Naïve Bayesian belief network modeling is applied to direct numerically simulated imagery of oscillatory sedimentladen flow to illustrate the feasibility of creating a system model which captures the statistical interrelationship of the surface layer sediment concentration, pressure, and vertical velocity eddy scales with the sub-surface Reynolds stress. From a prognostic reasoning viewpoint, preliminary model results suggest that large sediment concentration eddy scales may result from the application of large positive Reynolds stress. However, from a diagnostic reasoning viewpoint, initial results suggest that robustly inferring sub-surface boundary layer stress from surface sediment concentration eddy scales may be a difficult task. The model formulism used allows for the ability to statistically characterize flow structure at depth from observations taken across a surface boundary layer, making the results relevant to image analysis at the airsea interfacial boundary layer in large-scale coastal and riverine systems.
A nonlinear cluster analysis algorithm is used to characterize the spatial structure of a wind-sheared turbulent flow obtained from the direct numerical simulation (DNS) of the three-dimensional temperature and momentum fields. The application of self-organizing mapping to DNS data for data reduction is utilized because of the dimensional similitude in structure between DNS data and remotely sensed hyperspectral and multispectral data where the technique has been used extensively. For the three Reynolds numbers of 150, 180, and 220 used in the DNS, self-organized mapping is successful in the extraction of boundary layer streaky structures from the turbulent temperature and momentum fields. In addition, it preserves the cross-wind scale structure of the streaks exhibited in both fields which loosely scale with the inverse of the Reynolds number. Self-organizing mapping of the along wind component of the helicity density shows a layer of the turbulence field which is spotty suggesting significant direct coupling between the large and small-scale turbulent structures. The spatial correlation of the temperature and momentum fields allows for the possibility of the remote extrapolation of the momentum structure from thermal structure.