| Proper Orthogonal Decomposition was heralded as an objective means of extracting coherent structures from turbulent flows. Prior to its introduction, coherent structures were subjectively defined and discussed among experts. Since its introduction many reduced order models have been developed with inconsistent and often flow dependent validation procedures. This work sets up a framework for a data driven approach to validation of reduced order models derived from steady turbulent flows. Here it is proposed that the 'goodness' of a model can be scored by how similar experimental and simulated data move through the model space. This is achieved by generating a Markov model for both data sets, using clustering techniques and maximum likelihood estimates. Results show increasing scores correlate with improved turbulent kinetic energy and modal amplitude for 3 data sets and 14 models. Additionally the generation of a surrogate Markov model can be used to identify missing dynamics in a simulation. |
