Realtime Data Fusion for Nuclear Fusion


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1 Realtime Data Fusion for Nuclear Fusion Chris Rapson R. Fischer, L. Giannone, M. Reich, W. Treutterer & the ASDEX Upgrade Team MPI for Plasma Physics, Garching ASDEX Upgrade This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 8 under grant agreement No The views and opinions epressed herein do not necessarily reflect those of the European Commission.
2 ydata fusion in daily life ASDEX Upgrade Principle: combine independent measurements to improve parameter estimate hear a bang, turn to look Realtime Data Fusion for Nuclear Fusion /5
3 ydata fusion in daily life ASDEX Upgrade Principle: combine independent measurements to improve parameter estimate hear a bang, turn to look position estimation phones: GPS, WiFi, Accelerometer cars: as above, plus wheel encoders, LIDAR, Sonar, cameras, IR... [ Realtime Data Fusion for Nuclear Fusion /5
4 ydata fusion in daily life ASDEX Upgrade Principle: combine independent measurements to improve parameter estimate hear a bang, turn to look position estimation phones: GPS, WiFi, Accelerometer cars: as above, plus wheel encoders, LIDAR, Sonar, cameras, IR... [ sports refereeing player statistics entertainment [ Realtime Data Fusion for Nuclear Fusion /5
5 ydata fusion in theory A large body of work, applied to many different fields combine two independent measurements E( obs, = σ E( obs σ, = σ σ + σ + σ + σ E( obs σ + σ P( obs P( obs Realtime Data Fusion for Nuclear Fusion 3/5
6 ydata fusion in theory A large body of work, applied to many different fields combine two independent measurements E( obs, = σ E( obs σ, = σ σ + σ + σ + σ E( obs σ + σ P( obs P( obs P( obs, Realtime Data Fusion for Nuclear Fusion 3/5
7 ydata fusion in theory A large body of work, applied to many different fields combine two independent measurements E( obs, = σ E( obs σ, = σ σ + σ + σ + σ E( obs σ + σ P( obs P( obs P( obs, identify and correct systematic errors & drifts if E( obs E( obs, > large error { } fi(obs line integrated density [ 9 m 3 ] 8 #987 DCN H DCN H Thomson time [s] Realtime Data Fusion for Nuclear Fusion 3/5
8 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π obs t,t obs t obs t P( t obs t Realtime Data Fusion for Nuclear Fusion /5
9 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( initial estimate particles true value Realtime Data Fusion for Nuclear Fusion /5
10 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( measured, t= Realtime Data Fusion for Nuclear Fusion /5
11 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( resampled, t= Realtime Data Fusion for Nuclear Fusion /5
12 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( evolved, t= Realtime Data Fusion for Nuclear Fusion /5
13 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( measured, t= Realtime Data Fusion for Nuclear Fusion /5
14 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( resampled, t= Realtime Data Fusion for Nuclear Fusion /5
15 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( evolved, t= Realtime Data Fusion for Nuclear Fusion /5
16 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( measured, t= Realtime Data Fusion for Nuclear Fusion /5
17 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( resampled, t= Realtime Data Fusion for Nuclear Fusion /5
18 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( evolved, t= Realtime Data Fusion for Nuclear Fusion /5
19 ydata fusion in theory We can also include our prior knowledge of the system dynamics Kalman Filter optimal for linear systems with Gaussian noise developed in the 6s flew on the Apollo missions Etended Kalman Filter local linearisation of nonlinear systems with Gaussian noise Particle Filter fully nonlinear system and noise e.g. linear model ( = +.5 with bimodal measurement (aliasing, mirroring or modulo π P( t obs t obs t obs t obs t,t P( measured, t= Realtime Data Fusion for Nuclear Fusion /5
20 yeisting applications of data fusion in tokamaks Lots of potential because one parameter can be measured in different ways different parameters are linked by common geometry Offline Integrated Data Analysis [Fischer, this conference] Minerva [Svensson 8 PPCF, von Nessi PoP] L/Hmode identification [e.g. Giannone PPCF] Z eff [Verdoolaege IEEE] Realtime [Fischer F Sci Tech, ] RAPTOR [Felici, this conference] disruption prediction [several contributors, this conference] MHD mode number tracking [e.g. Galperti PPCF, Alves 3 PPCF] Realtime Data Fusion for Nuclear Fusion 5/5
21 yntm Stabilisation NTMs decrease confinement and can cause disruptions stabilise or prevent NTMs with localised heating and current drive ECCD Realtime Data Fusion for Nuclear Fusion 6/5
22 yntm Stabilisation NTMs decrease confinement and can cause disruptions stabilise or prevent NTMs with localised heating and current drive ECCD requires accurate targetting: accuracy < w island in AUG, accuracy < cm in ITER, accuracy < 3 cm [Sauter PPCF, La Haye 8 NF] more power required if ECCD deposition is less accurate growth rate dw/dt [ms ]. w marg ECCD off ECCD on w sat. 6 w [cm] NTM w island Realtime Data Fusion for Nuclear Fusion 6/5
23 yntm Stabilisation NTMs decrease confinement and can cause disruptions stabilise or prevent NTMs with localised heating and current drive ECCD requires accurate targetting: accuracy < w island in AUG, accuracy < cm in ITER, accuracy < 3 cm [Sauter PPCF, La Haye 8 NF] more power required if ECCD deposition is less accurate in terms of mirror angle <.. ITER mirrors specified to ±.5 requires accurate localisation growth rate dw/dt [ms ]. w marg ECCD off ECCD on w sat. 6 w [cm] NTM w island Realtime Data Fusion for Nuclear Fusion 6/5
24 yntm Localisation  Method : Equilibrium NTMs only occur at flu surfaces with rational q location of q surfaces can be obtained from equilibrium equilibrium is a prerequisite for EC raytracing calculations + always available, even before NTM appears difficult to diagnose to q profile large uncertainty of flu surface location Realtime Data Fusion for Nuclear Fusion 7/5
25 ASDEX Upgrade yntm Localisation  Method : Te Fluctuations NTM detected as B by Mirnov coils and T e by ECE inversion radius can be associated with an ECE channel (change of correlation phase by π [Reich et al. F Sci Tech. ] ECE channel location is known at the midplane + + low uncertainty in ECE channel identification uncertainty improves for big NTMs some false positives midplane location must be transposed to deposition location reintroduces uncertainty only useful once island eists Realtime Data Fusion for Nuclear Fusion ECRH ECE 8/5
26 yntm Localisation  Method 3: Amplitude ASDEX Upgrade decreases when EC is well aligned + no spatial resolution limitation ρq=.5.6 ρeccd.5 ρtarget. 6 NTM width [au] NTM amplitude is B island width ρ [no units] #967 ep sim time [s] B is a noisy measurement search for min B is nonlinear difficult for control amplitude changes can be due to adding or removing NBI power only useful if EC approimately on target only useful once island eists [Humphreys et al. PoP. 6] Realtime Data Fusion for Nuclear Fusion 9/5
27 Equilibrium σ =. ysensor Functions P(NTM pos ρ pol.6.8 T e Fluctuations σ =. weighted by the NTM amplitude Ḃ P(NTM pos ρ pol Amplitude σ =. negative if amplitude increases weighted by + dβ dt one peak for each gyrotron which is within. ρ pol of the qsurface P(NTM pos.. ρ pol.6.8 Realtime Data Fusion for Nuclear Fusion /5
28 ydata Fusion for NTM Localisation use a gridbased filter  like a particle filter without resampling avoids sample impoverishment since NTM can occur anywhere, anytime more computation since more grid points needed for same resolution maintain likelihood estimate for each sensor separately, then add Realtime Data Fusion for Nuclear Fusion /5
29 ydata Fusion for NTM Localisation use a gridbased filter  like a particle filter without resampling avoids sample impoverishment since NTM can occur anywhere, anytime more computation since more grid points needed for same resolution maintain likelihood estimate for each sensor separately, then add instead of normalising every timestep, use a forgetting factor τ forget (mag = τ forget (ECE =. s τ forget (ampl =.7 s time [s] time [s] Realtime Data Fusion for Nuclear Fusion /5
30 ydata Fusion for NTM Localisation use a gridbased filter  like a particle filter without resampling avoids sample impoverishment since NTM can occur anywhere, anytime more computation since more grid points needed for same resolution maintain likelihood estimate for each sensor separately, then add instead of normalising every timestep, use a forgetting factor τ forget (mag = τ forget (ECE =. s τ forget (ampl =.7 s compensate the cumulative effects of a long τ forget with weights w(mag = w(ece = w(ampl = time [s] time [s] Realtime Data Fusion for Nuclear Fusion /5
31 yapply Fused Data to Feedback #357  test archived data ρ pol.8.6 mag estimate data fus estimate w [cm] ρ ρ NTM pol pol ECE estimate data fus estimate data fus estimate EC 6 time [s] 5 5 Realtime Data Fusion for Nuclear Fusion /5
32 yapply Fused Data to Feedback tracks mag estimate before NTM appears #357  test archived data ρ pol.8.6 mag estimate data fus estimate w [cm] ρ ρ NTM pol pol ECE estimate data fus estimate data fus estimate EC 6 time [s] 5 5 Realtime Data Fusion for Nuclear Fusion /5
33 yapply Fused Data to Feedback tracks mag estimate before NTM appears tracks ECE estimate when island is large and EC off #357  test archived data ρ pol ρ pol ECE estimate data fus estimate mag estimate data fus estimate w [cm] ρ NTM pol data fus estimate EC 6 time [s] 5 5 Realtime Data Fusion for Nuclear Fusion /5
34 yapply Fused Data to Feedback tracks mag estimate before NTM appears tracks ECE estimate when island is large and EC off ampl changes show if NTM is at the EC deposition location  or not! #357  test archived data ρ pol ρ pol ECE estimate data fus estimate mag estimate data fus estimate w [cm] ρ NTM pol data fus estimate EC 6 time [s] 5 5 Realtime Data Fusion for Nuclear Fusion /5
35 yapply Fused Data to Feedback tracks mag estimate before NTM appears tracks ECE estimate when island is large and EC off ampl changes show if NTM is at the EC deposition location  or not! result is better than any of the diagnostics by themselves #357  test archived data ρ pol ρ pol ρ pol w NTM [cm] ECE estimate data fus estimate mag estimate data fus estimate data fus estimate EC 6 time [s] 5 5 Realtime Data Fusion for Nuclear Fusion /5
36 yapply Fused Data to Feedback tracks mag estimate before NTM appears tracks ECE estimate when island is large and EC off ampl changes show if NTM is at the EC deposition location  or not! result is better than any of the diagnostics by themselves in FB, sweep target around ma likelihood, down to 9% ρ pol ρ pol ρ pol w NTM [cm] #357  FB simulation NTM mag estimate data fus estimate NTM ECE estimate data fus estimate NTM data fus estimate target EC.5 3 time [s] Realtime Data Fusion for Nuclear Fusion /5
37 yapply Fused Data to Feedback tracks mag estimate before NTM appears tracks ECE estimate when island is large and EC off ampl changes show if NTM is at the EC deposition location  or not! result is better than any of the diagnostics by themselves in FB, sweep target around ma likelihood, down to 9% realtime C ++ version ready to run ρ pol ρ pol ρ pol w NTM [cm] #357  FB simulation NTM mag estimate data fus estimate NTM ECE estimate data fus estimate NTM data fus estimate target EC.5 3 time [s] Realtime Data Fusion for Nuclear Fusion /5
38 #365 s Equilibria: EQI( Diagnostics: ECE Radiometer ECE Radiometer (6 yalternative Concepts for Position Control magnetic diagnostics susceptible to drift in long pulse eperiments radiation damage in reactors need alternatives for DEMO the more options the better [Santos NF ] Reflectometry measures n e profiles and can estimate gaps ECE can estimate the separatri position where T e ev take care with shinethrough Soft XRay can estimate position of ma radiation as proy for z centre Trad [ev] 3 R aus.6.8. R [m] [SXR images courtesy of M. Weiland, to be published] Realtime Data Fusion for Nuclear Fusion 3/5
39 ydata Fusion for Position Control P(R P(R P(R P(R P(R AUG mag refl total ECE model R [m] Realtime Data Fusion for Nuclear Fusion /5
40 ydata Fusion for Position Control P(R P(R P(R P(R P(R AUG mag refl total ECE model R [m] Realtime Data Fusion for Nuclear Fusion /5
41 ydata Fusion for Position Control P(R AUG mag P(R DEMO mag (slow P(R refl P(R refl P(R ECE P(R ECE P(R model P(R model P(R total P(R total R [m] R [m] Realtime Data Fusion for Nuclear Fusion /5
42 yconclusions Data fusion in nuclear fusion mature methods eist potential to eploit these techniques in fusion eperiments  more than we have so far will be vital for diagnostics in a reactor Two eamples shown NTM localisation combining equilibrium, ECE and amplitude performs well in FB simulations ready to test in eperiment plasma position estimation concept involving many diagnostics Realtime Data Fusion for Nuclear Fusion 5/5
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