Reducing fuel consumption of tankers in waves: Optimising the main dimensions of next generation MR tankers using Monte Carlo simulations in a performance model

Abstract

This thesis presents a novel methodology to assess the performance of ocean going commercial vessels. The purpose is to optimise the hull main dimensions in order to reduce the fuel consumption and thus reduce the environmental impact from shipping. This is part of the shipping industry’s, through the International Maritime Organisation (IMO), target to reach net-zero greenhouse gas emissions by 2050. The optimisation is Monte Carlo based and the performance of each sample is assessed through a speed loss model based on empirical resistance methods and the SHOPERA-project NTUA NTU MARIC (SNNM) method for added resistance in waves. The environmental factors are extracted from hindcast weather data provided by Copernicus Marine. To improve the accuracy of the predicted added resistance in waves, which is in the order of magnitude of 0-10% of the total resistance, different machine learning models are investigated through processing full-scale measurement data from the IMOIIMAX vessels operated by Stena Bulk, to complement the SNNM model, that is developed and regressed for a broad range of vessel types. In this report, the methodology based on the non-improved SNNM is implemented on, and used to propose main dimensions for Stena’s next generation Medium Range (MR) tankers. For the set of weighted routes and weather, the methodology resulted in a design where the model predicted fuel savings of 8.1 % compared to the current generation. "All models are wrong, but some are useful" - George E.P. Box