Development and simulation of multi-diagnostic Bayesian analysis for 2D inference of divertor plasma characteristics

Autor:	O. Myatra, K. J. Gibson, Bruce Lipschultz, Matthew Carr, C. Bowman, Kevin Verhaegh, S. Orchard, J. R. Harrison
Rok vydání:	2020
Předmět:	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
Zdroj:	Plasma Physics and Controlled Fusion. 62:045014
ISSN:	1361-6587 0741-3335
DOI:	10.1088/1361-6587/ab759b
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cb031f014ae7dc7f4c50fa3810317d9f https://doi.org/10.1088/1361-6587/ab759b Zobrazit plný text záznamu