Data-Driven Techniques for Analysis of Turbulent Flows

Explore data-driven techniques for analyzing turbulent flows in this 52-minute talk by Akhil Nekkanti from CalTech. Delve into spectral proper orthogonal decomposition (SPOD) and its extensions for low-rank reconstruction, denoising, and frequency-time analysis. Discover applications in gappy-data reconstruction and intermittency of coherent structures in turbulent flows. Learn about a novel convolution-based strategy for frequency-time analysis and its application to turbulent jet data. Examine bispectral mode decomposition (BMD) for extracting flow structures linked to nonlinear triadic interactions. Gain insights into reduced-order modeling, hydrodynamic stability, aeroacoustics, and turbulent flows from an expert in high-fidelity numerical simulations and data-driven techniques for flow control and physics discovery.

DDPS | “Data-driven techniques for analysis of turbulent flows”