Wavelet analysis of high-speed transition and turbulence over a flat surface

This paper presents a study of high speed boundary layers using the wavelet method. We analyze direct numerical simulation data for high-speed, compressible transitional, and turbulent boundary layer flows using orthogonal anisotropic wavelets. The wavelet-based method of extraction of coherent structures is applied to the flow vorticity field, decomposed into coherent and incoherent contributions using thresholding of the wavelet coefficients. We show that the coherent parts of the flow, enstrophy spectra, are close to the statistics of the total flow, and the energy of the incoherent, noise-like background flow is equidistributed. Furthermore, we investigate the distribution of the incoherent vorticity in the transition and turbulent regions and examine the correlation with the near-wall pressure fluctuations. The results of our analysis suggest that the incoherent vorticity part is not a random "noise"and correlates with the actual noise emanating from inside the boundary layer. This could have implications regarding our understanding of the physics of compressible boundary layers and the development of engineering models

Approximating turbulent and non-turbulent events with the Tensor Train decomposition method

Low-rank multilevel approximation methods are often suited to attack high-dimensional problems successfully and they allow very compact representation of large data sets. Specifically, hierarchical tensor product decomposition methods, e.g., the Tree-Tucker format and the Tensor Train format emerge as a promising approach for application to data that are concerned with cascade-of-scales problems as, e.g., in turbulent fluid dynamics. Beyond multilinear mathematics, those tensor formats are also successfully applied in e.g., physics or chemistry, where they are used in many body problems and quantum states. Here, we focus on two particular objectives, that is, we aim at capturing self-similar structures that might be hidden in the data and we present the reconstruction capabilities of the Tensor Train decomposition method tested with 3D channel turbulence flow data

General-relativistic Model of Magnetically Driven Jet

The general scheme for the construction of the general-relativistic model of the magnetically driven jet is suggested. The method is based on the usage of the 3+1 MHD formalism. It is shown that the critical points of the flow and the explicit radial behavior of the physical variables may be derived through the jet ``profile function."Comment: 12 pages, LaTex, no figure