Dealing with artefacts In JET iterative bolometric tomography using masks

Author(s):  
Emmanuele Peluso ◽  
Michela Gelfusa ◽  
Teddy Craciunescu ◽  
Luca Martellucci ◽  
Pasquale Gaudio ◽  
...  

Abstract Bolometric tomography is a widely applied technique to infer important indirect quantities in magnetically confined plasmas, such as the total radiated power. However, being an inverse and ill-posed problem, the tomographic algorithms have to be carefully steered to converge on the most approriate solutions and often specialists have to balance the quality of the obtained reconstructions between the core and the edge of the plasma. Given the topology of the emission and the layout of the diagnostics in practically all devices, the tomographic inversions of bolometry are often affected by artefacts, which can influence derived quantities and specific studies based on the reproduced tomograms, such as power balance studies and benchmarching of gyrokinetic simulations. This article deals with the introduction of a simple, but very efficient methodology. It is based on constraining the solution of the tomographic inversions by using a specific estimate of the initial solution, built with the data from specific combinations of detectors (called ‘masks’). It has been tested with phantoms and with real data, using the Maximum Likelihood approach at JET. Results show how the obtained tomograms improve sensibly both in the core and at the edge of the device when compared with those obtained without the use of masks as initial guess. The correction for the main artefacts can have a significant impact on the interpretation of both the core (electron transport, alpha heating) and the edge physics (detachment , SOL). The method is completely general and can be applied by any iterative algorithm starting from an initial guess for the emission profile to be reconstructed.

2018 ◽  
Vol 63 ◽  
pp. 179-207 ◽  
Author(s):  
Nicolas Bouzat ◽  
Camilla Bressan ◽  
Virginie Grandgirard ◽  
Guillaume Latu ◽  
Michel Mehrenberger

In magnetically confined plasmas used in Tokamak, turbulence is respon-sible for specific transport that limits the performance of this kind of reactors. Gyroki-netic simulations are able to capture ion and electron turbulence that give rise to heat losses, but require also state-of-the-art HPC techniques to handle computation costs. Such simulations are a major tool to establish good operating regime in Tokamak such as ITER, which is currently being built. Some of the key issues to address more re- alistic gyrokinetic simulations are: efficient and robust numerical schemes, accurate geometric description, good parallelization algorithms. The framework of this work is the Semi-Lagrangian setting for solving the gyrokinetic Vlasov equation and the Gy-sela code. In this paper, a new variant for the interpolation method is proposed that can handle the mesh singularity in the poloidal plane at r = 0 (polar system is used for the moment in Gysela). A non-uniform meshing of the poloidal plane is proposed instead of uniform one in order to save memory and computations. The interpolation method, the gyroaverage operator, and the Poisson solver are revised in order to cope with non-uniform meshes. A mapping that establish a bijection from polar coordinates to more realistic plasma shape is used to improve realism. Convergence studies are provided to establish the validity and robustness of our new approach.


2016 ◽  
Vol 198 ◽  
pp. 139-153 ◽  
Author(s):  
Andreas Stegmeir ◽  
David Coster ◽  
Omar Maj ◽  
Klaus Hallatschek ◽  
Karl Lackner

2014 ◽  
Vol 21 (10) ◽  
pp. 102304 ◽  
Author(s):  
B. Nold ◽  
P. Manz ◽  
T. T. Ribeiro ◽  
G. Fuchert ◽  
G. Birkenmeier ◽  
...  

Author(s):  
Risheng Liu

Numerous tasks at the core of statistics, learning, and vision areas are specific cases of ill-posed inverse problems. Recently, learning-based (e.g., deep) iterative methods have been empirically shown to be useful for these problems. Nevertheless, integrating learnable structures into iterations is still a laborious process, which can only be guided by intuitions or empirical insights. Moreover, there is a lack of rigorous analysis of the convergence behaviors of these reimplemented iterations, and thus the significance of such methods is a little bit vague. We move beyond these limits and propose a theoretically guaranteed optimization learning paradigm, a generic and provable paradigm for nonconvex inverse problems, and develop a series of convergent deep models. Our theoretical analysis reveals that the proposed optimization learning paradigm allows us to generate globally convergent trajectories for learning-based iterative methods. Thanks to the superiority of our framework, we achieve state-of-the-art performance on different real applications.


Sign in / Sign up

Export Citation Format

Share Document