Thermal problem solution using a surrogate model clustering technique

The thermal problem defined for the validation challenge workshop involves a simple one-dimensional slab geometry with a defined heat flux at the front face, adiabatic conditions at the rear face, and a provided baseline predictive simulation model to be used to simulate the time-dependent heatup of the slab. This paper will discuss a clustering methodology using a surrogate heat transfer algorithm that allows propagation of the uncertainties in the model parameters using a very limited series of full simulations. This clustering methodology can be used when the predictive model to be run is very expensive, and only a few simulation runs are possible. A series of time-dependent statistical comparisons designed to validate the model against experimental data provided in the problem formulation will also be presented, and limitations of the approach discussed. The purpose of this paper is to represent methods of propagation of uncertainty with limited computer runs, validation with uncertain data, and decision-making under uncertainty. The final results of the analysis indicate that the there is approximately 95% confidence that the regulatory criteria under consideration would be failed given the high level of physical data provided.