SafeDivertor: Faithful Divertor Heat Flux Reconstruction from Macroscopic Plasma State Signals via Time-Frequency Prior Exploitation

2026-08-06

AI Summary

Traditional infrared-based divertor heat flux inversion relies on post-discharge processing and complex modeling, hindering its applicability for real-time operation. This work proposes a novel signal-driven paradigm tailored for online reconstruction, directly leveraging multi-source macroscopic plasma signals measurable during discharges to enable real-time, time-resolved radial heat flux profile estimation. To this end, we introduce DivMPS2HF—the first multi-source dataset for this task—and present the SafeDivertor framework, which integrates physics-informed initialization, input perturbation robustness enhancement, spectrum-aware optimization, and a multi-objective progressive training strategy. Experimental results demonstrate that SafeDivertor significantly outperforms existing time-series baselines across all five evaluation metrics, establishing a new performance benchmark for online divertor heat flux reconstruction.

Abstract

Divertor heat-flux analysis is essential for understanding plasma-wall interactions and protecting plasma-facing components in magnetic-confinement fusion devices, while conventional infrared-based inversion is usually performed after discharge and requires heat-conduction modeling with device-specific material properties, divertor geometry, and boundary conditions. Rather than accelerating this conventional infrared-based inversion paradigm, we introduce a new online-oriented signal-based reconstruction paradigm that directly reconstructs time-resolved radial heat-flux profiles from multi-source macroscopic plasma-state signals available during discharge. To enable systematic study of this task, we construct \textbf{DivMPS2HF}, a multi-source discharge dataset that provides the data foundation and benchmark for signal-based divertor heat-flux reconstruction. We further propose \textbf{SafeDivertor}, a task-driven framework designed to address the key challenges of signal-based heat-flux reconstruction. It employs physical prior-aware initialization to provide radial-distribution guidance for target channels, input perturbation to reduce over-reliance on specific heterogeneous signals, spectral-aware reconstruction optimization to exploit time-frequency priors and preserve transient dynamics, and progressive training to stabilize the optimization of these complementary objectives. Experiments on DivMPS2HF demonstrate that SafeDivertor achieves the best overall performance among the evaluated time-series baselines across all five metrics, establishing a new performance benchmark for signal-based divertor heat-flux reconstruction. The source code will be released on https://github.com/Event-AHU/OpenFusion

Problem

• divertor heat flux
• plasma-state signals
• real-time reconstruction
• magnetic-confinement fusion
• infrared inversion

Innovation

• signal-based reconstruction
• time-frequency prior
• divertor heat flux
• plasma-state signals
• physical prior-aware initialization