Recent Publications

Diagnostic of magnetically confined plasmas with superconducting transition edge sensors

Luciano Gottardi, Filipe Ventura Grilo, Liyi Gu, M Botz, M De Wit, Jonas Werner Danisch, José R. R Crespo López-Urrutia

SRON, Max-Planck-Institut für Kernphysik, Deutsches Elektronen-Synchrotron (DESY)

High-resolution X-ray spectroscopy is a key diagnostic tool for the hot plasma core of fusion reactors, since it delivers crucial information on temperature, density and concentrations of heavy element impurities. Originally developed for astrophysical applications, cryogenic X-ray instruments based on superconducting transition-edge sensor (TES) microcalorimeters are non-dispersive spectrometers with high resolving power over a broad energy range of 100 eV to 12 keV. They reach over 90% quantum efficiency and offer extremely low background counts. In this study, we explore the advantages of our TES microcalorimeter for a specific burning plasma scenario of the International Thermonuclear Experimental Reactor (ITER) using a realistic end-to-end simulator developed for future X-ray space instrumentation. We compare the performance of existing diagnostic instruments with that of our TES microcalorimeter, which can simultaneously register spectra from the soft to the hard X-ray range at a fast rate, resolving closely spaced lines from heavy ions such as iron (Fe) and tungsten (W). This provides detailed diagnostics of ionisation balance and impurity content for ITER and other tokamaks as well as stellarators and reversed-field pinches. The TES spectrometer is capable of passively detecting X-ray emissions without interfering with the plasma. It can function from a considerable distance, minimizing neutron hazards, which makes it ideal for future fusion reactor such as DEMO, where diagnostic access is limited.

Beam optics and stripping losses in a full-scale ITER negative ion source: multibeamlet analysis by beam emission spectroscopy

Riccardo Agnello, Marco Barbisan, Roberto Pasqualotto, Antonio Pimazzoni, Emanuele Sartori, Barbara Zaniol, Edgard Zuin

Consorzio RFX (CNR ENEA INFN University of Padova Acciaierie Venete SpA), École Polytechnique Fédérale de Lausanne Swiss Plasma Center, Consiglio Nazionale delle Ricerche, Università degli Studi di Padova

In this work, Beam Emission Spectroscopy (BES) is applied to the investigation of beam divergence and stripping losses in the full-scale ITER negative ion source prototype SPIDER, operating in multibeamlet configuration. A semi-analytical model is developed to simulate the emission spectra produced by overlapping beamlets, accounting for realistic beamlet divergence, aiming, and beam composition along the accelerator and drift regions. The comparison between synthetic and experimental spectra shows that the Doppler broadening measured in multibeamlet operation cannot be interpreted solely in terms of single-beamlet divergence, but results from the combined effects of beamlet overlap, residual magnetic deflections, electrostatic repulsion, and halo contributions. Using beamlet parameters independently obtained from calorimetric diagnostics, the model reproduces a significant fraction of the experimentally measured divergence. Moreover, the possibility of displacing a line-of-sight allows the detection of variations in the populations of particles with different divergences, namely the core and halo components, across the beam. As a phenomenological characterization, stripping losses are systematically quantified over a wide range of operational parameters, showing an approximately linear increase with source pressure and values consistent with previous single-beamlet studies. These results demonstrate the capability of BES, combined with multibeamlet modelling, to support beam optimization and performance assessment in ITER-relevant negative ion sources.

Experimental Identification of the n=2 Error Field and its Interaction with MHD Activity and Plasma Rotation in MAST-U

Lidia Piron, David Anthony Ryan, Andrew Kirk, Alessandra Tonel, Paolo Zanca, Geoffrey Cunningham, Matteo Baruzzo, Sam Blackmore, Christopher J Ham, Scott Alan Silburn, et al.

Culham Centre for Fusion Energy, Consorzio RFX, UKAEA, Universita degli Studi di Padova, ENEA for EUROfusion

Spurious magnetic field perturbations, known as error fields (EFs), with toroidal mode number n = 2 can have deleterious effects similar to those associated with n = 1 error fields. An n = 2 error field source is expected in MAST-U because the poloidal field coils P4 and P5, retained from the previous device MAST, exhibit n = 1 and n = 2 deformations due to coil manufacturing imperfections. This work presents the n = 2 error field identification studies carried out in MAST-U. The n = 2 compass scan indicates that, for 750 kA plasma current, double-null divertor H-mode plasmas, the n = 2 error field is relatively small when assessed using locked mode onset and rotation braking as metrics, suggesting that the n = 2 EF is effectively screened by the plasma in this scenario. Furthermore, during the n = 2 EF identification studies, an interesting interplay between the onset times of n = 1 and n = 2 rotating modes and plasma rotation was observed. This observation enables the identification of control strategies aimed at delaying the onset of n = 1 and n = 2 MHD modes while sustaining plasma rotation.

Deep learning tearing mode evolution prediction for instability control

Runyu Luo, Wei Zheng, Fengming Xue, Chengshuo Shen, Zhengkang Ren, Yu Zhong, Ruomu Wang, Yong Hua Ding, Zhongyong Chen, Nengchao Wang

Huazhong University of Science and Technology, Institution of Fusion and Plasmas

To address the general need for control oriented prediction of deleterious m/n=2/1 tearing mode evolution in tokamak plasmas, this work develops a data-driven framework and is validated on J-TEXT experimental database. Based on multi-diagnostic histories and future control reference trajectories, including resonant magnetic perturbation (RMP) and electron cyclotron resonance heating (ECRH) references, the framework forecasts the rotating 2/1 tearing mode evolution trajectory over the energy confinement time scale by jointly forecasting the occurrence probability, rotation frequency, and mode amplitude, while locked phases are treated as the termination of the rotating state in the present framework. A sequence based learning model is adopted to capture correlations among diagnostic signals and control references, enabling direct multistep generation of full evolution trajectories for multiple prediction targets in a single forward pass. To help the model form latent representations of the rotating 2/1 tearing mode state, a curriculum learning strategy is employed to guide the model from simpler present window, 2/1 mode identification tasks, to long horizon evolution prediction, which is informative for predicting control relevant transitions. To further evaluate the prediction performance critical for tearing mode suppression involving the appearance and termination of rotating tearing mode states, this research constructed targeted subsets. Finally, the model achieves an AUC of 98.4% on the full test dataset. On targeted subsets, it attains an onset prediction accuracy of 82.87%, and achieves an 11.4% relative improvement in similarity of predicted amplitude evolution trends. Finally, to examine the physical consistency of the model’s response to changes in control references, counterfactual analyses are performed under modified control conditions. The results indicate that the framework is capable of reproducing the corresponding evolution of key parameters of the rotating 2/1 tearing mode state, supporting its potential as a data driven surrogate for future control-oriented studies.

On the feasibility of model-based feedback control of vertical instability growth rate using out-vessel coils in ARC-like scenarios

Arunav Kumar, Cesar Clauser, Theodore Golfinopoulos, Jon C. Hillesheim

In this work, we propose a model-based feedback controller that regulates the vertical instability growth rate ($γ_{gr}$) of a high-elongation, double-null tokamak directly, using only out-vessel poloidal field (PF) coils. High elongation raises the achievable plasma current and fusion performance but makes the plasma vertically unstable, and in a fusion power plant the in-vessel coils that present devices rely on for stabilization may be absent, leaving only distant out-vessel circuits. The controller couples a machine learning surrogate of non-rigid, profile agnostic vertical instability metric to a constrained quadratic program: the surrogate supplies real-time $γ_{gr}$ estimates and, via automatic differentiation, the actuator sensitivities, while the program allocates coil voltages to track a target growth rate, maintain double-null divertor balance, and respect electromechanical limits. We tested this method on the ARC~V3A power plant design configuration across 24 closed-loop simulations spanning equilibrium variations, actuator degradations, and transient disturbances. We achieved full or marginal success in 83\% of these cases (full in 50\%, marginal in a further 33\%) and lose control in the remaining 17\%; the failures map the boundary of out-vessel controllability (occurring at the highest growth rates) and under actuator limits. The controller does not regulate boundary shape explicitly: separatrix geometry follows indirectly from growth rate and flux balance control and would require a separate shape control layer for sustained scenario evolution.

Areal-time disruption prediction and mitigation system for the EXL-50U spherical torus

J. P. Zhou, S. F. Liu, J. Q. Cai, H. Y. Zhao, J. Li, Y. P. Zhang, D. Guo, C. Wu, A. Wang, H. Y. Li, et al.

This work presents a real-time disruption prediction and mitigation system developed for high-current operations in the EXL-50U Spherical Torus. By leveraging Reflective Memory (RFM) technology, the system establishes a low-latency real-time data path, creating a fully integrated pipeline that synchronizes multi-channel diagnostic acquisition, online preprocessing, real-time inference, and Massive Gas Injection (MGI) triggering. At its core, a lightweight prediction model based on a Temporal Convolutional Network (TCN) with a channel attention mechanism extracts disruption precursor features while adaptively weighting the importance of different diagnostic channels. {Tested across discharges \#14036--\#14790, the system achieves a true positive rate of 82.4\% and a false positive rate of 16.5\%, with end-to-end latency below $1~\mathrm{ms}$ in online operation.} Mitigation experiments further show that the MGI system can supply the required gas inventory and trigger a rapid post-injection plasma response, supporting the operational requirements of EXL-50U and providing engineering guidance for future devices such as EHL-2. These results confirm the engineering feasibility of integrated real-time disruption control on EXL-50U, offering a robust basis for future research in higher-parameter fusion devices.

AI Surrogate Modeling for Real-Time Tokamak Equilibrium Prediction: Benchmarking Neural Architectures and Validation on EXL-50U

Guoyang Shi, Zitong Zhang, Siqi Ding, Jianguo Chen, Yapeng Zhang, Jiayi Zhi, Hanyue Zhao, Tianyuan Liu

Fast and reliable plasma equilibrium prediction is essential for real-time tokamak operation and control, but conventional Grad-Shafranov (GS) solvers are often too costly for real-time deployment. We develop an AI surrogate framework and benchmark five architectures (MLP, CNN, FNO, Transformer, and KAN) on a numerical GS database with 100,000 IID and 10,000 OOD samples. Under a unified protocol, we evaluate accuracy, inference efficiency, model scaling, and robustness. We also establish device-level validation on the EXL-50U tokamak by linking numerical GS solutions, surrogate predictions, and the standard Shape Editor reference to assess simulation-to-device consistency. The surrogates achieve errors of $10^{-3}$-$10^{-2}$ relative to GS solutions, while the GS-to-device discrepancy remains at $10^{-3}$. Transformer gives the best IID accuracy, whereas CNN offers the best balance of accuracy, robustness, and speed, reaching 0.7 ms TensorRT latency. On unseen plasma geometries and parameter regimes, CNN and FNO show the strongest extrapolation stability, with 4%-5% relative $L_2$ error, while models with weaker inductive biases degrade more substantially. Scaling data and model capacity improves interpolation but not necessarily extrapolation, revealing a trade-off between capacity and OOD generalization. Overall, this work provides a systematic, device-consistent benchmark for AI-based GS prediction and practical guidance for selecting reliable surrogates for real-time plasma control and fusion applications.

Interpretable statistical feature engineering for early disruption prediction in the short pulse ADITYA tokamak

Jyoti Agarwal, Kavit Patel, Bhaskar Chaudhury, Abhishek Sharma, Shrichand Jakhar, Manika Sharma

Reliable early disruption prediction is critical for the safe operation and real-time control of tokamaks. However, machine learning based prediction frameworks have predominantly targeted medium and long pulse devices, with comparatively limited attention given to short pulse tokamaks where available warning time is inherently constrained. In this work, an interpretable machine learning framework is developed for feature engineering and early prediction of disruptions in the ADITYA using the initial plasma evolution information, prior to the activation of the negative converter of the ohmic transformer power supply. Statistical descriptors comprising the mean, variance, skewness, kurtosis and wavelet energy entropy are extracted from routinely available plasma diagnostics over different operation time windows. Decision tree based feature selection is employed to identify physically meaningful disruption precursors and to reduce feature dimensionality. These selected features are used to train a random forest classifier. The proposed framework achieves stable predictive performance across different analysis windows, with a maximum ROC-AUC of 0.87 for 0-35 ms and 0-40 ms windows. Comparable and in some cases improved, performance is obtained using the reduced feature set, demonstrating that the selected statistical descriptors retain the essential information required for disruption prediction. The proposed methodology provides an interpretable and computationally efficient framework for real time disruption prediction in short pulse tokamaks and establishes that carefully engineered statistical descriptors can effectively replace raw time series inputs for early disruption prediction, thereby offering a practical pathway toward real time plasma control in short pulse tokamaks similar to ADITYA and ADITYA-U.

State-Space Model-Enabled Reinforcement Learning for Magnetic Configuration Controlon EXL-50U

Pei Guo, Zhengyuan Chen, Jianguo Chen, Xuanhe Wang, Guoyang Shi, Siqi Ding, Yapeng Zhang, Lei Xing, Yong Liu, Xiang Gu, et al.

Accurate feedback control of the plasma current ($I_p$) and centroid position $(R_c,Z_c)$ is essential for the stable operation of spherical torus (ST) plasmas. Conventional proportional-integral-derivative (PID) controllers require extensive manual tuning and struggle with the fast, strongly coupled dynamics that arise as plasma performance improves. Reinforcement learning (RL) has recently emerged as a promising alternative to such complex magnetic control problems, yet its practical deployment on ST devices remains challenging. This paper presents a practical RL controller for the EXL-50U ST, trained within a rigid RZIP state-space model (SSM) that enables efficient offline policy learning. A lightweight plasma position reconstructor is developed to estimate $(R_c,Z_c)$ from magnetic probe signals within the real-time control cycle. The trained policy is seamlessly deployed on the EXL-50U plasma control system, achieving stable regulation of $I_p$ and $(R_c,Z_c)$ and sustaining discharges up to 650 ms under RL control. These results demonstrate the feasibility and practical potential of model-informed RL for magnetic configuration control in ST devices, offering a promising direction beyond conventional PID-based schemes.

Modified Kalman Filtering Derived from Non-Maxwellian Distribution Functions in Open Systems

Olivier Izacard

Kalman filtering is exact for linear dynamics with Gaussian state and observation statistics, but a mean-covariance representation cannot retain finite non-Gaussian structure generated by source-driven kinetic evolution. We formulate a modified filtering theory for open plasma systems in which the additional state structure is derived from a non-Maxwellian velocity-space distribution (NMDF) rather than introduced as an empirical residual family. A kinetic manifold defines $f_s(\mathbf X_s,\mathbf v)$, while a fixed diagnostic map $H_D$ generates the measurement PDF $p_{D,s}$; the projected kinetic equation determines the state-prediction dynamics. The posterior evolves continuously and is corrected by Bayes' rule, with positivity-constrained relative-entropy projection when required. The Gaussian posterior with affine dynamics and a linear Gaussian observation model recovers the Kalman-Bucy and discrete Kalman limits. The first explicit non-Gaussian closure is a five-coordinate INMDF from exact five-moment inversion, with Kappa retained as a broad-tail alternative. Using seven Alcator C-Mod Langmuir-probe ion-saturation-current PDFs, six kinetic manifolds are propagated through the same source statistics, noise model, normalization, and probe response. Because the published histograms lack time ordering, recursive tracking is not tested. In universal leave-one-condition-out prediction, double-INMDF ranks first in all four held-out divertor conditions, with mean error 0.0989 versus 0.1089 for MDF. Within-region calibration gives nearly identical divertor errors for first- and double-INMDF, 0.0936 and 0.0937, while two-Maxwellian gives the smallest midplane error, 0.0718. Predictions remain conditional on published source and noise controls, but show that a frozen source-to-kinetic response generalizes to an unseen current PDF and that the preferred response is region dependent.

AXUV synthetic diagnostic for ASDEX Upgrade and its application for SPI simulations

Ferenc Lengyel, Weikang Tang, Matthias Hölzl, Matthias Bernert, Matěj Tomeš, Peter Halldestam, Paul Heinrich, Gergely Papp, Stefan Jachmich, Umar Sheikh, et al.

We introduce an Absolute eXtended UltraViolet (AXUV) diode-based camera forward-modelling tool to support the validation of mitigated disruption simulations and the interpretation of experimental phenomena, with applications to the ASDEX Upgrade (AUG) tokamak. AXUV diodes measure electromagnetic radiation across a wide spectral range with a significantly higher time resolution (~microseconds) than foil bolometers (~milliseconds), albeit with a non-uniform spectral responsivity. AXUV is suitable for examining fast phenomena, such as shattered pellet injection (SPI), where the radiation localisation and radiated power provide information on the deposition of pellet material. Due to the characteristics and degradation of AXUV diodes, absolute power measurements are subject to large systematic uncertainties, especially when the spectra are time-varying, as in e.g. mixed Ne/D2 SPI experiments. These challenges motivated the development of a synthetic diagnostic within the Cherab-Raysect optical modelling framework, which is applied here to four AXUV cameras in two poloidal cross-sections of AUG. The synthetic diagnostic provides a means to understand how the diodes measure radiation under SPI conditions and to connect first-principles plasma simulations with experimental measurements. The details of the synthetic diagnostic are presented, and the capabilities are illustrated with applications to AUG SPI simulations performed in JOREK. The synthetic signals generated from these simulations are compared with experimental measurements from the 2022 SPI campaign and show qualitatively similar features in many respects. Particularly good agreement was found in the time evolution of the studied high Ne-content (10%) pellet, whereas a different, low Ne-content (0.17%) case exhibited more pronounced differences, likely due to the absence of background impurities in the underlying SPI simulations.

Advantage-level Aggregation Reinforcement Learning for X-point Target Magnetic Configuration Control in an EXL-50U Experiment-Calibrated Simulation Environment

Siqi Ding, Xuanhe Wang, Pei Guo, Guoyang Shi, Changquan Yu, Yiting Wang, Xianming Song, Xiang Gu, Zhengyuan Chen, Lei Xing, et al.

Managing divertor heat loads is a central challenge for compact, high-power tokamaks. To increase local flux expansion and decouple the dissipation volume from the core, EHL-2 adopts the X-point target (XPT) divertor. This requires the secondary X-point to remain on the divertor leg; displacement degrades the topology and exhaust geometry. Current experiments, including EXL-50U discharges, rely on precomputed feedforward waveforms with PID loops on global quantities. Lacking dedicated closed-loop feedback for the secondary null, XPT operation is repeatable but not routine. We formulate XPT feedback as a multi-objective reinforcement learning (RL) control problem in a free-boundary environment calibrated to EXL-50U discharge #13906. To address strong coupling among plasma current, shape, and null constraints - where reward scalarisation collapses objective-specific temporal credit - we develop Advantage Aggregation (AdvA). AdvA preserves objective-wise temporal credit before worst-objective-aware nonlinear scalarisation and introduces a residual correction to policy updates. AdvA-PPO is evaluated against Reward-PPO and a feedforward-plus-PID baseline under nominal operation, measurement uncertainties, and unseen initial equilibria. On a 500 ms rollout, AdvA-PPO raises the mean worst-channel score from 0.23 to 0.81 over Reward-PPO, reducing X-point flux RMSE by ~20x. Under combined measurement uncertainties, it is the only learned controller completing the horizon while retaining a usable XPT shape. Multi-initialization fine-tuning enables a single AdvA-PPO policy to complete full-horizon operation across divertor and limiter initial equilibria. These results provide a simulation-based foundation for future real-time XPT validation on EXL-50U.

Reinforcement learning for vertical position control on the EXL-50U spherical tokamak

Lei Xing, Huicong Ma, Changquan Yu, Xuanhe Wang, Jiayi Zhi, Pei Guo, Mengyao Li, Zhengyuan Chen, Yapeng Zhang, Guoyang Shi, et al.

Vertical position control is essential for sustaining high-performance operation in spherical tokamaks, where increased plasma elongation introduces stringent requirements on fast and robust stabilization. This work presents an experimentally validated reinforcement-learning(RL)-based vertical position control framework for the EXL-50U spherical tokamak. A high-fidelity discharge-reconstructed simulation environment is developed by integrating physics-based plasma-circuit models with experimental equilibrium information, enabling systematic controller synthesis and sim-to-real evaluation. Within this framework, RL is benchmarked in simulation against operational proportional--integral--derivative (PID) and model-based linear quadratic regulator (LQR) controllers under identical plant dynamics, actuator constraints, and measurement imperfections.Simulation results show that RL achieves tracking accuracy comparable to PID with consistently lower vertical-stabilization coil effort, while lightweight integral compensation improves robustness against residual model--plant mismatch. The RL controller is subsequently deployed on EXL-50U for closed-loop experiments. Across more than ten discharges with RL takeover, stable vertical regulation is achieved within the controlled windows. For seven representative discharges, RL maintains millimetre-scale tracking accuracy comparable to the operational PID controller (MAE typically ~ 1-5 mm) while consistently reducing actuator effort. These results demonstrate the feasibility of learning-based plasma control on a real spherical tokamak and establish a practical pathway toward future fusion control systems.

Real-time total ECRH power control for reliable long-pulse operation and density feedback at Wendelstein 7-X

Laurent Krier, Stefan Marsen, Heike Laqua, Heinrich Laqua, Dmitry Moseev, Frank Noke, Hans Oosterbeek, Niklas Simon Polei, Sergiy Ponomarenko, Taurino Reichert, et al.

Max-Planck-Institut für Plasmaphysik

The Electron Cyclotron Resonance Heating (ECRH) system at the Wendelstein 7-X (W7-X) stellarator is equipped with eleven megawatt-class gyrotrons that operate at a frequency of 140 GHz, designed for pulse durations of up to thirty minutes. For long-pulse detached-divertor plasma experiments at W7-X, a stable total ECRH power delivered to the plasma is crucial, because the radiated power at the plasma edge is preferably close to the heating power. Consequently, the unexpected shutdown of only one gyrotron can cause a premature end of the experiment due to radiation collapse of the plasma. This paper introduces a system-level controller that dynamically redistributes power among gyrotrons to maintain total ECRH power, improving reliability and enabling feedback control. The controller maintains a specified total ECRH power output by adjusting the accelerating voltage of all gyrotrons. First implemented in the operational phase (OP) 2.2, the system contributed to two major milestones of W7-X during OP 2.3: highest long-pulse triple product for 43 s and highest energy turnover of 1.8 GJ during 360 s. In addition to reliability improvements, the controller enables the adjustment of the total ECRH power for other control objectives, such as maintaining a desired line-integrated plasma density to counteract the ECRH pump-out effect in high-performance scenarios. This paper also presents the first proof-of-principle experiments demonstrating an ECRH-based density feedback system.

Dynamic mechanisms across the transition from the L-mode to steady-state H-mode in Large Helical Device

Wei Li, Yuhong Xu, Masahiro Kobayashi, Xian-Qu Wang, Jun Cheng, Akihiro Shimizu, M Yoshinuma, Haifeng Liu, X Zhang, Jie Huang, et al.

Southwest Jiaotong University, Institute of Fusion Science, National Institute for Fusion Science, National Institutes of Natural Sciences

Dynamic features across the transition from low (L) to steady-state high (H)-mode in Large Helical Device are investigated. We focus on several transition processes from the L-mode, developing H-mode towards the stable H-mode. It appears that for the initial L-H transition, the mean Er × B flow curvature and nonlinear energy cascading of ambient turbulence both play significant roles for entering the developing H-mode. From the developing to stable H-mode, experimental results reveal essential effects of nonlinear energy coupling between turbulence and large-scale MHD modes on sustaining the steady H-mode, for which the MHD bursts act as a predator whereas turbulence is a prey. These findings provide additional insight into the dynamic evolution from the L-mode to steady-state H-mode.

Evaluation of beam properties using a checkerboard masking configuration at the NNBI test stand BATMAN Upgrade

Araceli Navarro, Eleni Nanou, Michael Barnes, Christian Wimmer, Dirk Wünderlich, Ursel Fantz

Max-Plank-Institut für Plasmaphysik

Innovative lasers and polychromators for multi-wavelength real-time Thomson scattering in magnetic-confinement fusion devices

Eugene E. Mukhin, Sergey Yu. Tolstyakov, Gleb S. Kurskiev, Nikita V. Ermakov, Nikita S. Zhiltsov, Philip Ph. Forsh, Ekaterina E. Tkachenko, Valery A. Solovey, Sergey E. Aleksandrov, Andrey V. Nikolaev, et al.

Ioffe Institute, LLC “Spectral-Tech”, LLC “Lasers & Optical Systems”, Institution “Project Center ITER”, ITER Organization

GAM frequency structure and properties in ohmic and powerful ECR-heated plasmas in a tokamak

Alexander V Melnikov, Leonid E Eliseev, Yaroslav Maksimovich Ammosov, Sergey E. Lysenko

Russian Research Centre - Kurchatov Institute

The geodesic acoustic mode (GAM) is a high-frequency branch of zonal flows, considered as a mechanism of the turbulence self-regulation, which affect the radial transport of energy and particles. The GAM studies were performed in the T-10 tokamak, using the heavy ion beam probing (HIBP). The power spectral density of plasma potential has the main GAM peak with frequency f ~ 20 kHz, and two satellite peaks, high-frequency (HF) and low-frequency (LF) ones. In ohmic plasmas and in discharges with moderate electron cyclotron resonance heating (ECRH), both satellites are separated from the main peak by the Δ f ~± 3−4 kHz. Each of three peaks has the character of a global eigenmode of plasma oscillations with the frequency and amplitude of fluctuations almost constant in a wide radial region from the core to the edge. At the edge, the amplitude of the GAM peaks decreases to zero. Thus, the radial dependence of the GAM frequency does not obey the local Winsor formula f GAM ~ C s ( r )/ R , where C s ~ T e 1/2 is the ion-sound velocity. Nevertheless, in ohmic discharges and at the moderate ECRH power P EC <0.5 MW, the frequencies of all peaks depend on C s , taken in their birth points located at the edge. With a further increase in temperature or at the powerful ECRH (0.5 MW < P EC < 2.2 MW) the frequencies of all peaks deviate from the C s dependence and saturate. When temperature increases, the frequency difference between the main GAM peak and the HF-satellite decreases, and these two peaks merge into the single one, reaching the upper limit for f GAM . The bicoherence analysis finds the three-wave coupling of GAM with quasicoherent and stochastic low-frequency turbulent modes. Each peak has its own frequency range of coupling.

Multi-diagnostic characterization of neutrals in the confined region of DIII-D using interpretive DEGAS2 simulations

Quinn Pratt, Shaun R Haskey, George Wilkie, Laszlo Horvath, Raúl Gerrú, Gilson Ronchi, Mathias Groth

Princeton Plasma Physics Laboratory, Massachusetts Institute of Technology, Oak Ridge National Laboratory, Aalto University

Measurements from multiple diagnostics are combined to constrain the density of neutrals in the confined plasma and improve our understanding of edge particle sources. Passive D α emission spectrum measurements are obtained along tangential views at the plasma midplane and near the X-point. Spectral D α measurements provide a strong constraint on the neutral population through energy information in the wavelength distribution of emission. The two-dimensional distribution of neutrals is calculated using interpretive DEGAS2 neutral transport simulations with a plasma background based largely on 1D profiles and magnetic equilibrium reconstruction. DEGAS2 is used as a forward model to predict the emission measured along various lines of sight. We demonstrate two approaches for calibrating DEGAS2 simulations to match spectral emission measurements: (1) fitting the strength of neutral sources at the simulation boundary, and (2) optimizing the plasma background in the pedestal/SOL. Traditional filter-based measurements of D α and Ly α emission are used to validate the calibrated DEGAS2 case at multiple poloidal locations. Once calibrated, DEGAS2 is able to match the measurements generally within a factor of 2, garnering confidence in our diagnostic models and the physics included in DEGAS2. The experimentally constrained 2D neutral distribution is used to quantitatively study particle transport. We report the flux surface averaged neutral density and (main ion) particle source for a standard DIII-D H-mode plasma. In the pedestal, the particle source is found to be primarily driven by divertor neutral sources (recycling). However, neutrals originating in the main chamber play a significant role further inside the plasma. We find the global (main ion) particle confinement time to be τ p,D+ ≈ 130 ms (τ p,D+ ≈ τ E /2). Finally, we present evidence for poloidal asymmetries in the plasma, including decreased main ion temperature above the X-point, and a high density region above the inner target.

Thermomechanical analysis of the conceptual design of the ITER loss alpha monitor probe head

J. Hidalgo-Salaverri, J. Gonzalez-Martin, M. Kocan, J. Ayllon-Guerola, C. Navarro, E. Agus, G. Simic, M. Garcia-Munoz, J. Rueda-Rueda, R. Marques

University of Seville, Politecnico di Torino