Reduced order modeling of fluid flows using convolutional neural networks

Reduced order modeling of fluid flows using convolutional neural networks

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Application of machine learning is currently one of the hottest topics in the fluid mechanics field.While machine learning seems to have a great possibility, its limitations should also be clarified.In our research group, we valhalla axys have started a research project to construct a nonlinear feature extraction method by applying machine learning techniques to big data of fluid flow, i.e., extracting the low-dimensional nonlinear modes essential to the unsteady flow phenomena and deriving the governing equations for such low-dimensionalized dynamics.

This self-review article is a focused but extended version of the keynote lecture given by the author at the 7th International Conference on Jets, Wakes and Separated Flows (ICJWSF2022).We will first introduce the use of a convolutional neural network (CNN) to learn the temporal evolution of cross-sectional velocity field in a turbulent channel flow.Subsequently, we also consider an application of CNN for extraction of low-dimensional dynamics for flow around a bluff body accompanying vortex shedding and our preliminary attempt to caruso milk thistle use the extracted low-dimensional dynamics for an advanced design of flow control.