Numerical simulation of impact of supersonic molecular beam injection on edge localized modes

A three-field model with the impact of supersonic molecular beam injection(SMBI) based on the BOUT++ code is built to simulate edge localized modes(ELMs). Different parameters of SMBI are explored to find an optimal SMBI scenario for ELM mitigation. The linear simulations show that the growth rate of peeling-ballooning mode is reduced by SMBI. The reduction amplitude of the growth rate is increased when the amplitude or width of SMBI is increased, and when SMBI is deposited at the top, bottom and middle of the pedestal, the reduction amplitude increases successively. The nonlinear simulations show that the ELM size is reduced by SMBI. The reduction amplitude of the ELM size is increased when the amplitude or width of SMBI is increased, and when SMBI is deposited at the bottom, top and middle of the pedestal, the reduction amplitude increases successively. Surface-averaged pressure profiles and filamentary structures are analyzed when the ELMs erupt. Deep deposition of SMBI such as at the top and middle of the pedestal reduces the inward collapse amplitude of the pressure profiles, which can improve the confinement efficiency during ELMs. Shallow deposition of SMBI such as at the middle and bottom of the pedestal reduces the outer extent of the filamentary structures, which can slow down the erosion of plasma-facing components caused by ELMs. In conclusion,shallow deposition of SMBI with sufficient amplitude and width can meet the needs of ELM mitigation.

An extended binary subband canonical correlation analysis detection algorithm oriented to the radial contraction-expansion motion steady- state visual evoked paradigm

The radial contraction-expansion motion paradigm is a novel steady-state visual evoked experimental paradigm,and the electroencephalography(EEG)evoked potential is different from the traditional luminance modulation paradigm.The signal energy is concentrated chiefly in the fundamental frequency,while the higher harmonic power is lower.Therefore,the conventional steady-state visual evoked potential recognition algorithms optimizing multiple harmonic response components,such as the extended canonical correlation analysis(eCCA)and task-related component analysis(TRCA)algorithm,have poor recognition performance under the radial contraction-expansion motion paradigm.This paper proposes an extended binary subband canonical correlation analysis(eBSCCA)algorithm for the radial contraction-expansion motion paradigm.For the radial contraction-expansion motion paradigm,binary subband filtering was used to optimize the weighting coefficients of different frequency response signals,thereby improving the recognition performance of EEG signals.The results of offline experiments involving 13 subjects showed that the eBSCCA algorithm exhibits a better performance than the eCCA and TRCA algorithms under the stimulation of the radial contraction-expansion motion paradigm.In the online experiment,the average recognition accuracy of 13 subjects was 88.68%±6.33%,and the average information transmission rate(ITR)was 158.77±43.67 bits/min,which proved that the algorithm had good recognition effect signals evoked by the radial contraction-expansion motion paradigm.