First results of the multi-purpose real-time processing video camera system on the Wendelstein 7-X stellarator and implications for future devices

Abstract

A special video camera has been developed for the 10-camera overview video system of the Wendelstein 7-X (W7-X) stellarator considering multiple application needs and limitations resulting from this complex long-pulse superconducting stellarator experiment. The event detection intelligent camera (EDICAM) uses a special 1.3 Mpixel CMOS sensor with non-destructive read capability which enables fast monitoring of smaller Regions of Interest (ROIs) even during long exposures. The camera can perform simple data evaluation algorithms (minimum/maximum, mean comparison to levels) on the ROI data which can dynamically change the readout process and generate output signals. Multiple EDICAM cameras were operated in the first campaign of W7-X and capabilities were explored in the real environment. Data prove that the camera can be used for taking long exposure (10-100 ms) overview images of the plasma while sub-ms monitoring and even multi-camera correlated edge plasma turbulence measurements of smaller areas can be done in parallel. These latter revealed that filamentary turbulence structures extend between neighboring modules of the stellarator. Considerations emerging for future upgrades of this system and similar setups on future long-pulse fusion experiments such as ITER are discussed.

Multi-diagnostic analysis of plasma filaments in the island divertor

“…The W7-X overview camera system [13] consists of 10 tangential views, most of them equipped with EDICAM cameras providing overview images at a typical 100 Hz frame rate and in parallel small regions of interest running at 5-10 kHz. Although signatures of turbulence were observed in these cameras [14], in the present study we use the tangential view (module 2, j≈80 deg) equipped with an image guide (800×1000 individual fibers) attached to a Photron SA-X2 fast framing camera located at port AEQ21. The camera was running at a 30-90 kHz frame rate while viewing the whole plasma.…”

Detecting Plasma Detachment in the Wendelstein 7-X Stellarator Using Machine Learning

“…The so-called Event Detection Intelligent Cameras (EDICAMs) serve as the workhorse for the visible video diagnostics. The EDICAMs use a special 1.3 megapixel CMOS sensor with non-destructive read capability, enabling fast monitoring of smaller, predefined Regions of Interest (ROIs) in parallel to a normal, slow framing (100 Hz), fullframe overview [6]. The camera can perform simple operations on the ROI data in real-time, which can adapt and change the readout process and generate various output signals.…”

“…Machine learning is a powerful tool in the hands of physicists to be able to process and analyze this growing amount of data. As the first step to a (supervised) machine learning toolkit for fusion research, we trained several classical machine learning (ML) algorithms, to be able to detect plasma detachment in the images captured by the EDICAM camera system [5,6] in the Wendelstein 7-X stellarator [7][8][9][10]. One of the major aims of this study was to show that power detachment can be detected using the combination of the installed video diagnostics and intelligent algorithms, and how these algorithms can enhance the capabilities of such diagnostics.…”

A study of turbulent filaments in the edge plasma of the Wendelstein 7-X stellarator

“…They are capable of framerates up to 1 MHz (albeit with strongly reduced ROI). For a detailed schematic of the camera setup at the time, refer to [21].…”