A compressed sensing based framework for surface pressure field reconstruction from sparse measurement

Traditional experimental methods usually measure the aerodynamic load characteristics of an object by deploying a large number of pressure sensors on its surface, which are often challenging to economically and efficiently obtain accurate surface pressure distribution due to limitation imposed by experimental space, the complexity of geometry, and the cost of measurement instruments. To address this, a compressed sensing (CS) based framework has been proposed in this paper to investigate the reconstruction of the original surface pressure field from extremely sparse measurement data. The proposed framework integrates the generalized proper orthogonal decomposition method for flow field dimensionality reduction, the CS technique for accurate reconstruction of the original signal, and the improved particle swarm optimization algorithm for optimizing sensor placement strategies. Unlike image and unsteady flow field reconstructions, the method presented in this paper has been successful in reconstructing surface pressure fields of high-speed train under various conditions. Based on the accurate reconstruction of surface pressure fields, further aerodynamic load data can be obtained. Additionally, this paper optimizes the traditional pressure sensor layout using a particle swarm optimization method, which not only improves reconstruction accuracy but also significantly reduces the deployment of redundant sensors. Moreover, traditional point selection strategies based on experience can still be incorporated into the pressure sensor layout scheme and effectively reduce reconstruction errors under crosswind conditions. Comparison of the results showed that the proposed framework can accurately and efficiently reconstruct the surface flow field of three-dimensional complex-shaped objects from sparse measurement.