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Type I ELMy H-mode operation in JET with the ITER-like Be/W wall (JET-ILW) generally occurs at lower pedestal pressures compared to those with the full carbon wall (JET-C). The pedestal density is similar but the pedestal temperature where type I ELMs occur is reduced and below to the so-called critical type I–type III transition temperature reported in JET-C experiments. Furthermore, the confinement factor H98(y,2) in type I ELMy H-mode baseline plasmas is generally lower in JET-ILW compared to JET-C at low power fractions Ploss/Pthr,08 < 2 (where Ploss is (Pin − dW/dt), and Pthr,08 the L–H power threshold from Martin et al 2008 (J. Phys. Conf. Ser. 123 012033)). Higher power fractions have thus far not been achieved in the baseline plasmas. At Ploss/Pthr,08 > 2, the confinement in JET-ILW hybrid plasmas is similar to that in JET-C. A reduction in pedestal pressure is the main reason for the reduced confinement in JET-ILW baseline ELMy H-mode plasmas where typically H98(y,2) = 0.8 is obtained, compared to H98(y,2) = 1.0 in JET-C. In JET-ILW hybrid plasmas a similarly reduced pedestal pressure is compensated by an increased peaking of the core pressure profile resulting in H98(y,2) ⩽ 1.25. The pedestal stability has significantly changed in high triangularity baseline plasmas where the confinement loss is also most apparent. Applying the same stability analysis for JET-C and JET-ILW, the measured pedestal in JET-ILW is stable with respect to the calculated peeling–ballooning stability limit and the ELM collapse time has increased to 2 ms from typically 200 µs in JET-C. This indicates that changes in the pedestal stability may have contributed to the reduced pedestal confinement in JET-ILW plasmas. A comparison of EPED1 pedestal pressure prediction with JET-ILW experimental data in over 500 JET-C and JET-ILW baseline and hybrid plasmas shows a good agreement with 0.8 < (measured pped)/(predicted pped,EPED) < 1.2, but that the role of triangularity is generally weaker in the JET-ILW experimental data than in the model predictions.
The EUROfusion JET-ILW pedestal database is described, with emphasis on three main issues. First, the technical aspects are introduced, including a description of the data selection, the datasets, the diagnostics used, the experimental and theoretical methods implemented and the main definitions. Second, the JET-ILW pedestal structure and stability are described. In particular, the work describes the links between the engineering parameters (power, gas and divertor configuration) and the disagreement with the peeling-ballooning (PB) model implemented with ideal MHD equations. Specifically, the work clarifies why the JET-ILW pedestal tends to be far from the PB boundary at high gas and high power, showing that a universal threshold in power and gas cannot be found but that the relative shift (the distance between the position of the pedestal density and of the pedestal temperature) plays a key role. These links are then used to achieve an empirical explanation of the behavior of the JET-ILW pedestal pressure with gas, power and divertor configuration. Third, the pedestal database is used to revise the scaling law of the pedestal stored energy. The work shows a reasonable agreement with the earlier Cordey scaling in terms of plasma current and triangularity dependence, but highlights some differences in terms of power and isotope mass dependence.
We present an ultrafast neural network (NN) model, QLKNN, which predicts core tokamak transport heat and particle fluxes. QLKNN is a surrogate model based on a database of 300 million flux calculations of the quasilinear gyrokinetic transport model QuaLiKiz. The database covers a wide range of realistic tokamak core parameters. Physical features such as the existence of a critical gradient for the onset of turbulent transport were integrated into the neural network training methodology. We have coupled QLKNN to the tokamak modelling framework JINTRAC and rapid control-oriented tokamak transport solver RAPTOR. The coupled frameworks are demonstrated and validated through application to three JET shots covering a representative spread of H-mode operating space, predicting turbulent transport of energy and particles in the plasma core. JINTRAC-QLKNN and RAPTOR-QLKNN are able to accurately reproduce JINTRAC-QuaLiKiz T i,e and n e profiles, but 3 to 5 orders of magnitude faster. Simulations which take hours are reduced down to only a few tens of seconds. The discrepancy in the final source-driven predicted profiles between QLKNN and QuaLiKiz is on the order 1%-15%. Also the dynamic behaviour was well captured by QLKNN, with differences of only 4%-10% compared to JINTRAC-QuaLiKiz observed at mid-radius, for a study of density buildup following the L-H transition. Deployment of neural network surrogate models in multi-physics integrated tokamak modelling is a promising route towards enabling accurate and fast tokamak scenario optimization, Uncertainty Quantification, and control applications.
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