-
UKAEA-CCFE-PR(23)1802023
-
UKAEA-CCFE-PR(23)1142022
This work applies the coupled JINTRAC and QuaLiKiz-neural-network (QLKNN) model on the ohmic current ramp-up phase of a JET D discharge. The chosen scenario exhibits a hollow Te profile attributed to core impurity accumulation, which is observed to worsen with the increasing fuel ion mass from D to T. A dynamic D simulation was validated, evolvi…
-
UKAEA-CCFE-PR(23)882020
The pellet cycle of a mixed isotope tokamak plasma is successfully reproduced with reduced turbulent transport modelling within an integrated simulation framework. In JET tokamak experiments, deuterium pellets with reactor-relevant deposition characteristics were injected into a pure hydrogen plasma. Measurements of the isotope ratio profile inf…
-
UKAEA-CCFE-PR(20)1222019
We present an ultrafast neural network (NN) turbulent tokamak transport model, QLKNN, for heat and particle fluxes. QLKNN is a surrogate model based on a database of 3 · 108 flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. To ensure accurate reproduction of the underlying model, we include known features of the…
-
UKAEA-CCFE-PR(20)172019
Core turbulent particle transport with multiple isotopes can display observable differences in behaviour between the electron and ion particle channels. Experimental observations at JET with mixed H-D plasmas and varying NBI and gas-puff sources [M. Maslov et al., Nucl. Fusion 7 076022 (2018)] inferred source dominated electr…
-
UKAEA-CCFE-PR(19)752019
The evolution of the JET high performance hybrid scenario, including central accumulation of the tungsten (W) impurity, is reproduced with predictive multi-channel integrated modelling over multiple confinement times using first-principle based models. 8 transport channels are modelled predictively, with self-consistent sources, radiation and magne…
-
UKAEA-CCFE-CP(18)022018
-
UKAEA-CCFE-PR(19)102018
This paper outlines an approach towards improved rigour in tokamak turbulence transport model validation within integrated modelling. Gaussian process regression (GPR) techniques were applied for profile fitting during the preparation of integrated modelling simulations. This allows for rigourous sensitivity tests of prescribed initial and bound…
-
UKAEA-CCFE-PR(18)542018
Neoclassical and turbulent heavy impurity transport in tokamak core plasmas are determined by main ion temperature, density and toroidal rotation profiles. Thus, in order to reproduce experimental behaviour of W accumulation, integrated modelling of main ion heat and particle transport is a vital prerequisite. For the first time, the quasilinear …
-
UKAEA-CCFE-PR(18)152018
Particle transport is of a great importance for understanding physics of tokamak plasmas and planning future experiments on larger machines such as ITER. The subject was intensively studied in the past, particularly in relation to density peaking and presence of anomalous inward particle convection in L- and H-mode. While in the L-mode case presenc…
Showing 1 - 10 of 16 UKAEA Paper Results