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- Fenstermacher, M.E., et al.
(författare)
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DIII-D research advancing the physics basis for optimizing the tokamak approach to fusion energy
- 2022
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 0029-5515 .- 1741-4326. ; 62:4
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Tidskriftsartikel (refereegranskat)abstract
- DIII-D physics research addresses critical challenges for the operation of ITER and the next generation of fusion energy devices. This is done through a focus on innovations to provide solutions for high performance long pulse operation, coupled with fundamental plasma physics understanding and model validation, to drive scenario development by integrating high performance core and boundary plasmas. Substantial increases in off-axis current drive efficiency from an innovative top launch system for EC power, and in pressure broadening for Alfven eigenmode control from a co-/counter-I p steerable off-axis neutral beam, all improve the prospects for optimization of future long pulse/steady state high performance tokamak operation. Fundamental studies into the modes that drive the evolution of the pedestal pressure profile and electron vs ion heat flux validate predictive models of pedestal recovery after ELMs. Understanding the physics mechanisms of ELM control and density pumpout by 3D magnetic perturbation fields leads to confident predictions for ITER and future devices. Validated modeling of high-Z shattered pellet injection for disruption mitigation, runaway electron dissipation, and techniques for disruption prediction and avoidance including machine learning, give confidence in handling disruptivity for future devices. For the non-nuclear phase of ITER, two actuators are identified to lower the L-H threshold power in hydrogen plasmas. With this physics understanding and suite of capabilities, a high poloidal beta optimized-core scenario with an internal transport barrier that projects nearly to Q = 10 in ITER at ∼8 MA was coupled to a detached divertor, and a near super H-mode optimized-pedestal scenario with co-I p beam injection was coupled to a radiative divertor. The hybrid core scenario was achieved directly, without the need for anomalous current diffusion, using off-axis current drive actuators. Also, a controller to assess proximity to stability limits and regulate β N in the ITER baseline scenario, based on plasma response to probing 3D fields, was demonstrated. Finally, innovative tokamak operation using a negative triangularity shape showed many attractive features for future pilot plant operation.
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- Reimerdes, H., et al.
(författare)
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Overview of the TCV tokamak experimental programme
- 2022
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Ingår i: Nuclear Fusion. - : IOP Publishing. - 1741-4326 .- 0029-5515. ; 62:4
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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.
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- Litaudon, X., et al.
(författare)
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EUROfusion-theory and advanced simulation coordination (E-TASC): programme and the role of high performance computing
- 2022
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Ingår i: Plasma Physics and Controlled Fusion. - : IOP Publishing. - 1361-6587 .- 0741-3335. ; 64:3
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Tidskriftsartikel (refereegranskat)abstract
- This paper is a written summary of an overview oral presentation given at the 1st Spanish Fusion High Performance Computer (HPC) Workshop that took place on the 27 November 2020 as an online event. Given that over the next few years ITER24 will move to its operation phase and the European-DEMO design will be significantly advanced, the EUROfusion consortium has initiated a coordination effort in theory and advanced simulation to address some of the challenges of the fusion research in Horizon EUROPE (2021-2027), i.e. the next EU Framework Programme for Research and Technological Development. This initiative has been called E-TASC, which stands for EUROfusion-Theory and Advanced Simulation Coordination. The general and guiding principles of E-TASC are summarized in this paper. In addition, an overview of the scientific results obtained in the pilot phase (2019-2020) of E-TASC are provided while highlighting the importance of the required progress in computational methods and HPC techniques. In the initial phase, five pilot theory and simulation tasks were initiated: towards a validated predictive capability of the low to high transition and pedestal physics; runaway electrons in tokamak disruptions in the presence of massive material injection; fast code for the calculation of neoclassical toroidal viscosity in stellarators and tokamaks; development of a neutral gas kinetics modular code; European edge and boundary code for reactor-relevant devices. In this paper, we report on recent progress made by each of these projects.
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- Jaervinen, A. E., et al.
(författare)
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Representation learning algorithms for inferring machine independent latent features in pedestals in JET and AUG
- 2024
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Ingår i: Physics of Plasmas. - : AIP Publishing. - 1070-664X .- 1089-7674. ; 31:3
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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.
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- Kit, A., et al.
(författare)
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Developing deep learning algorithms for inferring upstream separatrix density at JET
- 2023
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Ingår i: Nuclear Materials and Energy. - : Elsevier BV. - 2352-1791. ; 34
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Tidskriftsartikel (refereegranskat)abstract
- Predictive and real-time inference capability for the upstream separatrix electron density, ne, sep, is essential for design and control of core-edge integrated plasma scenarios. In this study, both supervised and semi -supervised machine learning algorithms are explored to establish direct mapping as well as indirect compressed representation of the pedestal profiles for predictions and inference of ne, sep. Based on the EUROfusion pedestal database for JET (Frassinetti et al., 2021), a tabular dataset was created, consisting of machine parameters, fraction of ELM cycle, high resolution Thomson scattering profiles of electron density and temperature, and ne, sep for 608 JET shots. Using the tabular dataset, the direct mapping approach provides a mapping of machine parameters and ELM percentage to ne, sep. Through representation learning, a compressed representation of the experimental pedestal electron density and temperature profiles is established. By conditioning the representation with machine control parameters, a probabilistic generative predictive model is established. For prediction, the machine parameters can be used to establish a conditional distribution of the compressed pedestal profiles, and the decoder that is trained as part of the algorithm can be used to decode the compressed representation back to full pedestal profiles. Although, in this work, a proof-of-principle for predicting and inferring ne, sep is given, such a representation learning can be used also for many other applications as the full pedestal profile is predicted. An implementation of this work can be found at https://github.com/ fusionby2030/psi_2022.
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- Kit, A., et al.
(författare)
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Supervised learning approaches to modeling pedestal density
- 2023
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Ingår i: Plasma Physics and Controlled Fusion. - : IOP Publishing. - 0741-3335 .- 1361-6587. ; 65:4
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Tidskriftsartikel (refereegranskat)abstract
- Pedestals are the key to conventional high performance plasma scenarios in tokamaks. However, high fidelity simulations of pedestal plasmas are extremely challenging due to the multiple physical processes and scales that are encompassed by tokamak pedestals. The leading paradigm for predicting the pedestal top pressure is encompassed by EPED-like models. However, EPED does not predict the pedestal top density, n(e,ped), but requires it as an input. EUROPED (Saarelma et al 2019 Phys. Plasmas 26 072501) employs simplified models, such as log-linear regression, to constrain n(e,ped) with tokamak machine control parameters in an EPED-like model. However, these simplified models for n(e,ped) often show disagreements with experimental observations and do not use all of the available numerical and categorical machine control information. In this work it is observed that using the same input parameters, decision tree ensembles and deep learning models improves the predictive quality of n(e,ped) by about 23% relative to that obtained with log-linear scaling laws, measured by root mean square error. Including all of the available tokamak machine control parameters, both numerical and categorical, leads to further improvement of about 13%. Finally, predictive quality was tested when including global normalized plasma pressure and effective charge state as inputs, as these parameters are known to impact pedestals. Surprisingly, these parameters lead to only a few percent further improvement of the predictive quality. The corresponding code for this analysis can be found at github.com/fusionby2030/supervised_learning_jetpdb.
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