Real-time detection of overloads on the plasma-facing components of Wendelstein 7-X

Wendelstein 7-X (W7-X) is the leading experiment on the path of demonstrating that stellarators are a feasible concept for a future power plant. One of its major goals is to prove quasi-steady-state operation in a reactor-relevant parameter regime. The surveillance and protection of the water-cooled plasma-facing components (PFCs) against overheating is fundamental to guarantee a safe steady-state high-heat-flux operation. The system has to detect thermal events in real-time and timely interrupt operation if it detects a critical event. The fast reaction times required to prevent damage to the device make it imperative to automate fully the image analysis algorithms. During the past operational phases, W7-X was equipped with inertially cooled test divertor units and the system still required manual supervision. With the experience gained, we have designed a new real-time PFC protection system based on image processing techniques. It uses a precise registration of the entire field of view against the CAD model to determine the temperature limits and thermal properties of the different PFCs. Instead of reacting when the temperature limits are breached in certain regions of interest, the system predicts when an overload will occur based on a heat flux estimation, triggering the interlock system in advance to compensate for the system delay. To conclude, we present our research roadmap towards a feedback control system of thermal loads to prevent unnecessary plasma interruptions in long high-performance plasmas.This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2018 and 2019–2020 under grant agreement No 633053.Peer ReviewedArticle signat per 22 autors/es: Aleix Puig Sitjes* 1, Marcin Jakubowski 1, Dirk Naujoks 1, Yu Gao 1, Peter Drewelow 1, Holger Niemann 1, Joris Fellinger 1, Victor Moncada 2, Fabio Pisano 3, Chakib Belafdil 2, Raphael Mitteau 2, Marie-Hélène Aumeunier 2, Barbara Cannas 3, Josep Ramon Casas 4, Philippe Salembier 4, Rocco Clemente 4, Simon Fischer 1, Axel Winter 1, Heike Laqua 1, Torsten Bluhm 1, Karsten Brandt 1, and The W7-X Team † 1. Max-Planck-Institut für Plasmaphysik, Wendelsteinstr. 1, 17491 Greifswald, Germany / 2. Commissariat à l’Énergie Atomique et aux Énergies Alternatives (CEA), Institut de Recherche sur la Fusion par Confinement Magnétique (IRFM), F-13108 Saint Paul-lez-Durance, France / 3. Department of Electrical and Electronic Engineering, University of Cagliari (UniCa), Piazza d’Armi, 09126 Cagliari, Italy / 4. Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Jordi Girona 1-3, 08034 Barcelona, Spain / * Author to whom correspondence should be addressed. / † Membership of the Team Name is provided in Acknowledgments.Postprint (published version

Detecció i Classificació d'Events Tèrmics dins el Wendelstein 7-X

This project is the result of a research collaboration with the Institute of Plasma Physics (IPP) of Greifswald (Germany). The work proposed below is to be carried out from the Image Processing Group (GPI) in Barcelona. Wendelstein 7-X is the largest prototype of a fusion reactor of the "Stellarator" family. The first operation phase (OP1) started in 2017, and IPP offers now a large amount of the resulting data (videos, images) for research.The aim of this project is to develop a detector and classifier of thermal events in infrared images captured within the Wendelstein 7-X, an experimental fusion reactor located in Greifswald, Germany. As the number of experiments conducted within the reactor is not sufficiently high, the available dataset is not large, which are essential requirements in order to apply Deep Learning techniques. Hence, the proposed solution is based on traditional image processing and Machine Learning techniques. The approach is split in stages: the first stage consists of a Detection of thermal events by applying the Max-Tree representation of images and optimizing certain parameters extracted from these images and inserted in the tree representation. The second stage consists of a Classification of the thermal events previously detected. Descriptors from the detected regions are extracted and the best classification model is sought for the generated dataset. The reasoning of the proposed solution is fully detailed and justified in this document. This thesis also explores the research on the most suitable descriptors for each stage in order to successfully achieve the desired goals.El objetivo de este proyecto es desarrollar un detector y clasificador de eventos térmicos en imágenes infrarrojas capturadas dentro del Wendelstein 7-X, un reactor de fusión experimental situado en Greifswald, Alemania. Como el número de experimentos realizados dentro del reactor no es suficientemente elevado, el conjunto de datos disponibles no es grande, lo que constituye un requisito esencial para aplicar las técnicas de Deep Learning. Por lo tanto, la solución propuesta se basa en las técnicas tradicionales de procesamiento de imágenes y de Machine Learning. La solución propuesta se divide en etapas: la primera etapa consiste en la detección de eventos térmicos aplicando la representación Max-Tree de las imágenes y optimizando ciertos parámetros extraídos de estas imágenes e insertados en la representación en árbol. La segunda etapa consiste en una clasificación de los eventos térmicos detectados anteriormente. Se extraen los descriptores de las regiones detectadas y se busca el mejor modelo de clasificación para el conjunto de datos generados. El razonamiento de la solución propuesta se detalla y justifica plenamente en este documento. Esta tesis también explora la investigación de los descriptores más adecuados para cada etapa con el fin de alcanzar con éxito los objetivos deseados.L'objectiu d'aquest projecte és desenvolupar un detector i classificador d'esdeveniments tèrmics en imatges infraroges capturades dins del Wendelstein 7-X, un reactor de fusió experimental situat a Greifswald, Alemanya. Com el nombre d'experiments realitzats dins del reactor no és prou elevat, el conjunt de dades disponibles no és gran, la qual cosa constitueix un requisit essencial per a aplicar les tècniques de Deep Learning. Per tant, la solució proposada es basa en les tècniques tradicionals de processament d'imatges i Machine Learning. La solució proposada es divideix en etapes: la primera etapa consisteix en la detecció d'esdeveniments tèrmics aplicant la representació Max-Tree de les imatges i optimitzant uns certs paràmetres extrets d'aquestes imatges i inserits en la representació en arbre. La segona etapa consisteix en una classificació dels esdeveniments tèrmics detectats anteriorment. S'extreuen els descriptors de les regions detectades i es busca el millor model de classificació per al conjunt de dades generades. El raonament de la solució proposada es detalla i justifica plenament en aquest document. Aquesta tesi també explora la recerca dels descriptors més adequats per a cada etapa amb la finalitat d'aconseguir amb èxit els objectius desitjats