Ongoing work towards real-time reflectometry density profile measurements on ASDEX Upgrade

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1 Ongoing work towards real-time reflectometry density profile measurements on ASDEX Upgrade J. Santos, M. Manso, CFN Reflectometry Team and the ASDEX Upgrade Team Centro de Fusão Nuclear Associação EURATOM/IST Instituto Superior Técnico, 46- Lisboa, Portugal Max-Planck Institut für Plasmaphysik EURATOM/IPP Association Garching, Germany Abstract Previous studies performed in ASDEX Upgrade ELMy H-mode regimes (Santos et al. []), ITER reference scenario, have shown that O-mode FM-CW reflectometry should meet the accuracy required on ITER for using reflectometry for position control purposes. This application, however, requires the development of robust unsupervised methods to evaluate the density profiles in real time. To allow a statistical evaluation of the performance of these methods, an extensive H-mode mode density profile database was built using O-mode reflectometry experimental data. An D full wave FDTD code (Hacquin et al. []) was used to generate O-mode raw data adapted to the ASDEX Upgrade reflectometer configuration. To benchmark the robustness of the data processing techniques to the effects of plasma turbulence, several levels of radial density fluctuations (Heuraux et al. []) were applied to this database. Preliminary studies using neural networks for density profile inversion gave promising results and this approach is now being pursued. The adopted method not only proved to be quite robust to density fluctuations but it also intrinsically compensated the lack of density profile data below n e =.6 8 m, essential for the standard Abel inversion procedure. This robustness provides a relief on the requirements and sophistication of the procedures used for group delay evaluation, allowing the use of more real-time friendly signal processing techniques. In this paper are shown ASDEX Upgrade H-mode edge density profiles, reconstructed using an highly optimized real-time data processing code, based on the proposed neural network approach. The implementation strategy of the real-time upgrade to the ASDEX Upgrade O-mode reflectometer is also discussed. Introduction Frequency-modulated continuous wave (FM-CW) O-mode reflectometry is a stand-alone diagnostic technique capable of measuring the radial electron density profile. This technique has been proposed to complement the magnetics in ITER (Bretz et al. [998]) due to its minimum access requirements and its compatibility with the ITER harsh environment. O-mode reflectometry can directly provide the gap information necessary for machine protection. As shown in previous work (Santos et al. []), a device dependent scaling factor can be applied to the line average density ( n e ) to estimate the density at the separatrix (n e sep ). The position of this density layer can then be tracked using reflectometry density profiles. The performance of FM-CW O-mode reflectometry is affected essentially by two factors: the effect of plasma fluctuations, that can significantly modify the probing waves time of flight, and the impossibility of probing the plasma with frequencies below F P 7 GHz ( n e.6 9 m ). To overcome these limitations and simultaneously achieve both plasma position spatial and temporal requirements, a new neural network based profile reconstruction technique was developed (Santos et al. []). The proposed profile reconstruction technique associates the robustness and efficiency of time-frequency Fourier analyses to the non-linear regression capabilities of neural networks. It simultaneously addresses the problems caused by the lack of reflectometry data for the lowest incident frequencies and the effects of plasma turbulence, that cause scattering and refraction of the probing microwaves. Given the good generalization capabilities of neural networks, the technique lends itself to an unsupervised and robust implementation. These two characteristics are of paramount importance in a real-time (RT) plasma control environment. The RT measurement of density profiles for plasma position control purposes is an application that needs to be fully developed and tested in present devices. The particular equatorial location of both ASDEX Upgrade reflectometers allows the estimation of the position of two points of the separatrix. These are simultaneously two of the six points used in ASDEX Upgrade for plasma position control (see figure ). Due to the similarities to two of the four gaps to be

2 .5 4 gd [ns] Probed region NxLxM NN 9 - n [x m ] e 4 5 F [GHz] P { N τ g (f) values evaluated at N fixed probing frequencies IN OUT { M radial positions corresponding to M fixed density layers Figure : The neural network is used to produce a smooth profile from a small discrete set of non initialized perturbed group delay measurements evaluated using simple Fourier analysis (blue dots). monitored in ITER using O-mode reflectometry, and to the achievable ITER relevant plasma geometries and regimes, ASDEX Upgrade is an excellent test-bed for this new reflectometry application. ASDEX Upgrade ITER Z up 5 g 5 g 4 Z(m) R in R out Z(m) g 6 g Z I - Z s,i Z s,o -5 g g Divertor Cassette - - PF coils PF coils R(m) R(m) Figure : In ASDEX Upgrade the line-of-sight of both HFS and LFS reflectometers is close to the plasma column magnetic axis (red arrows). Rin and Rout control points are similar to ITER gaps g and g6 that are to be monitored by the position reflectometers. Assessement of the Neural approach in the position application To train and test the neural networks used to reconstruct the density profiles and to compare the performances of both the neural network based approach and the standard profile reconstruction procedure, an extensive database of complete smooth H-mode density profiles was built using experimental measurements. To the original smooth H-mode density profiles were added simulated density fluctuations produced using a physically motivated model (Heuraux et al. []). Realistic reflectometry raw data signals, corresponding to the perturbed profiles, were generated using a D finite-difference time-domain Maxwell code (Hacquin et al. []) adapted to the ASDEX Upgrade O-mode reflectometer geometry and operating characteristics. The resulting assessement universe is composed of a total of 49 pairs of simulated perturbed raw signals and corresponding target smooth profiles, distributed over the two sides of the machine (HFS and LFS) and five levels of fluctuation amplitude (%, %, 5%, 7% and 9% of the density

3 at the separatrix). The database was built using experimental profiles, typical of the ASDEX Upgrade type I ELMy H-mode regime.these profiles were measured during the various ELMy phases and reflect the asymmetries between the shapes of HFS and LFS profiles. The high number of entries in the database allowed a statistically relevant comparison between methods whose results were first presented in (Santos et al. [6]) and will be the subject of a future in-depth publication. In all the perturbed simulated datasets, the profiles reconstructed using the faster neural approach exhibited better and less biased position error distributions, when compared to the standard group delay initialization+abel inversion procedure.. [a.u.].8.4 #97 H α (inner) time [s] LFS R(%n e ) HFS Δ R Δ R_RMS =. cm magnetic sep. Δ R_RMS =. cm Dist. to magn. Separatrix [cm] time [s] Figure : HFS and LFS positions of density layer % n e (dark blue), after removing measurements made during ELM activity, evaluated using a 4 8 NN for H-mode radial scan shot #97. The proposed neural reconstruction procedure was also successfully tested directly using experimental ASDEX Upgrade reflectometry data. The method produced excellent separatrix position estimates, on par with the best results obtained with a fully supervised standard analysis procedure. In figure is shown the evolution of the position of the % n e density layer. In this steady state H-mode discharge (#97) % n e roughly corresponds to the density found at the magnetic separatrix position using LFS Li-beam profiles. As can be seen, the estimate for the separatrix position (dark blue curves) obtained using this scaling closely matches the magnetic separatrix position (black curves) both at the HFS and LFS ( Δ R cm). The light blue curves represent the position of the % n e density layer obtained including profiles occurring during ELM activity. In this discharge, individual profiles were evaluated every ms and no averaging was performed to obtain the represented position curves.

4 #5 - HFS 766 profiles (before removing profiles on the onset of ELMs) profile / ms (measurement rate) Burst processing: 4 sweeps (K, Ka, Q bands) / burst sweep / 5 μs Input data / profile: x 4 sample raw signals n e [ 9 m - ] Data processing cycle: I. Raw signals FIR filtering and linearizing II. FFT calculations - 4 x 4 = 96 (4 pts.) FFTs III. Profile inversion (4x8x NN) profile / 4 μs ( GHz Intel core duo) time [s] Figure 4: HFS density profiles, after removing profiles on the onset of ELMs, calculated using the RT multithreaded code - shotfile #5. Real-time implementation ASDEX Upgrade has recently entered the commissioning and initial operational phases of its advanced RT control and data acquisition system (Raupp et al. [6], Behler et al. [7], Treuterrer et al. [7]). The ultimate goal of this study is thus to prepare the RT feedback of reflectometry plasma position information in the fastest ASDEX Upgrade RT control cycle (.6 ms, (Raupp et al. [6]), possibly ms (Treuterrer et al. [7])). Achieving such a measurement availability rate would make the reflectometry profile data compatible with the plasma performance feedback control timings. Furthermore, it would allow multiple profile measurements to be made on the timescale of the magnetic feedback control of the plasma vertical and radial position ( ms) and of the plasma shape ( ms) (Raupp et al. [5]). At the time of writting, the proposed neural network approach evolved to an highly optimized multithreaded code that runs efficiently in regular shared memory PCs equipped with modern multi-core processors, such as the Intel Core Duo or AMD Opteron families. During the code optimization, special care was taken in what concerns data handling so that the acquired data fits in the large caches of such processors together with the multithreaded processing code. Figure 4 shows the HFS profiles from ASDEX Upgrade shotfile #5 processed using the optimized code and a 4 8 NN. Each profile in this figure was calculated in < 4 µs using a dual core, GHz Core Duo processor. The calculation of the 96 single precision floating point 4 point FFTs (needed to compute the 4 value NN input group delay vector from the 4 sweep individual bursts) took, as expected, most of the time, i.e. 6%. The remaining time was used to perform the initial FIR filtering and the linearization of the sampled sweeps (4 sweeps bands). The NN profile inversion, with its inherent initialization, took, in comparison, a mere µs. The present AUG broadband reflectometry system was not built with a RT operation in mind. The biggest constraint is its acquisition system which has no on-line data transfer capabilities. To bring this measurements on-line, a new acquisition system has to be built so that the raw data is made available in a powerfull computational platform soon after it is acquired. RT diagnostics entering the operational phase are built with simplified acquisition front ends connected to timing/data collecting boards by serial, high speed, bidirectional optical links (based on Cypress Hot- Link II technology). These boards are connect to the diagnostic host computers, running a RT operating system, through a cpci or cpci express bus. Preliminary calculations indicate that using the described arrangement will allow the envisaged RT timings to be attained. A single U rackmount dual quad-core Xeon computer running RT Linux, or Sun X4 for example, will ensure that the remaining timing constrains are met. Figure 5 depicts the proposed setup. RT profile measurements will be furthered to the Real-Time Signal Server (RTSS) either via a high latency connection (ethernet) or a fast low latency dedicated RT connection. The future realization of the later, possibly using a technology such as Infiniband, will give access to both the fringe corrected linear average density and the actual plasma regime provided by the reference value injector (Raupp et al. [5]). These interpolated values, available on a.6 ms timescale, will be used to estimate the separatrix density during the ELMy H-mode phases. On-line estimations of R in and R out, the innermost and outermost separatrix positions, will then be computed using the RT NN density profiles. 4

5 K Ka Q V Acquisition front-end Hot link serial card optical links Reflect. RT Diag. Host Computer UTDC SIO board (DMA) RAM Discharge Control System (DCS) Real-Time Signal Server (RTSS) ether. RT network Interface card μp μp Dual Quad-core RT network (Infiniband?) Figure 5: Building blocks of the forseen reflectometry diagnostic RT implementation. Achieving such a fast measurement cycle, when compared with the control timing requirements, enables a degree of measurement redundancy that works in favor of the robustness of the real-time reflectometry application. At this measurement rate, data bursts acquired during the ELM activity can be discarded without incurring in feedback measurement starvation. Some of the envisaged future improvements, made possible by this high profile availability cycle, are the online estimation of edge density gradients, pedestal density and the detection of ELM occurrence. 4 Final remarks It has been shown that, in experimental H-mode regimes, it is possible to use O-mode reflectometry data alone to produce plasma position information with the accuracy required for plasma control. In particular, it was possible to accurately estimate the position of the last closed magnetic surface, the separatrix. This is only possible with the aid of a rule-derived estimate for the density at the neighborhood of the separatrix. The application of neural networks coupled to simple FFT analysis of O-mode reflectometry data proved to be a robust unsupervised method to reconstruct the density profile. This new technique was tested using an extensive realistic H-mode synthetic database. In this universe, it out-performed the standard data processing/inversion procedure. When used with ASDEX Upgrade experimental data it proved to be able to follow the separatrix, with the required accuracy, when compared to magnetic and Lithium beam diagnostic data. The first step to the physical implementation of the real-time neural approach was finished with the development of an highly optimized multithreaded profile reconstruction code. With this code, a calculation time more than faster than the required ITER measurement cycle was accomplished. This achievement opens good perspectives for a successfull implementation of the real-time reflectometry diagnostic on ASDEX Upgrade. Other parameters such as, edge gradients, pedestal density and ELM occurrence are also forseen as real-time measurements obtainable from the reflectometry density profile. The design and development of the required upgrades to the reflectometry diagnostic are underway. A dedicated RT prototype is expected to be operational by the end of 8 early 9. Acknowledgments This work has been carried out in the frame of the Contract of Association between the European Atomic Energy community and Instituto Superior Técnico (IST) and has also received financial support from Fundaç ão para a Ciência e Tecnologia (FCT). The content of the publication is the sole responsibility of the authors and does not necessarily represent the views of the Commission of the European Union, FCT or their services. 5

6 References J. Santos, M.E. Manso, P. Varela, J. Neuhauser, and the ASDEX Upgrade Team. Plasma position measurements from ordinary FM CW reflectometry on ASDEX Upgrade. Review of Scientific Instruments, 74():489 49, March. S Hacquin, F. Silva, S. Heuraux, M. Colin, G. Leclerc, M. Manso, T. Ribeiro, J. Santos, and F. Serra. D and D full-wave simulations of o-mode reflectometry experiments in the presence of non-coherent fluctuations. In Proceedings of the 8 th European Conference on Controlled Fusion and Plasma Physics, volume 5B, pages 9, Funchal, Madeira, June. European Physical Society. S. Heuraux, S. Hacquin, F. Silva, F. Clairet, R. Sabot, and G. Leclert. Radial wave number spectrum of density fluctuations deduced from reflectometry phase signals. Review of Scientifc Instruments, 74():5 55, March. N.L Bretz, C. Kessel, E.J. Doyle, and G. Vayakis. ITER position control reflectometry - conceptual design. In Stott. P.E., Giuseppe Gorini, Paolo Prandoni, and Elio Sindoni, editors, Diagnostics for experimental thermonuclear fusion reactors, pages 9 7, New York, 998. Plenum Press. J. Santos, F. Nunes, M.E. Manso, and P. Varela. A neural network approach to evaluate density profiles from reflectometry in ASDEX Upgrade discharges with internal barriers. Fusion Engineering and Design, 48(8):9 6, August. J. Santos, M. Manso, F. M. Silva, and the ASDEX Upgrade Team. Neural networks as an approach towards reflectometry real time density profile measurements on ASDEX Upgrade (poster). In 6 th Topical Conference on High Temperature Plasma Diagnostics, Williamsburg, USA, May 6. G. Raupp, K. Behler, R. Cole, K. Engelhardt, A. Lohs, K. Lüddecke, W. Treutterer, G. Neu, T. Vijverberg, D. Zasche, Th. Zehetbauer, and The ASDEX Upgrade Team. Commissioning and initial operation experience with ASDEX Upgrade s new real-time control and data acquisition. Fusion Engineering and Design, 8:747 75, May 6. K. Behler, H. Blank, H. Eixenberger, A. Lohs, K. Lüddecke, R. Merkel, G. Raupp, G. Schramm, W. Treutterer, M. Zilker, and The ASDEX Upgrade Team. Real-time diagnostics at ASDEX Upgrade - architecture and operation. In Proceedings of the 6th IAEA TCM on Control Data Acquisition and Remote Participation on Fusion Research, Japan, 7. W. Treuterrer, K. Behler, L. Giannone, N. hicks, A. Manini, M. Marascheck, G. Raupp, M. Reich, A.C.C. Sips, J. Stoeber, W. Suttrop, and The ASDEX Upgrade Team. Real-time diagnostics at ASDEX Upgrade integration with MHD feedback control. In Proceedings of the 6th IAEA TCM on Control Data Acquisition and Remote Participation on Fusion Research, Japan, 7. G. Raupp, G. Neu, W. Treutterer, V. Mertens, D. Zasche, Th. Zehetbauer, and The ASDEX Upgrade Team. Control process structure of ASDEX Upgrade s new control and data acquisition system. Fusion Engineering and Design, 74:697 75, August 5. 6