Gaussian process tomography (GPT) is a recently developed tomography method based on the Bayesian probability theory [J. Svensson, JET Internal Report EFDA-JET-PR(11)24, 2011 and Li et al., Rev. Sci. Instrum. 84, 083506 (2013)]. By modeling the soft X-ray (SXR) emissivity field in a poloidal cross section as a Gaussian process, the Bayesian SXR tomography can be carried out in a robust and extremely fast way. Owing to the short execution time of the algorithm, GPT is an important candidate for providing real-time reconstructions with a view to impurity transport and fast magnetohydrodynamic control. In addition, the Bayesian formalism allows quantifying uncertainty on the inferred parameters. In this paper, the GPT technique is validated using a synthetic data set expected from the WEST tokamak, and the results are shown of its application to the reconstruction of SXR emissivity profiles measured on Tore Supra. The method is compared with the standard algorithm based on minimization of the Fisher information.

Gaussian process tomography (GPT) [J. Svensson, JET Internal Report EFDA-JET-PR(11)24, 2011 and D. Li, J. Svensson, H. Thomsen, F. Medina, A. Werner, and R. Wolf, Rev. Sci. Instrum. 84, 083506 (2013)] is a recently developed tomography method applied earlier to soft X-ray (SXR) spectroscopy on WEST—Tungsten (W) Environment in Steady-state Tokamak. The short execution time of the algorithm makes GPT an important candidate for providing real-time information on impurity transport and for fast MHD control. In earlier work, GPT has shown its flexibility by providing good reconstruction results without background information about the magnetic equilibrium. On the other hand, information about the magnetic flux surface geometry can in general be useful for additional regularization of the solution. In this paper, we develop a way to take into account the equilibrium information, by constructing a covariance matrix of the prior Gaussian process depending on the flux surface geometry. The GPT method is validated using synthetic SXR emissivity profiles relevant to WEST plasmas and compares favorably with the classical algorithm based on minimization of the Fisher information.

The supersonic molecular beam injection (SMBI) technique is widely used in magnetic fusion devices for plasma fueling and active control of particles. The beam flow formed by the SMBI system is directly visualized by a newly established schlieren diagnostic system on the SMBI testing platform. This schlieren system could provide a 2D density distribution of the beam with the sub-mm spatial resolution by detecting the change in the refractive index of the medium caused by the SMBI in the low-pressure vacuum chamber. The structure and the divergence angle of the supersonic molecular beam for fusion plasma fueling are first measured. Additionally, the density profile of the supersonic molecular beam is reconstructed from the measured light intensity by the schlieren system. The experimental comparison between the standard nozzle and the optimized nozzle of the SMBI system shows the great potential of the diagnostic system in optimizing the beam characteristics.

Mitigation of long-lived mode (LLM) by lower hybrid wave (LHW) is recently achieved on the HL-2A tokamak. The LLM changes from a typical steady-state to a fishbone-like frequency-chirping characteristic, and its higher poloidal harmonics disappear when LHW is injected into toroidal plasma. It is found that density fluctuation declines during this process while the total neutron count increases gradually. Those evidences indicate there is a mitigation effect of LHW on LLM, and the underlying mechanism can be explained as follows. On one hand, high power LHW firstly causes a drop in toroidal rotation and then results in decline of E × B shear, which is unfavorable for the maintenance of the internal transport barrier. The resulting relaxation of ion temperature gradient enhances thermal transport and leads to a drop of ion temperature at the core region. Thus, the plasma pressure becomes flattened and finally contributes to the mitigation of highly saturated internal mode. On the other hand, hybrid simulation by M3D-K suggests that the off-axis LHW can also reduce the grower rate of LLM via changing safety factor and magnetic shear.

An advanced plasma current tomography method is established for the Experimental Advanced Superconducting Tokamak (EAST), which combines Bayesian probability theory and neural networks. It is different from the existing current tomography method based on a conditional autoregressive (CAR) prior. Specifically, the CAR prior is replaced with an advanced squared exponential (ASE) kernel function prior. Therefore, the proposed method can overcome the deficiencies of the CAR prior, where the calculated core current is lower than the reference current and the uncertainty becomes severe after introducing noise in the diagnostics. The ASE kernel prior is developed from the squared exponential kernel function by integrating the useful information from the reference discharge. The ASE kernel prior adopts nonstationary hyperparameters and introduces the current profile into the hyperparameters, which can make the shape of the current profile more flexible in space. To provide a suitable reference discharge, a neural network model is also trained. The execution time is less than 1 ms for each time slice, which indicates its potential for application in future real-time plasma feedback control.