1. Development and simulation of multi-diagnostic Bayesian analysis for 2D inference of divertor plasma characteristics
- Author
-
O. Myatra, K. J. Gibson, Bruce Lipschultz, Matthew Carr, C. Bowman, Kevin Verhaegh, S. Orchard, and J. R. Harrison
- Subjects
Physics ,Light nucleus ,Tokamak ,Divertor ,Bayesian probability ,Iter tokamak ,Inference ,Plasma ,Condensed Matter Physics ,computer.software_genre ,Bayesian inference ,law.invention ,Nuclear Energy and Engineering ,law ,Data mining ,computer - Abstract
We present results of the design, implementation and testing of a Bayesian multi-diagnostic inference system which combines various divertor diagnostics to infer the 2D fields of electron temperature T e , density n e and deuterium neutral density n 0 in the divertor. The system was tested using synthetic diagnostic measurements derived from SOLPS-ITER fluid code predictions of the MAST-U Super-X divertor which include appropriate added noise. Two SOLPS-ITER simulations in different states of detachment, taken from a scan of the nitrogen seeding rate, were used as test-cases. Taken across both test-cases, the median absolute fractional errors in the inferred electron temperature and density estimates were 10.3% and 10.1% respectively. Differences between the inferred fields and the test-cases were well explained by solution uncertainty estimates derived from posterior sampling. This work represents a step toward a larger goal of obtaining a quantitative, 2D description of the divertor plasma state directly from experimental data, which could be used to gain better understanding of divertor physics phenomena.
- Published
- 2020
- Full Text
- View/download PDF