Published March 22, 2021 | Version v1.0
Dataset Open

QuaLiKiz-v2.6.2 linear instability spectra based on JET experimental plasma profiles

Creators

  • 1. DIFFER

Description

This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: https://doi.org/10.1063/5.0038290. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at www.qualikiz.com.

The data is saved under 3 keys in HDF5 format: "/input", "/spectrum", and "/wavenumber". The '/input' key contains the inputs used for the QuaLiKiz evaluations, representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. The "/spectrum" key contains the linear growth rate and frequency spectra corresponding to the 2 most dominant microinstabilities determined by the calculation (s0 = dominant, s1 = sub-dominant). The "/wavenumber" key contains an array representing the standard set of 18 wavenumbers (\(k_y \rho_s\)) was used to generate the spectra (k0 = lowest wavenumber, k17 = highest wavenumber).

Notes

The sole responsibility of this dataset belongs to A. Ho (contact: a.ho@differ.nl). Any further work performed using this dataset does have any affiliation with JET unless explicitly agreed-upon with the JET team, or with QuaLiKiz unless explicitly agreed-upon with the QuaLiKiz development team.

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Additional details

Related works

Is supplement to
Dataset: 10.5281/zenodo.7079669 (DOI)

Funding

European Commission
EUROfusion - Implementation of activities described in the Roadmap to Fusion during Horizon 2020 through a Joint programme of the members of the EUROfusion consortium 633053