Structural characterization of wind-sheared turbulent flow using self-organized mapping

Abstract

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.