Investigating the Nature and Invariance of Field Inversion Based on Transition in a Turbine Cascade

Abstract

The generation of data-driven turbulence models inherently requires the use of a sufficiently large database of high-fidelity reference data from DNS or LES. For technically relevant flows, such data is usually not readily available. However, in many cases there is a significant amount of experimental data available, though data points are mostly few and sparse. An approach which aims at deriving modelling errors by evaluating deviations from a given reference data set is the field inversion method proposed in [1]. Our aim is to verify this method as a tool which gives insight in the cause and nature of a given model’s inconsistencies. We therefore apply field inversion on the turbine cascade T106C for different reference data setups. We find, that for the investigated case, field inversion proved to qualitatively give the right hints towards the expected model correction, when only few data points were used as reference.