Model Data Fusion: developing Bayesian inversion to constrain equilibrium and mode structure
Abstract
Recently, a new probabilistic "data fusion" framework based on Bayesian principles has been developed on JET and W7-AS. The Bayesian analysis framework folds in uncertainties and inter-dependencies in the diagnostic data and signal forward-models, together with prior knowledge of the state of the plasma, to yield predictions of internal magnetic structure. A feature of the framework, known as MINERVA (J. Svensson, A. Werner, Plasma Physics and Controlled Fusion 50, 085022, 2008), is the inference of magnetic flux surfaces without the use of a force balance model. We discuss results from a new project to develop Bayesian inversion tools that aim to (1) distinguish between competing equilibrium theories, which capture different physics, using the MAST spherical tokamak; and (2) test the predictions of MHD theory, particularly mode structure, using the H-1 Heliac.
Cite
@article{arxiv.1002.3189,
title = {Model Data Fusion: developing Bayesian inversion to constrain equilibrium and mode structure},
author = {M. J. Hole and G. von Nessi and J. Bertram and J. Svensson and L. C. Appel and B. D. Blackwell and R. L. Dewar and J. Howard},
journal= {arXiv preprint arXiv:1002.3189},
year = {2010}
}
Comments
submitted to Journal of Plasma Fusion Research 10/11/2009