Extraction and analysis of flow features in planar synthetic jets using different machine learning techniques

Abstract

Synthetic jets are useful fluid devices with several industrial applications. In this study, we use the flow fields generated by two synchronously operating synthetic jets and simulated using direct numerical simulations. These flow fields are characterized by a jet Reynolds number, Re=100, 150, and 200, and a Strouhal number, St=0.03. We benchmark four different dimensionality reduction techniques: (1) higher-order dynamic mode decomposition (HODMD), (2) proper orthogonal decomposition, (3) vector quantization via principal component analysis (VQPCA), and (4) linear autoencoders. These techniques are often used in generating reduced-order models (ROMs). The performances of these techniques are compared (i) in terms of their ability to accurately reconstruct the high-dimensional flow fields from their low-dimensional manifolds and (ii) in terms of their ability to extract meaningful low-dimensional patterns/features/structures that best describe the main dynamics of the synthetic jets. The similarity between the extracted features is also quantitatively assessed with the help of Procrustes analysis, showing how manifolds from different techniques become more similar when a larger number of modes are retained. Accurate reconstruction and model complexity (or interpretability) are often two counter-balancing objectives. In this comparative study, we found that among the four techniques, VQPCA has clear advantages for developing accurate ROMs, while HODMD is useful for understanding the dynamics of synthetic jets, providing additional information that is not readily available with other methods.