Probabilistic Inference of Surface Heat Flux Densities from Infrared Thermography

Abstract

In nuclear fusion research, based on the magnetic confinement, the determination of the heat flux density distribution onto the plasma facing components is important. The heat load poses the threat of damaging the components. The heat flux distribution is a footprint of the transport mechanisms in the plasma, which are still to be understood. Obtaining the heat flux density is an ill-posed problem. Most common is a measurement of the surface temperature by means of infrared thermography. Solving the heat diffusion equation in the target material with measured temperature information as boundary condition allows to determine the surface heat load distribution. A Bayesian analysis tool is developed as an alternative to deterministic tools, which aim for fast evaluation. The probabilistic evaluation uses adaptive kernels to model the heat flux distribution. They allow for self-consistent determination of the effective Degree of Freedom, depending on the quality of the measurement. This is beneficial, as the signal-to-noise ratio depends on the surface temperatures, ranging from room temperatures up to the melting point of tungsten.