Estimation of Subsea Line Structure Behavior Based on Sequential Data Assimilation with Distributed Sensing

Abstract

In this paper, a method that estimates the real-time behavior of subsea line structures based on sequential data assimilation with distributed strain sensors is proposed. A finite element method is used to represent the behavior of subsea line structures and generates ensemble forecasts regarding unknown parameters. A merging particle filter technique is applied to integrate the observation data with the numerical models to calculate the posterior probability density function. The effectiveness of the proposed method is examined through twin experiments. The presented results validate the proposed method's capability to estimate the current state as well as unknown parameters of subsea line structures. The results suggest the advantage of distributed sensors against pointwise sensing when applied to line structures.