A framework for assessing the key statistical parameters of complex flows in choosing appropriate turbulence prediction methods on a quantitative basis is developed. These parameters characterise flow/modelling matching conditions quantified in this work. Matching conditions are important in classifying complex turbulent flows in order to frame best practice for model predictions to inform computational aerodynamics design optimisation in the context of virtual test beds. In the incompressible low Reynolds number shear flows considered here, the boundaries of the 'conforming domain' within which turbulence models are valid need to be defined, based on basic mechanisms of turbulence, and the statistical parameters. This has led to a new guideline ‘localness map’ for standard model applications. Since the choice of turbulence model depends on the complexity of the flows considered, it is useful if systematic sets of the parameters indicate the type of flow. They are that of residence time, the degree of spatial non-locality, the straining, and the non-Gaussianity, each of which is appropriately normalised. It can be demonstrated that the quantified map, in particular that of localness for the shear flows, provides a firm foundation for evaluating a wider range of Underlying Flow Regimes, including locating the Underlying Flow Regimes on the generalised localness modeling map as a framework for best practice guidelines. This work produces 7 sets of quantitative localness-structural parameters, which are used as baseline sets for grouping the Underlying Flow Regimes, and hence it opens the possibility of having complete modelling maps for Application Challenges to assess the need for zonal modelling.

A framework for assessing the key statistical parameters of complex flows in choosing appropriate turbulence prediction methods on a quantitative basis is developed. These parameters characterise flow/modelling matching conditions quantified in this work. Matching conditions are important in classifying complex turbulent flows in order to frame best practice for model predictions to inform computational aerodynamics design optimisation in the context of virtual test beds. In the incompressible low Reynolds number shear flows considered here, the boundaries of the 'conforming domain' within which turbulence models are valid need to be defined, based on basic mechanisms of turbulence, and the statistical parameters. This has led to a new guideline ‘localness map’ for standard model applications. Since the choice of turbulence model depends on the complexity of the flows considered, it is useful if systematic sets of the parameters indicate the type of flow. They are that of residence time, the degree of spatial non-locality, the straining, and the non-Gaussianity, each of which is appropriately normalised. It can be demonstrated that the quantified map, in particular that of localness for the shear flows, provides a firm foundation for evaluating a wider range of Underlying Flow Regimes, including locating the Underlying Flow Regimes on the generalised localness modeling map as a framework for best practice guidelines. This work produces 7 sets of quantitative localness-structural parameters, which are used as baseline sets for grouping the Underlying Flow Regimes, and hence it opens the possibility of having complete modelling maps for Application Challenges to assess the need for zonal modelling.