Conveners
Next Fusion Device Concepts: Data Challenges and Design Optimization
- Didier Mazon (CEA Cadarache)
This paper presents a novel uncertainty optimization algorithm for the design of line-of-sight (LOS) systems used in tomographic inversion. By extending Gaussian process tomography from discrete pixel space to continuous function space through Bayesian inference, we introduce an uncertainty function and analyse its typical distributions. We develop an algorithm to minimize the uncertainty,...
Next-generation fusion devices such as DEMO present significant challenges in diagnostic system design due to spatial and cost constraints. Previous work has demonstrated the successful application of Bayesian experimental design to DEMO, optimizing both the placement and orientation of magnetic coils to reduce sensor quantity while maintaining diagnostic accuracy.
In this study, we extend...
Disruption is a catastrophic event in tokamaks and represents a major challenge for future commercial fusion reactors. Although data-driven disruption prediction models trained on a single tokamak have successfully triggered disruption mitigation, obtaining large disruptive datasets for every new device is economically and operationally impractical. Cross-tokamak disruption prediction is...
