Identification of diffusive transport by means of an incremental approach

Abstract

Predictive models for diffusion in liquids contain still large uncertainties due to a lack of experimental data. Modern measurement techniques offer high-resolution concentration data but data analysis tools are usually designed for scarce data. A new incremental approach to model identification is therefore introduced and applied to the estimation of diffusion coefficients. The identification problem is split here in a sequence of inverse problems following the steps of model development. Thereby, model uncertainty and computational cost are minimized. The concentration dependence of binary diffusion coefficients can now be efficiently established from a single experiment. Furthermore, the robust estimation of the ternary diffusion matrix from a single data set is demonstrated.