Ship hull resistance estimation using multivariate statistics - Pala, Ahmet. (Thesis (M.S.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2020., 2020)
The biggest cost in shipbuilding belongs to the main engine and propeller systems to be used. The selection of these equipment to be used is made according to the resistance values of the ship. Since these are very expensive systems, it is critical to determine the resistance values of the ships precisely. For this reason, many studies have been done on estimating the resistance values of ships. The performances of these studies are measured by comparing the estimations with the towing tank test results. Within the scope of this thesis study, the towing tank test reports of a total of 58 different cargo ships are examined in detail and the needed data are extracted from these reports. Then, the hull resistance values of these ships are estimated with different statistical approaches. First, the problem is handled using a generalized linear model, and then predictions are made with artificial neural networks, which have been used many times in the literature. After all, because of the longitudinal nature of the available data, the problem is addressed using mixed effect linear models. Finally, hull resistance values are estimated by using generalized linear mixed models, which are the combination of generalized linear and mixed effect linear models. Comparisons are made among the completed statistical models using the leave one ship out cross validation technique. In addition, the results of these models are compared with the Holtrop & Mennen method, which is widely used in this field.
Keywords: Neural networks (Computer science); Machine learning.