Structural Analysis of Turbulent Boundary Layers
We leverage integral measurements of a passive scalar to identify structurally important velocity scales in boundary layers
Planar and Stereo Particle Image Velocimetry (PIV) measurements are performed in a thermal boundary layer with simultaneous integral measurements of the density gradient across the boundary layer height. The boundary layer is heated such that temperature acts as a passive scalar and a second laser is used to measure density gradients. Previous work on the topic  revealed that large density gradients are linked with specific patterns of coherent structures in both streamwise and wall-normal velocity fluctuations. Specifically, large wall-normal velocity structures extending across the entire boundary layer height were identified and it was postulated that they result from an average of smaller scales, residing at different wall-normal locations, all contributing to the same density change. The proposed model also included a combination of large- and small-scale streamwise velocity modes, varying in the wall-normal direction. Preliminary results from ongoing work support this model and show that, if a second condition on the vertical location of the identified features is imposed, one can indeed extract velocity structures that are localized in the wall-normal sense and convect with different velocities, while a multi-scale behavior of the streamwise fluctuations is also observed (Figure 1). Stereo PIV data in the cross-flow plane of the boundary layer will help unravel the spanwise variation of these features and provide a more complete picture of the phenomena they represent.
Figure: Planar PIV measurements. Streamwise (top) and wall-normal (bottom) velocity fluctuations when conditioned on a large negative streamwise density gradient. The conditional averaging is performed over velocity fields where, only structures residing in locations y<0.2δ (left), 0.3δ
Angeliki Laskari, Theresa Saxton-Fox, Beverley McKeon
ONR Grant # N00014-17-1-3022
- Saxton-Fox (2018), PhD Thesis, Caltech.