Application of regularization method with sparsity prior in riser vortex-induced vibration reconstruction

Abstract

In the experimental investigation or in-situ monitoring of the vortex-induced vibration (VIV) of offshore risers, it is often necessary to reconstruct the full-field structural response based on discrete observations. Conventionally, this can be done using the modal decomposition method. However, the method can be invalid with fewer measurements than unknown modes and is unstable when the data contains measurement noises. This study presents a novel sparse modal decomposition method based on the compressed sensing theory and the Compressive Sampling Matching Pursuit (CoSaMP) algorithm. It formulates the reconstruction problem in a regularization framework with a prior related to the sparsity of the modal coordinates, which differs from the classic method that seeks a least-square solution. Verification tests demonstrate that our method outperforms the classic method regarding accuracy and stability, especially when the measurements are noisy and insufficient, highlighting its potential applications in engineering.