Neural network surrogates of Bayesian diagnostic models for fast inference of plasma parameters.

We present a framework for training artificial neural networks (ANNs) as surrogate Bayesian models for the inference of plasma parameters from diagnostic data collected at nuclear fusion experiments, with the purpose of providing a fast approximation of conventional Bayesian inference. Because of the complexity of the models involved, conventional Bayesian inference can require tens of minutes for analyzing one single measurement, while hundreds of thousands can be collected during a single plasma discharge. The ANN surrogates can reduce the analysis time down to tens/hundreds of microseconds per single measurement.

First results from the implementation of the ITER diagnostic residual gas analyzer prototype at Wendelstein 7-X.

Fusion reactors and long pulse fusion experiments heavily depend on a continuous fuel cycle, which requires detailed monitoring of exhaust gases. We have used a diagnostic residual gas analyzer (DRGA) built as a prototype for ITER and integrated it on the most advanced stellarator fusion experiment, Wendelstein 7-X (W7-X). The DRGA was equipped with a sampling tube and assessed for gas time of flight sample response, effects of magnetic field on gas detection and practical aspects of use in a state of the art fusion environment.

First Observation of a Stable Highly Dissipative Divertor Plasma Regime on the Wendelstein 7-X Stellarator.

For the first time, the optimized stellarator Wendelstein 7-X has operated with an island divertor. An operation regime in hydrogen was found in which the total plasma radiation approached the absorbed heating power without noticeable loss of stored energy. The divertor thermography recorded simultaneously a strong reduction of the heat load on all divertor targets, indicating almost complete power detachment.