The precise plasma boundary gap identification at the midplane is a prerequisite for achieving controlled plasma positioning and holds a significant importance for the stable operation of tokamak devices. This study proposes a plasma boundary gap at the midplane recognition algorithm based on visual endoscopy diagnostic. The model is an end-to-end one that uses a convolutional neural network that does not require manual data labeling. The model performance is improved by experimentally comparing different convolutional layers and input image sizes. The model is validated using a testing dataset comprising 400 plasma discharge moments. The model has average errors of 3.7 and 4 mm for gap-in and -out, respectively, when compared to those obtained by equilibrium fitting. The proposed approach offers a convenient and effective means of obtaining the boundary gap value and is particularly suited for future fusion experimental devices, such as BEST and ITER tokamak.
Simulations of tokamak edge plasma turbulent fluctuations based on a minimal 3D model