Power handling and vapor shielding of pre-filled lithium divertor targets

This dataset is the replication package on experimentation and modeling, accompanying the paper: Power handling and vapor shielding of pre-filled lithium divertor targets Magnum-PS

Dataset underlying the paper: Using 3D-printed tungsten to optimize liquid metal divertor targets for flow and thermal stresses

Dataset accompanying the paper titled: Using 3D-printed tungsten to optimize liquid metal divertor targets for flow and thermal stresses. Includes experimental replication package and detailed information on conducted FEM modelin

Combined inversions from Multi-Wavelength Imaging and X-Point Imaging of D2 Fulcher emission

NAME,SIZE,TYPE,EXT,LAST_MODIFIED,FORMAT_NAME,MIME_TYPE eps_sh48330_multicam_Fulcher_grid_grid_MAST-U_MU03_lower_10mm_SART_v1_10mm.npz,47952306,Container,npz,2024-03-28T15:57:06,ZIP Format,application/zip Nt_eps.npy,136,File,npy,1980-01-01T00:00:00,, eps.npy,98337960,File,npy,1980-01-01T00:00:00,, datetime.npy,432,File,npy,1980-01-01T00:00:00,, inv_data.npy,3590,File,npy,1980-01-01T00:00:00,, time_eps.npy,2472,File,npy,1980-01-01T00:00:00,, inv_settings.npy,845803,File,npy,1980-01-01T00:00:00,

Experimental data on system identification studies and real-time feedback detachment control of MAST-U discharges with different divertor configurations. Data on synthetic diagnostic implementation for STEP.

Programme Area:Tokamak Scienc

Data set accompanying: Topology of the Warm plasma dispersion relation at the second Harmonic Electron Cyclotron Resonance Layer

The Warm Plasma Dispersion Relation, for waves in the electron cyclotron resonance range of frequencies, can be cast into the form of a bi-quadratic equation for $N_\perp$, where the coefficients are a function of $N_\perp^2$ and an iterative procedure is required to obtain a solution. However, this iterative procedure is not well understood and fails to converge towards a solution at the second harmonic resonance layer. In particular at higher densities where the wave can couple to an electron Bernstein wave. This paper focuses on a solution to the poor convergence of the iterative method, enabling determination of the topology of the dispersion relation around the second harmonic using a fully relativistic code for oblique waves. A feed-forward controller is proposed with the ability to adjust the rotation of a step of $N_\perp^2$ within the complex plane, while also limiting the step-size. It is shown that implementation of the controller stabilizes unstable solutions, while improving overall robustness of the iteration. This allows the evaluation of the coupling between the fast extraordinary mode and electron Bernstein waves at the second harmonic electron cyclotron resonance layer, for non-perpendicularly propagating waves

QLKNN7D-edge training set

QLKNN7D-edge training set This dataset contains a large-scale run of ~15 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The dataset is in a parameter regime typical of the L-mode near edge (pedestal forming region). QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at https://gitlab.com/qualikiz-group/QuaLiKiz/. This dataset was generated with QuaLiKiz 2.8.4, which includes numerical improvements increasing the robustness of strongly driven (high gradient) calculations typical of the L-mode near-edge. See https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/2.8.4 for the in-repository tag. The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See https://doi.org/10.1063/1.5134126 for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on arXiv and the older dataset on Zenodo. For an application example, see Van Mulders et al 2021, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. An additional, larger, QuaLiKiz dataset is found at https://zenodo.org/record/8017522. Neither the QLKNN10D or QLKNN11D datasets include L-mode near-edge parameters. For any learned surrogates developed for QLKNN7D-edge, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended. Related repositories: General QuaLiKiz documentation QuaLiKiz/QLKNN input/output variables naming scheme Training, plotting, filtering, and auxiliary tools QuaLiKiz related tools FORTRAN QLKNN implementation with wrapper for Python and MATLAB Weights and biases of 'hyperrectangle style' QLKN

QLKNN11D training set

QLKNN11D training set This dataset contains a large-scale run of ~1 billion flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. QuaLiKiz is applied in numerous tokamak integrated modelling suites, and is openly available at https://gitlab.com/qualikiz-group/QuaLiKiz/. This dataset was generated with the 'QLKNN11D-hyper' tag of QuaLiKiz, equivalent to 2.8.1 apart from the negative magnetic shear filter being disabled. See https://gitlab.com/qualikiz-group/QuaLiKiz/-/tags/QLKNN11D-hyper for the in-repository tag. The dataset is appropriate for the training of learned surrogates of QuaLiKiz, e.g. with neural networks. See https://doi.org/10.1063/1.5134126 for a Physics of Plasmas publication illustrating the development of a learned surrogate (QLKNN10D-hyper) of an older version of QuaLiKiz (2.4.0) with a 300 million point 10D dataset. The paper is also available on arXiv https://arxiv.org/abs/1911.05617 and the older dataset on Zenodo https://doi.org/10.5281/zenodo.3497066. For an application example, see Van Mulders et al 2021 https://doi.org/10.1088/1741-4326/ac0d12, where QLKNN10D-hyper was applied for ITER hybrid scenario optimization. For any learned surrogates developed for QLKNN11D, the effective addition of the alphaMHD input dimension through rescaling the input magnetic shear (s) by s = s - alpha_MHD/2, as carried out in Van Mulders et al., is recommended. Related repositories: General QuaLiKiz documentation https://qualikiz.com QuaLiKiz/QLKNN input/output variables naming scheme https://qualikiz.com/QuaLiKiz/Input-and-output-variables Training, plotting, filtering, and auxiliary tools https://gitlab.com/Karel-van-de-Plassche/QLKNN-develop QuaLiKiz related tools https://gitlab.com/qualikiz-group/QuaLiKiz-pythontools FORTRAN QLKNN implementation with wrapper for Python and MATLAB https://gitlab.com/qualikiz-group/QLKNN-fortran Weights and biases of 'hyperrectangle style' QLKNN https://gitlab.com/qualikiz-group/qlknn-hype Data exploration The data is provided in 43 netCDF files. We advise opening single datasets using xarray or multiple datasets out-of-core using dask. For reference, we give the load times and sizes of a single variable that just depends on the scan size `dimx` below. This was tested single-core on a Intel Xeon 8160 CPU at 2.1 GHz and 192 GB of DDR4 RAM. Note that during loading, more memory is needed than the final number. Timing of dataset loading Amount of datasets Final in-RAM memory (GiB) Loading time single var (M:SS) 1 10.3 0:09 5 43.9 1:00 10 63.2 2:01 16 98.0 3:25 17 Out Of Memory x:xx Full dataset The full dataset of QuaLiKiz in-and-output data is available on request. Note that this is 2.2 TiB of netCDF files

Experimental data and simulations comparing the divertor and core performance of the MAST Upgrade Super-X, Elongated and Conventional divertors in beam-heated L-mode conditions

Programme Area:Tokamak Scienc

Replication package Separation of transport in slow and fast time-scales using modulated heat pulse experiments (hysteresis in flux explained)

Old and recent experiments show that there is a direct response to the heating power of transport observed in modulated ECH experiments both in tokamaks and stellarators. This is most apparent for modulated experiments in the Large Helical Device (LHD) and in Wendelstein 7 advanced stellarator (W7-AS). In this paper we show that: 1) This power dependence can be reproduced by linear models and as such hysteresis (in flux) has no relationship to hysteresis as defined in the literature; 2) Observations of hysteresis (in flux) and a direct response to power can be perfectly reproduced by introducing an error in the estimated deposition profile as long as the errors redistribute the heat over a large radius; 3) Non-local models depending directly on the heating power can also explain the experimentally observed Lissajous curves (hysteresis); 4) How non-locality and deposition errors can be recognized in experiments and how they affect estimates of transport coefficients; 5) From a linear perturbation transport experiment, it is not possible to discern deposition errors from non-local fast transport components (mathematically equivalent). However, when studied over different operating points non-linear-non-local transport models can be derived which should be distinguishable from errors in deposition profile. To show all this, transport needs to be analyzed by separating the transport in a slow (diffusive) time-scale and a fast (heating/non-local) time-scale, which can only be done in the presence of perturbations

LiMeS-lab: An integrated laboratory for the development of Liquid-Metal Shield technologies for fusion reactors

The liquid metal shield laboratory (LiMeS-Lab) will provide the infrastructure to develop, test, and compare liquid metal divertor designs for future fusion reactors. The main research topics of LiMeS-lab will be liquid metal interactions with the substrate material of the divertor, the continuous circulation and capillary refilling of the liquid metal during intense plasma heat loading and the retention of plasma particles in the liquid metal. To facilitate the research, four new devices are in development at the Dutch Institute for Fundamental Energy Research and the Eindhoven University of Technology: LiMeS-AM: a custom metal 3D printer based on powder bed fusion; LiMeS-Wetting, a plasma device to study the wetting of liquid metals on various substrates with different surface treatments; LiMeS-PSI, a linear plasma generator specifically adapted to operate continuous liquid metal loops. Special diagnostic protection will also be implemented to perform measurements in long duration shots without being affected by the liquid metal vapor; LiMeS-TDS, a thermal desorption spectroscopy system to characterize deuterium retention in a metal vapor environment. Each of these devices has specific challenges due to the presence and deposition of metal vapors that need to be addressed in order to function. In this paper, an overview of LiMeS-Lab will be given and the conceptual designs of the last three devices will be presented