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  1. Synthetic diagnostics in the EU-ITM simulation platformR. Coelho[1], S. Äkäslompolo[2], A. Dinklage[3], A. Kus[3], E. Sundén[4], S. Conroy[4] E. Blanco[5], G. Conway[6], S. Hacquin[7], S. Heuraux[7b], C. Lechte[8], F. Silva[1], A. Sirinelli[7] and ITM-TF contributors*[1] Associação EURATOM/IST, Instituto de Plasmas e Fusão Nuclear – Laboratório Associado, Instituto Superior Técnico, Universidade Técnica de Lisboa, P-1049-001 Lisboa, Portugal[2] Aalto University, Euratom-Tekes Association, P.O. Box 14100, FI-00076 AALTO, Finland[3] Max-Planck-Institut für Plasmaphysik, EURATOM-Association, Wendelsteinstr. 1, Greifswald, Germany[4] Uppsala University, VR-Euratom Association, Box 516, 751 20 Uppsala, Sweden[5] Asociación EURATOM-CIEMAT para Fusión, CIEMAT, Madrid, Spain Association[6] Max-Planck-Institut für Plasmaphysik, EURATOM-IPP Association, Garching, Germany[7] CEA, IRFM, F-13108 Saint-Paul-lez-Durance, France.[7b] IJL, UMR CNRS 7198 U. Lorraine Faculty of Sciences BP 70239, F-54506 Cedex, France[8] Institute for Plasma Research, University of Stuttgart, 70569 Stuttgart, Germany 7th Workshop on Fusion Data Processing Validation and Analysis March 26th-28th 2012 7th Workshop on Fusion Data Proc. Validation and Analysis Conference name, data and Presenter

  2. Outline of the talk Rationale for synthetic diagnostics in ITM ITM software environment and selected tools Ongoing efforts in synthetic diagnostics integration in ITM Spectral MSE 3D Reflectometry Neutron Diagnostics Neutral Particle Analyser / Fast Ion Loss Detector Conclusions and perspectives 7th Workshop on Fusion Data Proc. Validation and Analysis

  3. The ITM-TF in short… Courtesy of P.Strand from EFDA SC (03)-21/4.9.2 (June 24th, 2003) 3 7th Workshop on Fusion Data Proc. Validation and Analysis

  4. Outline of the talk Rationale for synthetic diagnostics in ITM ITM software environment and selected tools Ongoing efforts in synthetic diagnostics integration in ITM Spectral MSE 3D Reflectometry Neutron Diagnostics Neutral Particle Analyser / Fast Ion Loss Detector Conclusions and perspectives 7th Workshop on Fusion Data Proc. Validation and Analysis

  5. Rationale for SD in ITM In a nut shell…synthetic diagnostic integration in ITM is needed for : Integrated data analysis : feed the best experimental data in interpretative Tokamak simulations Plasma Control : build diagnostic signals for feedback plasma control emulation. Code Validation : essential when multiscale complex physics is involved, e.g. turbulence (reflectometry, PCI, CECE, BES) 7th Workshop on Fusion Data Proc. Validation and Analysis

  6. Code Validation in ITM Long-term effort with guidelines since ITM-TF inception Qualification: Is the physics description adequate? Plasma Verification: Are the equations implemented and solved for correctly? Qualification Validation Validation: Do we have a reliable and sufficiently accurate description of the plasma? Data Validity Data Validity: Is our measured data a sufficient representation of reality? Computational model Conceptual model Verification Code benchmarking: (C2C) A tool in both V&V and physics exploration P. Strand 7th Workshop on Fusion Data Proc. Validation and Analysis

  7. Plasma Control System : ITER view • Controlling a full tokamak simulation Raw (e.g [V], [A] units) or post-processed (e.g. Te, j, v) data (depends on controller design and processing latency  Diagnostic Division) IMAS requirements towards Plant system integration, O. Sauter, IM Design Team, ITER IM Technology Workshop, Cadarache, France 7th Workshop on Fusion Data Proc. Validation and Analysis

  8. Outline of the talk Rationale for synthetic diagnostics in ITM ITM software environment and selected tools Ongoing efforts in synthetic diagnostics integration in ITM Spectral MSE 3D Reflectometry Neutron Diagnostics Neutral Particle Analyser / Fast Ion Loss Detector Conclusions and perspectives 7th Workshop on Fusion Data Proc. Validation and Analysis

  9. ITM Software Environment 7th Workshop on Fusion Data Proc. Validation and Analysis

  10. Consistent Physical Objects (1) • Dedicated derived types that describe • diagnostic / hardware describing a fusion device • physics elements of multi-scale plasma simulation Bookkeeping Diagnostic Setting (time independent) Time-dependent measurements 7th Workshop on Fusion Data Proc. Validation and Analysis

  11. Consistent Physical Objects (2) • An Ontology of CPOs to cover all system requirements New diagnostic CPOs or revision of CPO Ontology is welcome 7th Workshop on Fusion Data Proc. Validation and Analysis

  12. Workflow Engine – KEPLER (1) kepler-project.org CPOs_in CPO_out • Eq. Reconstruction Actor • The UAL libraries provide CPO awareness to KEPLER Ellaborated from C. Konz / W. Zingmann 7th Workshop on Fusion Data Proc. Validation and Analysis

  13. Workflow Engine – KEPLER (2) • Conceptual Kepler design for a synthetic diagnostic module (e.g. MSE) with Input and Output CPOs 7th Workshop on Fusion Data Proc. Validation and Analysis

  14. Outline of the talk Rationale for synthetic diagnostics in ITM ITM software environment and selected tools Ongoing efforts in synthetic diagnostics integration in ITM Spectral MSE 3D Reflectometry Neutron Diagnostics Neutral Particle Analyser / Fast Ion Loss Detector Conclusions and perspectives 7th Workshop on Fusion Data Proc. Validation and Analysis

  15. Spectral MSE • Forward model for emissivity and radiance spectra (MSE, CX, BS, edge,…) • Focus on MSE (π,σ±) + CX (beam attenuation) Total E in moving frame A. Dinklage et al. FST 59 L - Radiant flux over l.o.s. 7th Workshop on Fusion Data Proc. Validation and Analysis

  16. Emissivity model • Emissivity model follows from collisional radiative model for beam and plasma neutrals Emissivity Detector signal E, E/2 and E/3 beam components Included in the model 7th Workshop on Fusion Data Proc. Validation and Analysis

  17. Spectral MSE - Results Spectral MSE at ASDEX Upgrade and MSE spectrum from #25827 (R. Reimer et al Cont. Plasma Phys. 50) Full integration in ITM is ongoing (module tested, workflow under development, validation on other discharges by end 2012) 7th Workshop on Fusion Data Proc. Validation and Analysis

  18. Outline of the talk Rationale for synthetic diagnostics in ITM ITM software environment and selected tools Ongoing efforts in synthetic diagnostics integration in ITM Spectral MSE 3D Reflectometry Neutron Diagnostics Neutral Particle Analyser / Fast Ion Loss Detector Conclusions and perspectives 7th Workshop on Fusion Data Proc. Validation and Analysis

  19. 3D Reflectometer (1) • 3D kernel is integrated in ITM, reads CPOs (includes turbulence CPO). Testing of dedicating datastructure ongoing this week. • V&V initiates in 2012 (2d codes and exp.data). • Strong effort on 2D benchmarking is ongoing. erc3D workflow 7th Workshop on Fusion Data Proc. Validation and Analysis

  20. 3D Reflectometer (2) top side 7th Workshop on Fusion Data Proc. Validation and Analysis

  21. 3D Reflectometer (3) First results of erc3d 7th Workshop on Fusion Data Proc. Validation and Analysis

  22. Outline of the talk Rationale for synthetic diagnostics in ITM ITM software environment and selected tools Ongoing efforts in synthetic diagnostics integration in ITM Spectral MSE 3D Reflectometry Neutron Diagnostics Neutral Particle Analyser / Fast Ion Loss Detector Conclusions and perspectives 7th Workshop on Fusion Data Proc. Validation and Analysis

  23. Neutron diagnostics (1) • The synthetic neutron diagnostic handles two kinds of diagnostics: • Proportional counters (neutron camera) • Energy resolving neutron spectrometers (TOFOR and MPRu) •  fusiondiag CPO for diagnostic settings and exp. Data • First module : the Directional RElativistic Spectrum Simulator (DRESS) MC code, specifically developed for ITM. • Calculates the energy spectra and source rates of particles created in fusion reactions (alpha particles andneutrons) • Only source particles going in the direction of the detector are considered by DRESS (pick from differential cross section in CM !) •  coreprof or distribution(non-Maxwellian) for diff.cross section and kinematics, equilibrium for ψ(R,Z) mapping 7th Workshop on Fusion Data Proc. Validation and Analysis

  24. Neutron diagnostics (2) 10-9 • Neutrons are transported towards the neutron diagnostic as described by fusiondiag (collimator) • Fraction of solid angle of neutron source distribution seen by detector (LINE21). • Encapsulated in 3D “Voxels” • Each voxel has associated direction to detector (approx. valid for small volume voxels) • Poloidal projection of the fractional solid angles seen by the horizontal neutron camera at JET using the DT settings 7th Workshop on Fusion Data Proc. Validation and Analysis

  25. Neutron diagnostics (3) • Secondary particle interactions from neutron projectile are detected. • The JET camera detectors measure recoil protons’ deposited energies • TOFOR measures the time-of-flight of neutrons. • MPRu measures recoil proton track deviation in a magnetic field. • A Detector Response Function in fusiondiag maps the neutron energy to the actual measurement. Detector response function of the MPRu X – detector cell En – neutron energy Z-axis – counts (monenergetic highlighted) 7th Workshop on Fusion Data Proc. Validation and Analysis

  26. Neutron diagnostics (4) • Synthetic detector measurements Stored in fusiondiag CPO • Example : synthetic measurement of a 14MeV mono-energetic beam impinging on a 14-MeV dedicated time-of-flight spectrometer using two different settings (blue and red for different electronics setup emulation) synthetic counts for a 14-MeV time-of-flight spectrometer 7th Workshop on Fusion Data Proc. Validation and Analysis

  27. Outline of the talk Rationale for synthetic diagnostics in ITM ITM software environment and selected tools Ongoing efforts in synthetic diagnostics integration in ITM Spectral MSE 3D Reflectometry Neutron Diagnostics Neutral Particle Analyser / Fast Ion Loss Detector Conclusions and perspectives 7th Workshop on Fusion Data Proc. Validation and Analysis

  28. NPA diagnostic (1) • Based on the ASCOT kernel and tools. • Test ions that fall on NPA cone of sight and that can reach detector (neutralization and re-ionization included) Distribution of ASCOT “test particles” inside 3D ASDEX Upgrade wall Use : 3dwall, distribution CPOs Cone of sight of NPA detector (red – wall blocked; black – port blocked) Use : 3dwall, fusiondiag CPOs Neutral source from mass-m ion 7th Workshop on Fusion Data Proc. Validation and Analysis

  29. NPA diagnostic (2) • Velocity pitch (V// / VTot) dependence •  unknown Larmor phase lead to “cone of flight” (c.o.f) • Use : equilibrium, distribution CPOs Z=0 in Pol.plane 7th Workshop on Fusion Data Proc. Validation and Analysis

  30. NPA diagnostic (3) • Fraction of c.o.f intersect with collimator effective area •  scale factor on flux count • Use : fusiondiag (collimator) 7th Workshop on Fusion Data Proc. Validation and Analysis

  31. NPA diagnostic (4) • Re-ionization along flight path  scale factor on flux count • Use : coreprof, coreneutrals CPOs Flux Number of mean free paths Calculated neutral flux attenuation due to re-ionization. 7th Workshop on Fusion Data Proc. Validation and Analysis

  32. NPA diagnostic (5) • Final goal  Neutral flux as function of energy • Stored in : fusiondiag CPO Calculated neutral flux as a function of energy  ion distribution source : 60keV NBI pini, Eion>20keV 7th Workshop on Fusion Data Proc. Validation and Analysis

  33. Conclusions and Perspectives ITM-TF is conscious of the relevance of SD and integration work is in progress (Spectral MSE, 3d Reflectometer, NPA and neutron camera) ITM modelling platform : why exp./diag./control community should use it ITM datastructure is flexible : CPO can evolve to fit your needs. Kepler orchestrator : user friendly workflow design, independent of the device. Useful platform for diagnostic and control R&D ITM envisages integration of further SD from community to assist code validation, e.g. 3D cameras, Soft/hard X-rays, PCI, BES. 7th Workshop on Fusion Data Proc. Validation and Analysis

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