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Use of simulations to predict lifeboat survivability in extreme waves and the effectiveness of coxswain performed actions

Billard R., Rees R., Veitch B., Re A. S.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2024

Vol. 18 No. 1

233--246

Simulations were used to investigate the performance of lifeboats in high sea states using a virtual wave tank. Numerical simulations were performed in regular and irregular waves to study launch performance in extreme weather conditions. Limitations in launch equipment and the role of the timing of coxswains’ actions were investigated. The study indicated that the lifeboat may not be able to successfully launch when significant wave heights are above 8 m and the lifeboat is launched near the trough of a wave. High initial setback and continuous wave forces result in the vessel being unable to clear away from the launch platform. As wave heights increase, the amount of setback and time to exit the launch area increases. Over 35% of launches resulted in the lifeboat being unable to clear from the launch area when significant wave heights were 10 m or above. The study also identified that delay in completion of actions performed by the coxswain, such as releasing the lifeboat hooks and applying throttle, can increase setback and time to exit the launch area.

Using bayesian networks to model competence of lifeboat coxswains

Billard R., Smith J., Masharraf M., Veitch B.

TransNav : International Journal on Marine Navigation and Safety of Sea Transportation

2020

Vol. 14. no. 3

585--594

The assessment of lifeboat coxswain performance in operational scenarios representing offshore emergencies has been prohibitive due to risk. For this reason, human performance in plausible emergencies is difficult to predict due to the limited data that is available. The advent of lifeboat simulation provides a means to practice in weather conditions representative of an offshore emergency. In this paper, we present a methodology to create probabilistic models to study this new problem space using Bayesian Networks (BNs) to formulate a model of competence. We combine expert input and simulator data to create a BN model of the competence of slow-speed maneuvering (SSM). We demonstrate how the model is improved using data collected in an experiment designed to measure performance of coxswains in an emergency scenario. We illustrate how this model can be used to predict performance and diagnose background information about the student. The methodology demonstrates the use of simulation and probabilistic methods to increase domain awareness where limited data is available. We discuss how the methodology can be applied to improve predictions and adapt training using machine learning.