Wavelet adaptive proper orthogonal decomposition for large-scale flow data

Abstract The proper orthogonal decomposition (POD) is a powerful classical tool in fluid mechanics used, for instance, model reduction and extraction of coherent flow features. However, its applicability to high-resolution data, as produced by three-dimensional direct numerical simulations, limited owing computational complexity. Here, we propose wavelet-based adaptive version the POD (the wPOD), order overcome this limitation. amount data be analyzed reduced compressing them using biorthogonal wavelets, yielding sparse representation while conveniently providing control compression error. Numerical analysis shows how distinct error contributions wavelet truncation can balanced under certain assumptions, allowing us efficiently process from simulations problems. Using synthetic academic test case, compare our algorithm with randomized singular value decomposition. Furthermore, demonstrate ability method analyzing two-dimensional wake generated flapping insect computed simulation.