| 1. |
- Labit, B., et al.
(författare)
-
Dependence on plasma shape and plasma fueling for small edge-localized mode regimes in TCV and ASDEX Upgrade
- 2019
-
Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 59:8
-
Tidskriftsartikel (refereegranskat)abstract
- © 2019 Institute of Physics Publishing. All rights reserved. Within the EUROfusion MST1 work package, a series of experiments has been conducted on AUG and TCV devices to disentangle the role of plasma fueling and plasma shape for the onset of small ELM regimes. On both devices, small ELM regimes with high confinement are achieved if and only if two conditions are fulfilled at the same time. Firstly, the plasma density at the separatrix must be large enough (ne,sep/nG ∼ 0.3), leading to a pressure profile flattening at the separatrix, which stabilizes type-I ELMs. Secondly, the magnetic configuration has to be close to a double null (DN), leading to a reduction of the magnetic shear in the extreme vicinity of the separatrix. As a consequence, its stabilizing effect on ballooning modes is weakened.
|
|
| 2. |
- Coda, S., et al.
(författare)
-
Physics research on the TCV tokamak facility: From conventional to alternative scenarios and beyond
- 2019
-
Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 59:11
-
Tidskriftsartikel (refereegranskat)abstract
- The research program of the TCV tokamak ranges from conventional to advanced-tokamak scenarios and alternative divertor configurations, to exploratory plasmas driven by theoretical insight, exploiting the device's unique shaping capabilities. Disruption avoidance by real-time locked mode prevention or unlocking with electron-cyclotron resonance heating (ECRH) was thoroughly documented, using magnetic and radiation triggers. Runaway generation with high-Z noble-gas injection and runaway dissipation by subsequent Ne or Ar injection were studied for model validation. The new 1 MW neutral beam injector has expanded the parameter range, now encompassing ELMy H-modes in an ITER-like shape and nearly non-inductive H-mode discharges sustained by electron cyclotron and neutral beam current drive. In the H-mode, the pedestal pressure increases modestly with nitrogen seeding while fueling moves the density pedestal outwards, but the plasma stored energy is largely uncorrelated to either seeding or fueling. High fueling at high triangularity is key to accessing the attractive small edge-localized mode (type-II) regime. Turbulence is reduced in the core at negative triangularity, consistent with increased confinement and in accord with global gyrokinetic simulations. The geodesic acoustic mode, possibly coupled with avalanche events, has been linked with particle flow to the wall in diverted plasmas. Detachment, scrape-off layer transport, and turbulence were studied in L- and H-modes in both standard and alternative configurations (snowflake, super-X, and beyond). The detachment process is caused by power 'starvation' reducing the ionization source, with volume recombination playing only a minor role. Partial detachment in the H-mode is obtained with impurity seeding and has shown little dependence on flux expansion in standard single-null geometry. In the attached L-mode phase, increasing the outer connection length reduces the in-out heat-flow asymmetry. A doublet plasma, featuring an internal X-point, was achieved successfully, and a transport barrier was observed in the mantle just outside the internal separatrix. In the near future variable-configuration baffles and possibly divertor pumping will be introduced to investigate the effect of divertor closure on exhaust and performance, and 3.5 MW ECRH and 1 MW neutral beam injection heating will be added.
|
|
| 3. |
- Reimerdes, H., et al.
(författare)
-
Overview of the TCV tokamak experimental programme
- 2022
-
Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 62:4
-
Tidskriftsartikel (refereegranskat)abstract
- The tokamak a configuration variable (TCV) continues to leverage its unique shaping capabilities, flexible heating systems and modern control system to address critical issues in preparation for ITER and a fusion power plant. For the 2019-20 campaign its configurational flexibility has been enhanced with the installation of removable divertor gas baffles, its diagnostic capabilities with an extensive set of upgrades and its heating systems with new dual frequency gyrotrons. The gas baffles reduce coupling between the divertor and the main chamber and allow for detailed investigations on the role of fuelling in general and, together with upgraded boundary diagnostics, test divertor and edge models in particular. The increased heating capabilities broaden the operational regime to include T (e)/T (i) similar to 1 and have stimulated refocussing studies from L-mode to H-mode across a range of research topics. ITER baseline parameters were reached in type-I ELMy H-modes and alternative regimes with 'small' (or no) ELMs explored. Most prominently, negative triangularity was investigated in detail and confirmed as an attractive scenario with H-mode level core confinement but an L-mode edge. Emphasis was also placed on control, where an increased number of observers, actuators and control solutions became available and are now integrated into a generic control framework as will be needed in future devices. The quantity and quality of results of the 2019-20 TCV campaign are a testament to its successful integration within the European research effort alongside a vibrant domestic programme and international collaborations.
|
|
| 4. |
- Jaervinen, A. E., et al.
(författare)
-
Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG
- 2024
-
Ingår i: Physics of Plasmas. - : AIP Publishing. - 1070-664X .- 1089-7674. ; 31:3
-
Tidskriftsartikel (refereegranskat)abstract
- Variational autoencoder (VAE)-based representation learning algorithms are explored for their capability to disentangle tokamak size dependence from other dependencies in a dataset of thousands of observed pedestal electron density and temperature profiles from JET and ASDEX Upgrade tokamaks. Representation learning aims to establish a useful representation that characterizes the dataset. In the context of magnetic confinement fusion devices, a useful representation could be considered to map the high-dimensional observations to a manifold that represents the actual degrees of freedom of the plasma scenario. A desired property for these representations is organization of the information into disentangled variables, enabling interpretation of the latent variables as representations of semantically meaningful characteristics of the data. The representation learning algorithms in this work are based on VAE that encodes the pedestal profile information into a reduced dimensionality latent space and learns to reconstruct the full profile information given the latent representation. Attaching an auxiliary regression objective for the machine control parameter configuration, broadly following the architecture of the domain invariant variational autoencoder (DIVA), the model learns to associate device control parameters with the latent representation. With this multimachine dataset, the representation does encode density scaling with device size that is qualitatively consistent with Greenwald density limit scaling. However, if the major radius of the device is given through a common regression objective with the other machine control parameters, the latent state of the representation struggles to clearly disentangle the device size from changes of the other machine control parameters. When separating the device size as an independent latent variable with dedicated regression objectives, similar to separation of domain and class labels in the original DIVA publication, the latent space becomes well organized as a function of the device size.
|
|
