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P: Fachverband Plasmaphysik

P 5: Helmholtz Graduate School II

P 5.3: Vortrag

Montag, 5. März 2018, 17:05–17:30, A 0.112

Bayesian Evaluation of Infrared Thermography determining Surface Heat Flux Densitie — •Dirk Nille, Udo von Toussaint, Micheal Faitsch, Bernhard Sieglin, and the ASDEX Upgrade Team — Max-Planck-Institute for Plasma Physics, Boltzmannstr. 2, D-85748 Garching, Germany

In fusion research the determination of the heat flux distribution onto the material surrounding the plasma is crucial, as power exhaust is a major challenge in the development of a future fusion power plant. Infrared thermography provides spatially and temporally resolved data for this purpose. This is an inverse problem, where the result of the quantity of interest is observed and the cause has to be reconstructed.

Basis is to solve the heat diffusion equation in the target material with the surface temperature as boundary condition, given by measurements. A direct evaluation is in use for decades by deterministic codes, developed for fast evaluation of data. Standard deviation during quasi-static conditions is used as error bars.

Bayesian evaluation is based on a forward model – describing the response of the physical object, the target material – and a model describing the quantity of interest in order to find the most probable cause leading to the measurement. For this purpose adaptive kernel – a multi-resolution model – are used to describe the heat flux density distribution. Taking into account the known experimental uncertainty yields reconstructions of better quality and reliable credibility ranges.

This allows more detailed analysis about the plasma transporting heat from the confined area to the observed target material.