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Zhongbing Shi

Publications and source records attributed to Zhongbing Shi.

3 recordsLinked to original sources

Discovery of Density Limit Disruption Induced by Core-localized Alfv${é}$nic Ion Temperature Gradient Instabilities in a Tokamak Plasma

To achieve a high energy gain, the fusion reactor plasma must reach a very high density. However, the tokamak plasmas ofen undergo disruption when the density exceeds the Greenwald density. The density limit disruption in tokamak plasmas is a mysterious barrier to magnetic confinement nuclear fusion, and hitherto, is still an unresolved issue. Over the past several years, the high density experiments with Greenwald density ratio $n_e/n_{eG}\sim1$ has been carried out using the conventional gas-puff fuelling method in HL-2A NBI and Ohmically heated plasmas. It is found for the first time that there are multiple-branch MHD instabilities in the core plasmas while $n_e/n_{eG}>0.85$. The simulation analysis suggests that the core-localized magnetohydrodynamics (MHD) activities belong to Alfv${é}$nic ion temperature gradient (AITG) modes, and on experiment firstly, it is discovered that they trigger the minor or major disruption of bulk plasmas while the density is peaked. These new findings are of great importance to figure out and understand the origin of density limit disruptions, as well as to forecast and avoid them for future fusion rectors.

physics.plasm-ph

FusionMAE: large-scale pretrained model to optimize and simplify diagnostic and control of fusion plasma

In magnetically confined fusion device, the complex, multiscale, and nonlinear dynamics of plasmas necessitate the integration of extensive diagnostic systems to effectively monitor and control plasma behaviour. The complexity and uncertainty arising from these extensive systems and their tangled interrelations has long posed a significant obstacle to the acceleration of fusion energy development. In this work, a large-scale model, fusion masked auto-encoder (FusionMAE) is pre-trained to compress the information from 88 diagnostic signals into a concrete embedding, to provide a unified interface between diagnostic systems and control actuators. Two mechanisms are proposed to ensure a meaningful embedding: compression-reduction and missing-signal reconstruction. Upon completion of pre-training, the model acquires the capability for 'virtual backup diagnosis', enabling the inference of missing diagnostic data with 96.7% reliability. Furthermore, the model demonstrates three emergent capabilities: automatic data analysis, universal control-diagnosis interface, and enhancement of control performance on multiple tasks. This work pioneers large-scale AI model integration in fusion energy, demonstrating how pre-trained embeddings can simplify the system interface, reducing necessary diagnostic systems and optimize operation performance for future fusion reactors.

physics.plasm-ph

Long radial coherence of electron temperature fluctuations in non-local transport in HL-2A plasmas

The dynamics of long-wavelength ($k_θ<1.4 \mathrm{\ cm^{-1}}$), broadband (20-200 kHz) electron temperature fluctuations ($\tilde T_e/T_e$) of plasmas in gas-puff experiments were observed for the first time in HL-2A tokamak. In a relative low density ($n_e(0) \simeq 0.91 \sim 1.20 \times10^{19}/m^3$) scenario, after gas-puffing the core temperature increases and the edge temperature drops. On the contrary, temperature fluctuation drops at the core and increases at the edge. Analyses show the non-local emergence is accompanied with a long radial coherent length of turbulent fluctuations. While in a higher density ($n_e(0) \simeq 1.83 \sim 2.02 \times10^{19}/m^3$) scenario, the phenomena were not observed. Furthermore, compelling evidence indicates that $\textbf{E} \times \textbf{B}$ shear serves as a substantial contributor to this extensive radial interaction. This finding offers a direct explanatory link to the intriguing core-heating phenomenon witnessed within the realm of non-local transport.

physics.plasm-ph