Wendelstein 7-X near real-time image diagnostic system for plasma facing components protection

Transcription

1 Wendelstein 7-X near real-time image diagnostic system for plasma facing components protection A. Puig Sitjes1, M. Jakubowski1, P. Drewelow1, A. Ali1, F. Pisano2, B. Cannas2, V. Moncada3, T.T. Ngo4, J.M. Travere4, G. Kocsis5, T. Szepesi5 and W7-X Team1 1 Max Planck Institute for Plasma Physics, Wendelsteinstrasse 1, Greifswald, Germany. 2 University of Cagliari, Piazza d Armi, Cagliari, Italy. 3 ThemaDIAG, 100 Impasse des Houllières, ZA Le Pontet, F Meyreuil, France. 4 CEA, IRFM, F Saint Paul-lez-Durance, France. 5 Wigner RCP, RMI, Konkoly Thege 29-33, H-1121 Budapest, Hungary IAEA 2 nd Technical Meeting on Fusion Data Processing, Validation and Analysis

2 Outline W7-X imaging system PFC operational limits System overview Optical elements and cameras Software architecture Software functions Geometric camera calibration Thermal camera calibration Hot spot detection and classification Generation of thermal events Open challenges 1

3 W7-X imaging system for PFC protection 3D helically shaped stellarator 5 symmetric modules with 2 divertors each Imaging system for PFC protection: 10 IR cameras with frontal view of divertors 10 Video cameras with tangential view (fast particle losses) OP 1.2 ( ): inertially cooled test divertor 9 immersion tubes (1x IR camera) 1 endoscope (1x IR camera) 10 EDICAM video cameras OP 2 (2020): High heat flux water-cooled divertor 10 endoscopes (2x IR cameras) 10 EDICAM video cameras 2

4 PFC operational limits PFC Material Power load Max temp. OP1.2 Test divertor Graphite 10MW/m ºC Divertor tile Max 1200ºC OP2 HHF divertor CFC 10MW/m ºC Scraper element Graphite 8MW/m ºC Max 475 ºC Baffles Graphite 0.5 MW/m ºC Heat shield Graphite 350 kw/m ºC Wall panels Stainless steel 100 kw/m ºC Time requirements: Surface temperature of 1200 ºC 3s before Cu interlayer reaches 450 ºC The interlock system is required to react before 1s after hot spot detection The system needs to detect the hot spot, evaluate its risk and send an alarm within ms 3

5 Endoscopes Designed for high-heat flux steadystate operation Off-axis Cassegrain optical system with a pinhole aperture Field of View: 115º horizontal (-75º, +40º) 60º vertical (- 40º, + 20º) Optical resolution: 6 mm/pixel (40% MTF) Courtesy of M. Jakubowski & D. Chauvin IR Camera (InSb): Infratec 9300 Spectral range: μm (MWIR) Sensor size: 1280x Pixel size: 15 x 15 μm Bit depth: 14 bit Frame rate: 100 Hz 30 5

6 EDICAMs video cameras Courtesy of Wigner Institute Fast intelligent video cameras Field of View: 30º horizontal 24º vertical Optical resolution: 2mm Spectral range: nm CMOS sensor LUPA x 1024 Pixel size: 14 x 14 μm Bit depth: 12 bit Frame rate: M pixels pixels Non-destructive read-out Onboard FPGA processing Multiple dynamic ROI 6

7 System overview Visualization and control Thermal map + Event database Visualization and control Videostream + events Videostream + events Network Network Network x10 IR acquisition & analysis events Video acquition & analysis x10 IR camera Frame grabber Local storage Archive Local storage IPCU EDICAM IO board GPU IO board Fast Interlock System Trigger system 7

8 Open source imaging framework 8

9 Software architecture Acquisition and analysis Display Acquisition Triggers Processing Storage GUI GUI Camera driver ThermaVIP SDK IO board driver CUDA Qt ThermaVIP SDK Qt Operating System OS Camera IO board GPU Local storage Network Network Arquitecture Server-client topology Based on ThermaVIP SDK: a C++ open source framework Developed at CEA and ThermaDIAG Plugin modules to acquire, analyse and display multi-modal data Real-time and offline data analysis Based on QT library which provides portability to several OS 9

10 Multi-threaded processing pipeline Acquisition and analysis x10 Remote display & control Status server Status client Status Display Camera acquisition Raw video saver Bad pixel + NUC Temperature calculation Display Video analysis Video server Metadata server Output alarms Metadata client Video client Display Camera control Input triggers Thermal video saver Control server Control client Trigger System Fast Interlock System 10

11 Acquisition workstations Acquisition and analysis functions: Acquisition of thermographic data Temporal local data storage Image analysis on GPU: Hot spots detection and classification Strike-line detection Thermal event detection Alarms sent to the Fast Interlock System 11

12 Central workstation Visualization Video streams 9-cell grid: 25Hz 1 full screen: 1280x1024 (Infratec) or 1024x768 25Hz Thermal events (JSON structure) Overlaid on the video stream Time evolution Thermal map Offline replay: Video streams (local archiving) Thermal events (local database) Control: Remote control of 10 cameras Overnight archiving: HDF5 video stream (raw + full res. 50Hz) Thermal events 12

13 Courtesy of F. Pisano Scene model We can expect hot spots everywhere in the field of view Segmentation of the full field of view (instead of ROI-based) Pixel-wise correspondance with CAD Emissivity ε of the target material Target distance D Angle α with normal to target surface 3D world coordinates: X,Y,Z, ϕ, θ LoS ϕ, θ X,Y,Z α D ε PFC ID CAD view Multi-dimensional HDF5 image 13

14 Geometric calibration Courtesy of F. Pisano Pinhole camera model: P calibration matrix K intrinsic parameters R rotation matrix t translation vector Lens distortion model: radial (barrel) ìï ˆx = c x + (x - c x )(k 1 r 2 + k 2 r ) í îï ŷ = c y + (y - c y )(k 1 r 2 + k 2 r ) r 2 = (x - c x ) 2 + (y - c y ) 2 Zhang checkerboard camera calibration method Lens distortion model 14

15 Non-Uniformity Correction Courtesy of P. Drewelow & A. Ali Two point NUC: hot and cold image G r : Gain image of relative gains respect reference pixel r O r : Offset image of relatives offsets respect reference pixel r Apply NUC: ~ I xy I xy O G r xy r xy We only need to calibrate the sensor response in the reference pixel The sensor response is modelled and a LUT is created for a given emissivity and exp. time ì ï í ï î r G xy r O xy = I hot cold xy - I xy I hot cold r - I r = I hot xy -G r hot xy I r 15

16 Real-time temperature calculation Courtesy of P. Drewelow Raw image Scene model LUT map y ε XY LUT mapper LUT XY x I XY T xy = LUT xy [I xy ] T XY Thermal image LUTXY t exp Fast real-time processing (two indexing steps operation) S (DL) T (ºC)

17 Hot spot detection Courtesy of A. Ali & A. Rodatos Real-time (20ms) with GPU implementation (tested at GLADIS experiment) Temperature Detection Classification Clustering Temperature calculation Hot spot detection Hot spot classification Region clustering Risk analysis Event generation Pixel-wise temperature thresholding 17

18 Hot spot detection Courtesy of A. Ali & A. Rodatos Real-time (20ms) with GPU implementation (tested at GLADIS experiment) Temperature Detection Classification Clustering Temperature calculation Hot spot detection Hot spot classification Region clustering Risk analysis Event generation Heuristic decision tree: Power overload Surface layer Delamination 18

19 GLADIS experiment Courtesy of A. Ali & A. Rodatos ( t) T( t) T dt( t) / dt coolant Surface temperature Surface layer detection 19

20 GLADIS experiment Courtesy of A. Ali & A. Rodatos ( t) T( t) T dt( t) / dt coolant Surface temperature Delamination detection 20

21 Hot spot detection Courtesy of A. Ali & A. Rodatos Real-time (20ms) with GPU implementation (tested at GLADIS experiment) Temperature Detection Classification Clustering Temperature calculation Hot spot detection Hot spot classification Region clustering Risk analysis Event generation Evaluate size, mean and max temperature 21

22 Hot spot detection Courtesy of A. Ali & A. Rodatos Real-time (20ms) with GPU implementation (tested at GLADIS experiment) Temperature Detection Classification Clustering Temperature calculation Hot spot detection Hot spot classification Region clustering Risk analysis Event generation Scene model 22

23 Thermal events Thermal events SQL database Courtesy of T.T. Ngo Thermal Map Courtesy of F. Pisano 23

24 Open challenges towards steady-state operation Hot spots: LSTM classification Tracking algorithms Emissivity changes Reflexion model Optimal decision-making Safety vs performance Real-time (ms) Steady-state (30 min) Plasma control Interlock system Deep reinforcement learning? How to train it? Bayesian network Probabilistic approach Risk analysis Utility function Slow learning process Bayesian inference Data Acquisition Data analysis 24

25 Summary W7-X is an optimized stellarator with a 3D helical shape Aimed to prove steady-state operation with plasmas up to 30 min Protection of water-cooled PFCs is required A real-time imaging safety system has been developed based on 10 IR cameras in endoscopes / immersion tubes monitoring divertors 10 EDICAM video cameras monitoring the walls The system: Acquires and analyses the video streams by means of GPU-based computer vision techniques Detects: hot spots, surface layers, delaminations, strike-line and other thermal events in real-time Sends alarms to the Fast Interlock System Displays the metadata in the control room (thermal map) Future work: OP1.2 ( ): the system will be tested with inertially cooled divertor OP2 (2020): ready for HHF steady-state operation with water-cooled divertor 25

26 Thank you 26