Bayesian optimization of massive material injection for disruption mitigation in tokamaks

TL;DR

This paper employs Bayesian optimization to analyze disruption mitigation strategies in ITER, focusing on material injection effects, and finds that neon injection at the edge can significantly reduce runaway currents and heat losses.

Contribution

It introduces a Bayesian optimization framework to efficiently explore disruption mitigation scenarios with combined deuterium and neon injection in tokamaks.

Findings

Multi-megaampere runaway currents are inevitable in deuterium-tritium operations.

Edge-deposited neon reduces runaway currents and heat losses.

Bayesian approach improves parameter space mapping and robustness analysis.

Abstract

A Bayesian optimization framework is used to investigate scenarios for disruptions mitigated with combined deuterium and neon injection in ITER. The optimization cost function takes into account limits on the maximum runaway current, the transported fraction of the heat loss and the current quench time. The aim is to explore the dependence of the cost function on injected densities, and provide insights into the behaviour of the disruption dynamics for representative scenarios. The simulations are conducted using the numerical framework DREAM (Disruption Runaway Electron Analysis Model). We show that irrespective of the quantities of the material deposition, multi-megaampere runaway currents will be produced in the deuterium-tritium phase of operations, even in the optimal scenarios. However, the severity of the outcome can be influenced by tailoring the radial profile of the injected material; in particular if the injected neon is deposited at the edge region it leads to a significant reduction of both the final runaway current and the transported heat losses. The Bayesian approach allows us to map the parameter space efficiently, with more accuracy in favorable parameter regions, thereby providing us information about the robustness of the optima.