arXiv subjects

Menglong Zhao

Publications and source records attributed to Menglong Zhao.

6 recordsLinked to original sources

arXiv2026

Triggers for plasma detachment bifurcation in the edge divertor region of tokamaks

We report the discovery of the trigger for detachment bifurcation phenomenon in tokamak divertors, revealed through steady-state and time-dependent UEDGE simulations: The observed electron temperature cliff at the outer target in DIII-D H-mode plasmas with ion $B\times \nabla B$ drift driven into the active divertor results from a bifurcation-induced $T_e$ drop above the X-point accompanied by reversal of the $E\times B$ flow pattern in the private flux region. Time-dependent simulations reveal a two-phase transition mechanism: the high-field-side radiation front first extends across the last closed flux surface and stabilizes above the X-point, causing local $T_e$ to drop from $\sim 70\,\mathrm{eV}$ to $\sim 10\,\mathrm{eV}$ and inducing $E\times B$ flow reversal in a thin layer below the X-point, which lasts $< 0.5\,\mathrm{ms}$; Flow reversal below the X-point subsequently triggers the sharp drop in outer target temperature on a timescale of $1-2\,\mathrm{ms}$, establishing deep detachment a few ms thereafter. A bifurcation transition occurs when the high-field-side radiation front crosses the separatrix while the outer divertor remains attached, with the $T_e$ cliff manifesting distinctly when the outer target $T_e \gtrsim 10\,\mathrm{eV}$ prior to the bifurcation. These results demonstrate that the bifurcation is linked to in-out divertor asymmetry and asymmetric radiation front evolution.

physics.plasm-ph↗

arXiv2025

Divertor Detachment Characterization in Negative Triangularity Discharges in DIII-D via 2D Edge-Plasma Transport Modeling

Edge fluid modeling of the first divertor-plasma detachment experiments in negative triangularity discharges on DIII-D is presented using the 2D multi-fluid code UEDGE, including cross-field particle drifts. Density scans are performed to reproduce the experimental roll-over of the outer-target ion saturation current and to investigate detachment physics for both forward and reverse toroidal magnetic field configurations. Consistent with experiments, the simulations show that approximately 40% higher density is required to reach detachment onset for forward BT compared to reverse BT, and that deep detachment is not achieved for reverse BT. Comparisons with positive triangularity Ohmic discharges further demonstrate that negative triangularity requires substantially higher densities, at or above the Greenwald limit, to access detachment. The modeling indicates that the increased difficulty of achieving detachment in negative triangularity arises from a shorter midplane-to-target connection length, a reduced outer divertor leg length, and lower cross-field transport compared to positive triangularity configurations.

physics.plasm-ph↗

arXiv2025

Physics insights from a large-scale 2D UEDGE simulation database for detachment control in KSTAR

A large-scale database of two-dimensional UEDGE simulations has been developed to study detachment physics in KSTAR and to support surrogate models for control applications. Nearly 70,000 steady-state solutions were generated, systematically scanning upstream density, input power, plasma current, impurity fraction, and anomalous transport coefficients, with magnetic and electric drifts across the magnetic field included. The database identifies robust detachment indicators, with strike-point electron temperature at detachment onset consistently Te around 3-4 eV, largely insensitive to upstream conditions. Scaling relations reveal weaker impurity sensitivity than one-dimensional models and show that heat flux widths follow Eich's scaling only for uniform, low D and Chi. Distinctive in-out divertor asymmetries are observed in KSTAR, differing qualitatively from DIII-D. Complementary time-dependent simulations quantify plasma response to gas puffing, with delays of 5-15 ms at the outer strike point and approximately 40 ms for the low-magnetic-field-side (LFS) radiation front. These dynamics are well captured by first-order-plus-dead-time (FOPDT) models and are consistent with experimentally observed detachment-control behavior in KSTAR [Gupta et al., submitted to Plasma Phys. Control. Fusion (2025)]

physics.plasm-ph↗

arXiv2025

Detachment control in KSTAR with Tungsten divertor

KSTAR has recently undergone an upgrade to use a new Tungsten divertor to run experiments in ITER-relevant scenarios. Even with a high melting point of Tungsten, it is important to control the heat flux impinging on tungsten divertor targets to minimize sputtering and contamination of the core plasma. Heat flux on the divertor is often controlled by increasing the detachment of Scrape-Off Layer plasma from the target plates. In this work, we have demonstrated successful detachment control experiments using two different methods. The first method uses attachment fraction as a control variable which is estimated using ion saturation current measurements from embedded Langmuir probes in the divertor. The second method uses a novel machine-learning-based surrogate model of 2D UEDGE simulation database, DivControlNN. We demonstrated running inference operation of DivControlNN in realtime to estimate heat flux at the divertor and use it to feedback impurity gas to control the detachment level. We present interesting insights from these experiments including a systematic approach to tuning controllers and discuss future improvements in the control infrastructure and control variables for future burning plasma experiments.

physics.plasm-ph↗

arXiv2025

Latent Space Mapping: Revolutionizing Predictive Models for Divertor Plasma Detachment Control

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster, but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., $\sim0.2$ ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over $10^8$ compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.

physics.plasm-ph↗

arXiv2022

Data-driven model for divertor plasma detachment prediction

We present a fast and accurate data-driven surrogate model for divertor plasma detachment prediction leveraging the latent feature space concept in machine learning research. Our approach involves constructing and training two neural networks. An autoencoder that finds a proper latent space representation (LSR) of plasma state by compressing the multi-modal diagnostic measurements, and a forward model using multi-layer perception (MLP) that projects a set of plasma control parameters to its corresponding LSR. By combining the forward model and the decoder network from autoencoder, this new data-driven surrogate model is able to predict a consistent set of diagnostic measurements based on a few plasma control parameters. In order to ensure that the crucial detachment physics is correctly captured, highly efficient 1D UEDGE model is used to generate training and validation data in this study. Benchmark between the data-driven surrogate model and UEDGE simulations shows that our surrogate model is capable to provide accurate detachment prediction (usually within a few percent relative error margin) but with at least four orders of magnitude speed-up, indicating that performance-wise, it has the potential to facilitate integrated tokamak design and plasma control. Comparing to the widely used two-point model and/or two-point model formatting, the new data-driven model features additional detachment front prediction and can be easily extended to incorporate richer physics. This study demonstrates that the complicated divertor and scrape-off-layer plasma state has a low-dimensional representation in latent space. Understanding plasma dynamics in latent space and utilizing this knowledge could open a new path for plasma control in magnetic fusion energy research.

physics.plasm-ph↗