An important part of the European activities in the area of controlled thermonuclear fusion is focused on design and R&D toward a demonstration fusion power plant DEMO. With the transition of the pre-conceptual design into the conceptual design, started in 2021, questions regarding design uncertainties and their impact on machine operation have become of increased interest. The framework of integrated data analysis (IDA) allows uncertainty propagation studies and combination of data from multiple sensors or diagnostics. Using Bayesian probabilistic inference, information about the joint probability distribution of a set of plasma quantities of interest is derived, based on multiple sources of data. This way, the propagation can be studied of uncertainties through complicated, nonlinear models that describe the measurement process (forward models). At the same time, synergies in the measurements from multiple diagnostics can be exploited, since each diagnostic contributes with complementary information about the quantities of interest.

This research project involves the development of IDA for DEMO diagnostics. In addition, a very novel aspect of the project concerns the application of Bayesian methods for quantifying uncertainties in the response of the plasma to actuator signals set by the control system. Another key idea behind our research relates to the relatively new development of predictive maintenance for fusion applications. This involves automated, real-time analysis of measurements from sensors and diagnostics using statistical and machine learning techniques, in order to detect and predict anomalous behavior of the plasma or machine components.

In 2023, the objectives are as follows:

• Provide forward models for magnetic probes based on inductive measurements and Hall probes, and perform IDA for reconstruction of plasma current, position and shape.
• Develop techniques for fast reconstruction of Bayesian posterior distributions that can be used in a real-time setting for plasma control purposes.
• Apply and develop machine learning techniques for condition monitoring of plasma-facing components under DEMO-relevant conditions, with a view to estimation of their remaining useful life during plasma operation.