Stochastic Inversion Framework to Monitor Evolving Mass Properties of a Ship at Sea during Arctic Operations

Abstract

This work investigates the indirect monitoring of Arctic ice accretion on ship surfaces using a stochastic inversion framework. An accurate assessment of a ship’s mass properties during operation is an important concern for ships traveling in adverse conditions. Specifically, in the Arctic, the risk of ice accumulation on the topside of the ship is heightened. Within such contexts, the actual, or current, first and second moment properties of the vessel, including accumulated topside icing, become critical in the associated equations of motion for a given ship. By leveraging an existing on-board inertial measurement unit in conjunction with existing seakeeping software, the framework here recovers a posterior distribution of a single mass property. The inverse problem is demonstrated with two mass properties: the vertical center of gravity (a first moment mass property), and the roll gyradius (a second moment mass property). The inversion scheme requires two main inputs: an observed ground truth for the roll period, and an associated signal-to-noise ratio for the roll period measurement. The framework applies a Markov chain Monte Carlo (MCMC) inversion scheme, implemented in Python, that leverages Standard Ship Motion Program (SMP95) software in order to build a posterior distribution. Experimental model results from Research Vessel (R/V) Melville – Model 5748 provide the necessary inputs to the inversion scheme. Six different configurations, including one case of no icing and five cases of topside icing, are investigated within the context of this framework at full-scale from model-scale in order to invert for the six respective roll gyradii and vertical center of gravities. Icing configurations include both asymmetric and symmetric ice accumulation under moderate to heavy icing conditions.