On pressure modes in ice-induced vibrations using multivariate analysis

Abstract

Pressures at the ice-structure interface during model-scale ice-structure interaction are often measured with tactile sensors. Resulting datasets usually include large volume of data along with some measurement error and noise; therefore, it is inherently hard to extract the hidden fluctuating pressures in the system. Identifying the deterministic pressure fluctuation in ice-induced vibrations is essential to understand this complex phenomenon better. In this paper, we discuss the use of two different multivariate analysis techniques to decompose an ensemble of measured pressure data into spatiotemporal modes that gives insights into pressure distributions in ice-induced vibrations. In particular, we use proper-orthogonal decomposition (POD) and inexact robust principal component analysis (IRPCA) in conjunction with measurements of intermittent crushing at different ice speeds. Both decompositions show that most of the energy is captured in a ten-dimensional space; however, the corresponding eigenvalues are different between the decompositions. While POD-based modes have