Research | Infusion - Nuclear Fusion UGent

The research unit Nuclear Fusion at Ghent University is at the forefront of advancing fusion as a clean, safe, and virtually limitless energy source. Our work focuses on developing cutting-edge numerical methods to analyze and model the complex physics and technology underlying fusion devices.

In the rapidly evolving field of fusion data science, we leverage modern approaches such as Bayesian inference and machine learning. These techniques enable advanced pattern recognition, improved modeling of plasma fluctuations and the creation of digital twins for fusion systems.

Our expertise in physics modeling spans both magnetohydrodynamics and kinetic theory. A key strength of the group is the development of an in-house Vlasov–Maxwell code, supporting high-fidelity simulations of plasma behavior.

Research topics

Fusion digital twins

Digital twins of fusion devices or components can play a major role in accelerating R&D for fusion energy. This will allow experiment preparation and component testing in a digital environment, and will enhance predictive accuracy for real-time control.

Bayesian modeling of fusion diagnostics

We use Bayesian probabilistic inference for integrated analysis of data from multiple sensors (diagnostics) in fusion devices. This type of "data fusion" allows more reliable measurement of plasma conditions with uncertainty estimates.

Anomaly detection and predictive maintenance

Continuous real-time monitoring of the condition of a fusion power plant will be extremely important. We use machine learning techniques to monitor various components in fusion devices and for predicting possible failures.

Probabilistic characterization of stochastic plasma phenomena

We use probabilistic methods for characterizing stochasticity in fusion plasmas, like plasma instabilities and fluctuations. The challenge is to determine the plasma properties and machine design parameters that influence the corresponding probability distributions, reflecting the underlying physics.

Advanced regression analysis for estimation of fusion scaling laws

Scaling laws explain dependencies of key plasma parameters and contribute to the design of new fusion devices. They are estimated from complex databases by means of sophisticated statistical techniques. Our group plays a leading role in this domain.

Kinetic modeling

We are developing a finite-volume Vlasov-Maxwell code that will be able to accurately model the plasma in the scrape-off layer of fusion plasmas without statistical noise.

Europe is at the forefront of developing one of the most promising long-term energy options: fusion power.

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