Role of fast ion pressure in the isotope effect in JET L-mode plasmas

This paper presents results of JET ITER-like wall L-mode experiments in hydrogen and deuterium (D) plasmas, dedicated to the study of the isotope dependence of ion heat transport by determination of the ion critical gradient and stiffness by varying the ion cyclotron resonance heating power deposition. When no strong role of fast ions in the plasma core is expected, the main difference between the two isotope plasmas is determined by the plasma edge and the core behavior is consistent with a gyro-Bohm scaling. When the heating power (and the fast ion pressure) is increased, in addition to the difference in the edge region, also the plasma core shows substantial changes. The stabilization of ion heat transport by fast ions, clearly visible in D plasmas, appears to be weaker in H plasmas, resulting in a higher ion heat flux in H with apparent anti-gyro-Bohm mass scaling. The difference is found to be caused by the different fast ion pressure between H and D plasmas, related to the heating power settings and to the different fast ion slowing down time, and is completely accounted for in non-linear gyrokinetic simulations. The application of the TGLF quasi-linear model to this set of data is also discussed

Control of the hydrogen:deuterium isotope mixture using pellets in JET

Deuterium pellets are injected into an initially pure hydrogen H-mode plasma in order to control the hydrogen: deuterium (H:D) isotope mixture. The pellets are deposited in the outer 20% of the minor radius, similar to that expected in ITER, creating transiently hollow electron density profiles. A H: D isotope mixture of approximately 45%:55% is obtained in the core with a pellet fuelling throughput of Phi(pel) = 0.045P(aux)/T-e,T-ped similar to previous pellet fuelling experiments in pure deuterium. Evolution of the H: D mix in the core is reproduced using a simple model, although deuterium transport could be higher at the beginning of the pellet train compared with the flat-top phase

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

A new mechanism for increasing density peaking in tokamaks: improvement of the inward particle pinch with edge E x B shearing

Developing successful tokamak operation scenarios, as well as confident extrapolation of present-day knowledge requires a rigorous understanding of plasma turbulence, which largely determines the quality of the confinement. In particular, accurate particle transport predictions are essential due to the strong dependence of fusion power or bootstrap current on the particle density details. Here, gyrokinetic turbulence simulations are performed with physics inputs taken from a JET power scan, for which a relatively weak degradation of energy confinement and a significant density peaking is obtained with increasing input power. This way physics parameters that lead to such increase in the density peaking shall be elucidated. While well-known candidates, such as the collisionality, previously found in other studies are also recovered in this study, it is furthermore found that edge E x B shearing may adopt a crucial role by enhancing the inward pinch. These results may indicate that a plasma with rotational shear could develop a stronger density peaking as compared to a non-rotating one, because its inward convection is increased compared to the outward diffusive particle flux as long as this rotation has a significant on E x B flow shear stabilization. The possibly significant implications for future devices, which will exhibit much less torque compared to present day experiments, are discussed

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

Synthetic diagnostic for the JET scintillator probe lost alpha measurements

A synthetic diagnostic has been developed for the JET lost alpha scintillator probe, based on the ASCOT fast ion orbit following code and the AFSI fusion source code. The synthetic diagnostic models the velocity space distribution of lost fusion products in the scintillator probe. Validation with experimental measurements is presented, where the synthetic diagnostic is shown to predict the gyroradius and pitch angle of lost DD protons and tritons. Additionally, the synthetic diagnostic reproduces relative differences in total loss rates in multiple phases of the discharge, which can be used as a basis for total loss rate predictions

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