Description
Edge-localized modes (ELMs) are quasi-periodic edge instabilities in tokamaks, accompanied by the expulsion of heat and particles from the plasma. Large ELMs can heighten the risk of damage to the plasma-facing components. However, under certain conditions, ELMs can exhibit strongly stochastic behavior, showing a mix of relatively small and larger bursts. This complicates the predictability of the effects of ELMs on plasma operation and plasma-wall interaction. It is therefore of key interest to understand the conditions governing the degree of ELM stochasticity.
In this work, we investigate probability distributions of ELM timing and ELM size at JET, as well as their dependence on some of the main operational characteristics. This allows us to exploit information about ELM variability under stationary plasma conditions. Machine learning techniques were developed to robustly detect ELMs under a broad variety of plasma conditions, based on a training set of 16000 manually marked ELMs. More than 1400 JET discharges and over 130000 individual ELMs were analyzed, dating from 2012-2020, after the installation of the ITER-like wall. The inter-ELM time was determined for all ELM events in the database, as well as the drop in the plasma stored energy as a measure of ELM size. Regression analysis was then used to quantify the dependence on machine conditions of various parametric distributions. Going beyond the analysis of averaged ELM properties, we also studied the variance, percentiles, and properties of the distribution tail. In particular, the tail heaviness of the ELM size is an indicator of unaccounted risk, as the released energy may exceed wall tolerances defined on the basis of average ELM behavior. We highlight the influence of plasma current, triangularity and gas fueling rate on distributional properties. Another indicator of risk is the energy released by several consecutive ELMs. We show that the cumulative energy loss due to multiple ELMs is largely affected by heating power and plasma current. Overall, this study provides both qualitative and quantitative insight into the occurrence of rare, potentially impactful ELMs. The code for ELM detection, which can be easily adapted to other types of events, will be published as open source.