Abstract
A new pseudolocal tomography algorithm is developed for soft X-ray(SXR) imaging measurements of the turbulent electron temperature fluctuations (δ Te) in tokamaks and stellarators. The algorithm overcomes the constraints of limited viewing ports on the vessel wall (viewing angle) and limited number of lines of sight (LOS). This is accomplished by increasing the number of LOS locally in a region of interest. Numerical modeling demonstrates that the wavenumber spectrum of the turbulence can be reliably reconstructed, with an acceptable number of viewing angles and LOS and suitable low SNR detectors. We conclude that a SXR imaging diagnostic for measurements of turbulent δ Te using a pseudolocal reconstruction algorithm is feasible.
Highlights
Better understanding of turbulence-driven transport is vital to improving plasma confinement in tokamaks and requires measurements of turbulence-related quantities
A new pseudolocal tomography algorithm is developed for soft X-ray(SXR) imaging measurements of the turbulent electron temperature fluctuations (δ Te) in tokamaks and stellarators
We conclude that a soft x-ray imaging (SXR) imaging diagnostic for measurements of turbulent δ Te using a pseudolocal reconstruction algorithm is feasible
Summary
Better understanding of turbulence-driven transport is vital to improving plasma confinement in tokamaks and requires measurements of turbulence-related quantities. A pseudolocal tomography algorithm, a concept that has not been applied in fusion research before, is used to reconstruct local δTe from the measurements of line-integrated soft x-ray emissivity. Soft x-ray imaging is a well-known diagnostic in magnetic fusion research, commonly used for two applications: (1) measuring electron temperature and (2) tomographically reconstructing the dynamic 2D emissivity profiles to study MHD phenomena.. A number of tomographic reconstruction algorithms have been developed in fusion, including Cormack method, maximum entropy, linear regularization, minimum Fisher information, Gaussian process tomography, and deep learning.. A number of tomographic reconstruction algorithms have been developed in fusion, including Cormack method, maximum entropy, linear regularization, minimum Fisher information, Gaussian process tomography, and deep learning.13 These algorithms successfully overcome the limitation of sparse measurements in the. In this work, we assume high temporal resolution and focus on how to improve the spatial resolution subject to the constraints of the limited number of viewing angles and LOSs
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
