Magnetic diagnostics layout design for CFETR plasma equilibrium reconstruction

Plasma equilibrium reconstruction provides essential information for tokamak operation and physical analysis.An extensive and reliable set of magnetic diagnostics is required to obtain accurate plasma equilibrium.This study designs and optimizes the magnetic diagnostics layout for the reconstruction of the equilibrium of the plasma according to the scientific objectives,engineering design parameters,and limitations of the Chinese Fusion Engineering Test Reactor(CFETR).Based on the CFETR discharge simulation,magnetic measurement data are employed to reconstruct consistent plasma equilibrium parameters,and magnetic diagnostics'number and position are optimized by truncated Singular value decomposition,verifying the redundancy reliability of the magnetic diagnostics layout design.This provides a design solution for the layout of the magnetic diagnostics system required to control the plasma equilibrium of CFETR,and the developed design and optimization method can provide effective support to design magnetic diagnostics systems for future magnetic confinement fusion devices.

Plasma current tomography for HL-2A based on Bayesian inference

An accurate plasma current profile has irreplaceable value for the steady-state operation of the plasma.In this study,plasma current tomography based on Bayesian inference is applied to an HL-2A device and used to reconstruct the plasma current profile.Two different Bayesian probability priors are tried,namely the Conditional Auto Regressive(CAR)prior and the Advanced Squared Exponential(ASE)kernel prior.Compared to the CAR prior,the ASE kernel prior adopts nonstationary hyperparameters and introduces the current profile of the reference discharge into the hyperparameters,which can make the shape of the current profile more flexible in space.The results indicate that the ASE prior couples more information,reduces the probability of unreasonable solutions,and achieves higher reconstruction accuracy.

Prediction of multifaceted asymmetric radiation from the edge movement in density-limit disruptive plasmas on Experimental Advanced Superconducting Tokamak using random forest

Multifaceted asymmetric radiation from the edge(MARFE) movement which can cause density limit disruption is often encountered during high density operation on many tokamaks. Therefore, identifying and predicting MARFE movement is meaningful to mitigate or avoid density limit disruption for the steady-state high-density plasma operation. A machine learning method named random forest(RF) has been used to predict the MARFE movement based on the density ramp-up experiment in the 2022’s first campaign of Experimental Advanced Superconducting Tokamak(EAST). The RF model shows that besides Greenwald fraction which is the ratio of plasma density and Greenwald density limit, dβp/dt,H98and d Wmhd/dt are relatively important parameters for MARFE-movement prediction. Applying the RF model on test discharges, the test results show that the successful alarm rate for MARFE movement causing density limit disruption reaches ~ 85% with a minimum alarm time of ~ 40 ms and mean alarm time of ~ 700 ms. At the same time, the false alarm rate for non-disruptive and non-density-limit disruptive discharges can be kept below 5%. These results provide a reference to the prediction of MARFE movement in high density plasmas, which can help the avoidance or mitigation of density limit disruption in future fusion reactors.

Development of an upgraded motional Stark effect diagnostic system on EAST tokamak

A new multi-channel motional Stark effect(MSE)diagnostic system has been developed on the upgraded EAST tokamak,which was installed on the port C to observe a tangential neutral beam.A telecentric imaging lens was deployed to ensure uniform illumination from the core to the boundary.A square fiber head which contained 23 fiber bundles was mounted to this imaging lens;each fiber bundle contained 19 fibers and two of them were assigned to CXRS and BES spectrometer,respectively.The angle tuning method was used for matching the Doppler shift of theσcomponent’s wavelength which was caused by the beam voltage.At the present stage,the MSE system only contains ten channels that would be extended to 23 channels in the future,covering a measurement range from R=1.8 to R=2.27 m with a temporal resolution of 10 ms and a spatial resolution of3 cm.The polarization angle-constrained q profiles and current density profiles were reconstructed with EFIT equilibrium reconstructions.In the sawtooth discharges,the q=1 surface position was validated by the ECE signals,which further verified the rationality of the MSE measurement.

Experimental study on fast electron generation during internal crash

Hard x-ray(HXR)burst is found during internal crash in the flat top current stage of experimental advanced superconducting tokamak(EAST)discharges and it is caused by fast electrons.The generated electrons during internal crashes may be an operational safety issue in advanced tokamaks.During an internal crash,locations of fast electron generation from HXR evolution agree with areas of magnetic reconnection from soft x-ray(SXR)tomographic reconstruction.Further statistical analyses show a 27μs time difference between SXR crashes and HXR bursts,and the agreement between time broadening of HXR bursts and estimated characteristic time of magnetic reconnection in EAST.The magnetic reconnections during internal crash are proved to generate fast electrons,by both spatial and temporal agreements.

Fast estimation of ion temperature from EAST charge exchange recombination spectroscopy using neural network

Ion temperature, as one of the most critical plasma parameters, can be diagnosed by charge exchange recombination spectroscopy (CXRS). Iterative least-squares fitting is conventionally used to analyze CXRS spectra to identify the active charge exchange component, which is the result of local interaction between impurity ions with a neutral beam. Due to the limit of the time consumption of the conventional approach (~100 ms per frame), the Experimental Advanced Superconducting Tokamak CXRS data is now analyzed in-between shots. To explore the feasibility of real-time measurement, neural networks are introduced to perform fast estimation of ion temperature. Based on the same four-layer neural network architecture, two neural networks are trained for two central chords according to the ion temperature data acquired from the conventional method. Using the TensorFlow framework, the training procedures are performed by an error back-propagation algorithm with the regularization via the weight decay method. Good agreement in the deduced ion temperature is shown for the neural networks and the conventional approach, while the data processing time is reduced by 3 orders of magnitude (~0.1 ms per frame) by using the neural networks.

Design of snowflake-diverted equilibria of CFETR

The Chinese Fusion Engineering Test Reactor(CFETR)represents the next generation of full superconducting fusion reactors in China.Recently,CFETR was redesigned with a larger size and will be operated in two phases.To reduce the heat flux on the target plate,a snowflake(SF)divertor configuration is proposed.In this paper we show that by adding two dedicated poloidal field(PF)coils,the SF configuration can be achieved in both phases.The equilibria were calculated by TEQ code for a range of self-inductances l_i3.The coil currents were calculated at some fiducial points in the flattop phase.The results indicate that the PF coil system has the ability to maintain a long flattop phase in 7.5 and 10 MA inductive scenarios for the single null divertor(SND)and SF divertor configurations.The properties of the SF configuration were also analyzed.The connection length and flux expansion of the SF divertor were both increased significantly over the SND.