profiles are further constrained by the use of virtual observations [34], such that the GP prior is refined with 'virtual diagnostic measurements', described by a normal distribution. As usually observed in W7-X experiments, the electron and ion density and temperature profiles are peaked 2 in the core [59][60][61]. Therefore, the normalized pressure profile is constrained to 0 at the last closed flux surface (LCFS) and 1 on axis.…”

Section: Data Set Generationmentioning

confidence: 89%

Proof of concept of a fast surrogate model of the VMEC code via neural networks in Wendelstein 7-X scenarios

et al. 2021

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In magnetic confinement fusion research, the achievement of high plasma pressure is key to reaching the goal of net energy production. The magnetohydrodynamic (MHD) model is used to self-consistently calculate the effects the plasma pressure induces on the magnetic field used to confine the plasma. Such MHD calculations-usually done computationally-serve as input for the assessment of a number of important physics questions. The variational moments equilibrium code (VMEC) is the most widely used to evaluate 3D ideal-MHD equilibria, as prominently present in stellarators. However, considering the computational cost, it is rarely used in large-scale or online applications (e.g. Bayesian scientific modeling, real-time plasma control). Access to fast MHD equilibria is a challenging problem in fusion research, one which machine learning could effectively address. In this paper, we present artificial neural network (NN) models able to quickly compute the equilibrium magnetic field of Wendelstein 7-X. Magnetic configurations that extensively cover the device operational space, and plasma profiles with volume-averaged normalized plasma pressure β (β = 2μ 0 p B 2 ) up to 5% and non-zero net toroidal current are included in the data set. By using convolutional layers, the spectral representation of the magnetic flux surfaces can be efficiently computed with a single network. To discover better models, a Bayesian hyper-parameter search is carried out, and 3D convolutional NNs are found to outperform feed-forward fully-connected NNs. The achieved normalized root-mean-squared error, the ratio between the regression error and the spread of the data, ranges from 1% to 20% across the different scenarios. The model inference time for a single equilibrium is on the order of milliseconds. Finally, this work shows the feasibility of a fast NN drop-in surrogate model for VMEC, and it opens up new operational scenarios where target applications could make use of magnetic equilibria at unprecedented scales.

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Edge plasma measurements on the OP 1.2a divertor plasmas at W7-X using the combined probe

Cited by 16 publications

References 13 publications

Characterization of the W7-X scrape-off layer using reciprocating probes

Characterization of the W7-X scrape-off layer using reciprocating probes

Turbulent transport in the scrape-off layer of Wendelstein 7-X

Proof of concept of a fast surrogate model of the VMEC code via neural networks in Wendelstein 7-X scenarios

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