Interpretative and predictive modelling of Joint European Torus collisionality scans

Transport modelling of Joint European Torus (JET) dimensionless collisionality scaling experiments in various operational scenarios is presented. Interpretative simulations at a fixed radial position are combined with predictive JETTO simulations of temperatures and densities, using the TGLF transport model. The model includes electromagnetic effects and collisions as well as (E)over-right-arrow x (b)over-right-arrow shear in Miller geometry. Focus is on particle transport and the role of the neutral beam injection (NBI) particle source for the density peaking. The experimental 3-point collisionality scans include L-mode, and H-mode (D and H and higher beta D plasma) plasmas in a total of 12 discharges. Experimental results presented in (Tala et al 2017 44th EPS Conf.) indicate that for the H-mode scans, the NBI particle source plays an important role for the density peaking, whereas for the L-mode scan, the influence of the particle source is small. In general, both the interpretative and predictive transport simulations support the experimental conclusions on the role of the NBI particle source for the 12 JET discharges

Overview of JET results for optimising ITER operation

The JET 2019-2020 scientific and technological programme exploited the results of years of concerted scientific and engineering work, including the ITER-like wall (ILW: Be wall and W divertor) installed in 2010, improved diagnostic capabilities now fully available, a major neutral beam injection upgrade providing record power in 2019-2020, and tested the technical and procedural preparation for safe operation with tritium. Research along three complementary axes yielded a wealth of new results. Firstly, the JET plasma programme delivered scenarios suitable for high fusion power and alpha particle (alpha) physics in the coming D-T campaign (DTE2), with record sustained neutron rates, as well as plasmas for clarifying the impact of isotope mass on plasma core, edge and plasma-wall interactions, and for ITER pre-fusion power operation. The efficacy of the newly installed shattered pellet injector for mitigating disruption forces and runaway electrons was demonstrated. Secondly, research on the consequences of long-term exposure to JET-ILW plasma was completed, with emphasis on wall damage and fuel retention, and with analyses of wall materials and dust particles that will help validate assumptions and codes for design and operation of ITER and DEMO. Thirdly, the nuclear technology programme aiming to deliver maximum technological return from operations in D, T and D-T benefited from the highest D-D neutron yield in years, securing results for validating radiation transport and activation codes, and nuclear data for ITER

First principles and integrated modelling achievements towards trustful fusion power predictions for JET and ITER

Predictability of burning plasmas is a key issue for designing and building credible future fusion devices. In this context, an important effort of physics understanding and guidance is being carried out in parallel to JET experimental campaigns in H and D by performing analyses and modelling towards an improvement of the understanding of DT physics for the optimization of the JET-DT neutron yield and fusion born alpha particle physics. Extrapolations to JET-DT from recent experiments using the maximum power available have been performed including some of the most sophisticated codes and a broad selection of models. There is a general agreement that 11-15 MW of fusion power can be expected in DT for the hybrid and baseline scenarios. On the other hand, in high beta, torque and fast ion fraction conditions, isotope effects could be favourable leading to higher fusion yield. It is shown that alpha particles related physics, such as TAE destabilization or fusion power electron heating, could be studied in ITER relevant JET-DT plasmas

Ion cyclotron resonance heating scenarios for DEMO

The present paper offers an overview of the potential of ion cyclotron resonance heating (ICRH) or radio frequency heating for the DEMO machine. It is found that various suitable heating schemes are available. Similar to ITER and in view of the limited bandwidth of about 10 MHz that can be achieved to ensure optimal functioning of the launcher, it is proposed to make core second harmonic tritium heating the key ion heating scheme, assisted by fundamental cyclotron heating He-3 in the early phase of the discharge; for the present design of DEMO-with a static magnetic field strength of B-o = 5.855 T-that places the T and 3He layers in the core for f = 60 MHz and suggests centering the bandwidth around that main operating frequency. In line with earlier studies for hot, dense plasmas in large-size magnetic confinement machines, it is shown that good single pass absorption is achieved but that the size as well as the operating density and temperature of the machine cause the electrons to absorb a non-negligible fraction of the power away from the core when core ion heating is aimed at. Current drive and alternative heating options are briefly discussed and a dedicated computation is done for the traveling wave antenna, proposed for DEMO in view of its compatibility with substantial antenna-plasma distances. The various tasks that ICRH can fulfill are briefly listed. Finally, the impact of transport and the sensitivity of the obtained results to changes in the machine parameters is commented on

Adaptive learning for disruption prediction in non-stationary conditions

For many years, machine learning tools have proved to be very powerful disruption predictors in tokamaks. On the other hand, the vast majority of the techniques deployed assume that the input data is independent and is sampled from exactly the same probability distribution for the training set, the test set and the final real time deployment. This hypothesis is certainly not verified in practice, since the experimental programmes evolve quite rapidly, resulting typically in ageing of the predictors and consequent suboptimal performance. This paper describes various adaptive training strategies that have been tested to maintain the performance of disruption predictors in non-stationary conditions. The proposed approaches have been implemented using new ensembles of classifiers, explicitly developed for the present application. The improvements in performance are unquestionable and, given the difficulties encountered so far in translating predictors from one device to another, the proposed adaptive methods from scratch can therefore be considered a useful option in the arsenal of alternatives envisaged for the next generation of devices, particularly at the very beginning of their operation

Multiphysics approach to plasma neutron source modelling at the JET tokamak

A novel multiphysics methodology for the computation of realistic plasma neutron sources has been developed. The method is based on state-of-the-art plasma transport and neutron spectrum calculations, coupled with a Monte Carlo neutron transport code, bridging the gap between plasma physics and neutronics. In the paper two JET neutronics tokamak models are used to demonstrate the application of the developed plasma neutron sources and validate them. Diagnostic data for the record JET D discharge 92436 are used as input for the TRANSP code, modelling neutron emission in two external plasma heating scenarios, namely using only neutral beam injection and a combination of the latter and ion cyclotron resonance heating. Neutron spectra, based on plasma transport results, are computed using the DRESS code. The developed PLANET code package is employed to generate plasma neutron source descriptions and couple them with the MCNP code. The effects of using the developed sources in neutron transport calculations on the response of JET neutron diagnostic systems is studied and compared to the results obtained with a generic plasma neutron source. It is shown that, although there are significant differences in the emissivity profiles, spectra shape and anisotropy between the neutron sources, the integral response of the time-resolved ex-vessel neutron detectors is largely insensitive to source changes, with major relative deviations of up to several percent. However it is calculated that, due to the broadening of neutron spectra as a consequence of external plasma heating, larger differences may occur in activation of materials which have threshold reactions located at DD neutron peak energies. The PLANET plasma neutron source computational methodology is demonstrated to be suitable for detailed neutron source effect studies on JET during DT experiments and can be applied to ITER analyses

COREDIV numerical simulation of high neutron rate JET-ILW DD pulses in view of extension to JET-ILW DT experiments

Two high performance JET-ILW pulses, pertaining to the 2016 experimental campaign, have been numerically simulated with the self-consistent code COREDIV with the aim of predicting the ELM-averaged power load to the target when extrapolated to DT plasmas. The input power of about 33 MW as well as the total radiated power and the average density are similar in the two pulses, but for one of them the density is provided by combined low gas puff and pellet injection, characterized by low SOL density, for the other one by gas fuelling only, at higher SOT. density. Considering the magnetic configuration of theses pulses and the presence of a significant amount of Ni (not included in the version of the code used for these simulations), a number of assumptions are made in order to reproduce numerically the main core and SOL experimental data. The extrapolation to DT plasmas at the original input power of 33 MW, and taking into account only the thermal component of the alpha-power, does not show any significant difference regarding the power to the target with respect to the DD case. In contrast, the simulations at auxiliary power 40 MW, both at the original I-p = 3 MA and at I-p = 4 MA, show that the power to the target for both pulses is possibly too high to be sustained for about 5 s by strike-point sweeping alone without any control by Ne seeding. Even though the target power load may decrease to about 13-15 MW with substantial Ne seeding for both pulses, as from numerical predictions, there are indications suggesting that the control of the power load may be more critical for the pulse with pellet injection, due to the reduced SOL radiation

Self-consistent pedestal prediction for JET-ILW in preparation of the DT campaign

The self-consistent core-pedestal prediction model of a combination of EPED1 type pedestal prediction and a simple stiff core transport model is able to predict Type I ELMy (edge localized mode) pedestals of a large JET-ILW (ITER-like wall) database at the similar accuracy as is obtained when the experimental global plasma beta is used as input. The neutral penetration model [R. J. Groebner et al., Phys. Plasmas 9, 2134 (2002)] with corrections that take into account variations due to gas fueling and plasma triangularity is able to predict the pedestal density with an average error of 15%. The prediction of the pedestal pressure in hydrogen plasma that has higher core heat diffusivity compared to a deuterium plasma with similar heating and fueling agrees with the experiment when the isotope effect on the stability, the increased diffusivity, and outward radial shift of the pedestal are included in the prediction. However, the neutral penetration model that successfully predicts the deuterium pedestal densities fails to predict the isotope effect on the pedestal density in hydrogen plasmas

Deep neural networks for plasma tomography with applications to JET and COMPASS

Convolutional neural networks (CNNs) have found applications in many image processing tasks, such as feature extraction, image classification, and object recognition. It has also been shown that the inverse of CNNs, so-called deconvolutional neural networks, can be used for inverse problems such as plasma tomography. In essence, plasma tomography consists in reconstructing the 2D plasma profile on a poloidal cross-section of a fusion device, based on line-integrated measurements from multiple radiation detectors. Since the reconstruction process is computationally intensive, a deconvolutional neural network trained to produce the same results will yield a significant computational speedup, at the expense of a small error which can be assessed using different metrics. In this work, we discuss the design principles behind such networks, including the use of multiple layers, how they can be stacked, and how their dimensions can be tuned according to the number of detectors and the desired tomographic resolution for a given fusion device. We describe the application of such networks at JET and COMPASS, where at JET we use the bolometer system, and at COMPASS we use the soft X-ray diagnostic based on photodiode arrays