The IAEA Database for Dust Particles status

Transcription

1 The IAEA Database for Dust Particles status N. Endstrasser 1, V. Rohde 1, M. Balden 1, H. Chung 2, B. Braams 2, P. Humrickhouse 3, S.-H. Hong 4 and R. Neu 1 U.v.Toussaint 1,K.Tichmann 1,F.Tauchert 5,M.Rampp 6 1 Max-Planck-Institut für Plasmaphysik, EURATOM Association, Boltzmannstrasse 2, D Garching, Germany. 2 International Atomic Energy Agency, Atomic and Molecular Database Unit, Wagramer Straße 5, A-1220 Wien, Austria. 3 Idaho National Engineering and Environmental Laboratory, Fremont Ave, Idaho Falls, ID , USA. 4 National Fusion Research Institute, Gwahangno 113, Yusung-gu, Daejeon , Korea. 5 University of applied Science, Hof, Germany 6 Rechenzentrum Garching,Boltzmannstrass2, D Garching Meeting on Dust and Arc investigations in ASDEX Upgrade for ITER, 6 th April 2011

2 Need for Database Automated evaluation of dust collectors Some particles measured Statistical relevant database Commercial evaluation program Classification needed for extrapolation Classification needed for modelling frequency (%) mm -2 covered area (%) % of total area Cont Cu Fe B C W fks W sph IPP requirements Particle evaluation must fit scientific standards Documentation of experiment, evaluation Handle some pictures, data files Classification of results Develop Database for dust analysis Wafer no.

3 Potential Benefits from IAEA Database Input from all major fusion and laboratory plasma devices Identification of particle types (classes) Requirement: statistical relevant number of particles Approach: Cross-linking particle characteristics with information on sampling site and plasma conditions Goal: Identification of dust origin, formation and transport modelling ITER: Assessment of impact on operation and safety (design review, counter measures)

4 Data Data source: Microscopy images (SEM, LM, ) Element spectra (EDX, XPS, AES, RBS ) Data nature: particle classes morphological information tables (image analysis) elemental composition tables (spectrum analysis) Analysis tools: ImageJ adapted to IAEA requirements (open source image analyzer) Standard software, here INCA (commercial spectrum analyzer) Dataset completion tools: Match making (Morphological info + Elemental composition) m Images, n Spectra Analysis m+n Rows Matching 1 Dataset

5 Classification Why? Scheme = physics: Production process linked with characteristics Characteristics describe physical and chemical properties Properties define impact on Safety and Operation Class = label for safety risk & plasma performance loss For each class: Production, mobilization and reaction rates Abundance for different first wall configurations Critical plasma conditions instead of weight limits

6 Outer morphology - Shape measures C m 1. Area (sq.µm) 2. Perimeter (µm) 3. ConvexArea (sq.µm) 4. ConvexPerimeter (µm) 5. FeretMax (µm) 6. FeretMin (µm) 7. Equivalent Circle Diameter (µm) 8. ChordMax (µm) 9. ChordMin (µm) 10.MajorAxis (µm) 11.MinorAxis (µm) 12.Length (µm) 13.Breadth (µm)

7 Shape Descriptor Shape descriptors 4 A Circularity 2 P 4 A Roundness Solidity A A C PC Convexity P B Elongation L 2 F max Surface Roughness Cmean P Resolution (px/µm)

8 Classification scheme Image Round Solidity Elong W sph W fks C B Fe Cu Cont Roundness 0.85 < 0.85 B 1 0 C Na 0 0 Mg Al S K 0 0 Ca Ti Fe Ni 0 0 Cu Zn Mo Ag W Au Entry 0 and 1 means zero and > 0 At%. Class in scheme when > 5% of particles at least on 1 sample with similar characteristics.

9 Wafer no. 1 below HS W1

10 Wafer no. 3 & 4 lower PSL W3 W4

11 CRP Output: Dust database Database Design Phases: Conceptual Logical Physical Practical 1. Potential benefits 2. Source and nature of data 3. Database requirements 4. Effort estimation 1. Selection of Data Model 2. Entity definition and Entity Relationship Diagramming App. Server: glasfish : JAVA JAVA server faces 2.0 ImageJ: Opensource Database MySQL 1.Selection of coding techniques 2.Formulation of application standards 3.Selection of DBMS features 4.Proof of Concept 5.Prototyping User Access Privileges 1. Alpha Test Unit Integration Security Backup & Recovery End to End 2. Beta Test Load Stress Performance User Acceptance

12 Database Requirements Input Storage Output Data upload storage download + analysis: 1. Image & Spectra Upload 2. Image & Spectra Analysis and Sample data generation 3. Sample Info input 4. Sample info and Sample data Storage 5. Sample data and Classification scheme selection 6. Definition of new classification scheme 7. Generation and export of classification results IAEA / system operator will fix classification schemes

13 Database Structure

14 Database: Upload After login: select data and pictures for upload

15 Database: ImageJ Select threshold for particle identification in pictures Standardized evaluation of geometrical properties by ImageJ

16 Database: ImageJ Identified particles by ImageJ

17 Database: ImageJ results

18 Database experiment information

19 Database: Filter results

20 Database: Definition of filters

21 Database Status N.Endstrasser left IPP, looking for replacement K.Tichmann works on database,. F.Tauchert: Bachelor work on development of database M.Rampp will implement database at IPP computer centre Today: version runs on notebook on RZG computer open for tests (alpha version) Bachelor thesis F.Tauchert, database available Open via Browser for users / 10 GB storage / user Definition of local classes by users Definition of global classes proven by IAEA

22 Summary & Conclusion Automated SEM/EDX analysis Analysis system allows high sample throughput Up to now >140 mm² Si wafer area analysed Datasets Morphological and chemical characteristics of > particles particles from full-tungsten first wall four experimental campaigns Classification scheme 8 dominant classes identified in AUG: W-, Cu- and Fe- dominated spheroids and flakes, C flakes, B agglomerates, Contaminants Refinement ongoing, database will help Local dust sources disturb toroidally symmetric distribution in Tokamak IPP will support test database, dust classes by users Final dust classes fixed by IAEA, safety impact???