Data-Driven Approach on the Mechanism of Radiative Collapse in the Large Helical Device

摘要

A radiative collapse predictor has been developed using a machine-learning model based on high-density plasma experiments in the Large Helical Device (LHD). Concurrently, the physical background of radiative collapse was discussed based on the distinct features extracted by a sparse modeling, which is one of the frameworks of data-driven science. Electron density, CIV and OV line emissions, and electron temperature at the plasma edge have been extracted as the key parameters of radiative collapse. Those parameters are relevant to the physical knowledge that the major cause of radiative collapse is the enhancement of radiative loss by light impurities in the plasma-edge region. Using these four parameters, the likelihood of occurrence of radiative collapse has been estimated. The behavior of plasma at the edge—in particular, the carbon impurities outside the last closed flux surface—has been evaluated using EMC3-EIRENE code for the phase with increasing likelihood, that is, the plasma is getting close to the collapse. It is shown that the radiation caused by the C3 ion, which corresponds to the CIV emission, is enhanced in the region where electron temperature is around 10 eV.