Data-Driven Method of Modeling Sparse Flow Field Data

Abstract

Real-time perception and prediction of flow field have very important application value in aviation and navigation, and pose challenges such as high flow field dimension and less real-time measurement information. To solve such problem, a data-driven flow field modeling method framework is proposed, which realizes real-time reconstruction of online flow field by establishing sparse data and high-dimensional flow field mapping offline. In offline modeling, aimed at the high-dimensional challenge of the flow field, the eigenortho decomposition and other methods are used to reduce the dimensionality of the data and extract the spatial mode of the main flow field. The QR decomposition method is used to mine the modal sensitivity characteristics of the flow field and optimize the measurement point position. Dynamic modal decomposition with time delay significantly reduces the number of measurement points. In the online reconstruction, based on real-time sparse measurement data and data-driven models, the prediction of the current and future full-field flow field is realized. In the test of cylinder wake flow, using this method and using 20 sparse measurement points, the full-field reconstruction error obtained can reach less than 10%.

Keywords

• data-driven
• reduced order model
• dynamic mode decomposition
• location optimization
• sparse data
• flow field reconstruction