A strong relationship between the fishbone instability and internal transport barrier (ITB) formation has been found on the Experimental Advanced Superconducting Tokamak (EAST) in high β N ELMy H-mode discharges. ITB formation always appears after the fishbone instability, and the fishbone disappears when the ITB grows to a certain extent. Hybrid simulations with the global kinetic-magnetohydrodynamic (MHD) code M3D-K have been carried out to investigate the linear stability and non-linear dynamics of beam-driven fishbone instabilities in these shots. The simulation results show that the fishbone instability absorbs the energy of the fast ions and changes the distribution function of the fast ions, leading to the accumulation of fast ions near the ITB, which might eventually assist in the formation of the ITB. The q = 1 surface disappearance caused by the bootstrap current generated by the steep pressure gradient in the ITB region has been considered as the reason for the fishbone instability vanishing. This process has also been reproduced in simulation. However, the timescale of this change in the q profile is not sufficient under classical current diffusion times. The simulation utilizes another assumption explaining the disappearance of the fishbone instability. The density will form a barrier in the ITB region, which should broaden the distribution of the fast ions, and the broadening profile of the distribution of the fast ion mitigates the growth of the fishbone instability.
An X-mode polarized W-band multi-channel correlation reflectometry is installed on the Experimental Advanced Superconducting Tokamak (EAST). The reflectometry with two poloidally spaced receiving antennas works at four different probing frequencies (i.e. 79.2 GHz, 85.2 GHz, 91.8 GHz, 96 GHz), which enables measurement of density fluctuation at 4 (radial) × 2 (poloidal) spatial points. The diagnostics applied to observe density fluctuation and turbulence perpendicular velocity in plasma core on EAST. The detailed description and initial experimental results are presented in this paper.
:Crop residue is the remaining stems, leaves, and fruit pods in the field after crop harvest. Crop residue plays an important role in the farmland ecosystem. Remote sensing technology has advantages in time and space, and it has become the main method to estimate Crop Residue Cover (CRC). Using remote sensing technology to estimate CRC can obtain information about ground CRC quickly in a large scale, which is of great significance to the promotion of conservation tillage. On the basis of a Sentinel-1 SAR image and a Sentinel-2 optical image, radar index and optical remote sensing index were constructed, respectively. The autumn and spring field sample data in 2018 and 2019 in Lishu County, Jilin Province were combined. The correlation of the remote sensing index and the maize residue cover was explored, and the method of soil texture zoning modeling was adopted to reduce the influence of surface background factors on the estimation of CRC. To further improve the estimation accuracy of maize residue cover, the radar index and optical remote sensing index were combined. Moreover, the optimal subset regression and soil texture zoning were used to establish the maize residue cover estimation model, and the estimation mapping of maize residue cover in the study area was then completed. Results show that: soil texture zoning modeling can effectively solve the problem of soil heterogeneity, thus improving the accuracy of inversion. The performance of each remote sensing index in autumn high coverage period in 2018 is better than that in spring low coverage period in 2019. The STI and NDTI index have strong stability and the best performance in optical remote sensing index. R 2 is 0.701 and 0.697, respectively; whereas in the radar index, the correlation between γ 0 VH based on cosine correction method and CRC measured is the highest, and R 2 is 0.564. The combination of radar index and optical remote sensing index can effectively improve the accuracy of CRC estimation. The regression model based on the combined index has the best performance with the method of optimal subset regression and soil texture zoning. The R 2 of the model is 0.799, and the RMSE is 13.67%, which show high accuracy. Therefore, the proposed method improves the accuracy of CRC estimation.
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