Investigation into Using CFD for Estimation of Ship Specific Parameters for the SPICE Model for Prediction of Sea Spray Icing: Part 1—The Proposal

Abstract

A machine learning model for prediction of icing on vessels and offshore structures, Spice, was recently developed by Deshpande 2023. Some variables required for the prediction of icing rates in most prediction models, including Spice, such as the spray flux, cannot be easily measured. Existing models estimate these using empirical formulations that have been heavily criticized. Most existing models are also incapable of providing the distribution of icing on the structure. The current study demonstrates a method to estimate the local wind speeds, along with spray duration, spray period, and spray flux at different locations on the surface of a moving vessel. These, along with other easily measurable values of air temperature, water temperature, and salinity, are used to predict the icing rates. The result is a model, dubbed Spice2—an upgrade of the existing Spice model—that is able to provide the icing rates and the distribution of icing on the surface of vessels and other offshore structures. The model was demonstrated with a case study of a totally enclosed lifeboat where icing rates were predicted at different locations on its surface. Successful implementation of a two-phase simulation with a coupled wind–wave domain and a moving vessel was demonstrated. Research into simplification of the currently computationally expensive method is suggested. Validation of the proposed Spice2 model against a full-scale measurement is covered in part 2 of the study.