Advanced data-mining tool for feature detection from direct numerical simulations

While DNS of complex flow phenomena is a routine practice nowadays, the analysis tools lack the sophistication of utilizing the abundance of data generated, that requires physics-based data extraction and feature-detection protocols. The current effort presents an automated and intelligent co-processing datamining framework, that detects and tracks flow features of significance in DNS calculations. The salient features of the proposed framework are a) wavelet based multi-resolution data compression algorithm for extracting regions of interest, b) intelligent data-accumulation and monitoring methods for assessing specific terms in the governing equations of turbulent and aero-acoustic applications, and c) efficient featuredetection algorithms for coherent structures. The advantages of the proposed framework include coprocessing of the data allows access to transient correlations that are unavailable for post-processing tools, the physics-based choice of the mined-data addressing the deficiencies of the turbulent and aero-acoustic modeling, and feature detection and data extraction. This approach is efficient due to scalable algorithms combining wavelet-filters and Fourier-transforms. This paper demonstrates the applicability of this tool to representative flow fields in turbulence and aero-acoustic applications.