Multi-parameter stochastic inversion for first and second moment mass properties of a model-scale ship with topside ice accumulation

Abstract

This work investigates the indirect monitoring of 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 within the Arctic, where ice accumulation is a concern. Within such contexts, the actual (i.e., current) first and second moment properties of the vessel, including accumulated topside icing, must be considered within the associated equations of motion for a given ship. By leveraging instrumentation such as an existing on-board inertial measurement unit (IMU), in conjunction with existing seakeeping software, the proposed framework recovers a statistical description of two mass properties, simultaneously. The subsequent stochastic inverse problem is solved using the proposed framework in arriving at two mass properties in particular: 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 the Standard Ship Motion Program (SMP95) software in order to build a posterior distribution for the desired mass properties. Experimental model results for Research Vessel (R/V) Melville are used to validate the proposed framework. Six different configurations, including one case of no icing and five cases of topside icing, are investigated within the context of this framework in order to invert for their respective roll gyradii and vertical centers of gravity. Icing configurations include both asymmetric and symmetric ice accumulation under moderate to heavy icing conditions. Recommendations concerning strategies for applying the proposed framework are offered.