Sailboat Hull Resistance Dataset and Predictive Model
This document contains a dataset and a predictive model. The dataset is based on three sailboats systematic series with measurement of hull resistance through water: The Delft Series, US Sailing Series and Il Moro di Venezia Series. The data are stored in a Coma Separated Virgula text file (“Sailboat Hull Resistance Dataset V01.csv”) whereas the semicolon has been used as a separator character. The table includes 1018 records corresponding to towing tank tests of the systematic sailboat hull series. Respectively, 702 records are related to the Delft Series, 108 records to the Il Moro di Venezia Series and 208 records to the US Sailing Series. The table possesses 21 fields that are described in detail in the metadata file (“Sailboat Hull Resistance Metadata V01.csv”). The predictive model uses an Artificial Neural Network configured with 8 inputs to predict the hull resistance target variable “Rt/Delta * 10^3”. The inputs of the model are: The Froude number, the Reynolds number and the 8 principal components (PCA) of the following fields: Cp; Cm; Cb; Cw; Lwl/Bwl; Bwl/Tc; Lwl/Tc; Lwl/Vol^(1/3); Lcb/Lwl; Lcf/Lwl; Lcb/Lcf; Sc/Vol^(2/3); Aw/Vol^(2/3); Sc/Aw; Sc/Ax; Ax/Aw. The predictive model uses the PMML 4.2.1 data format (http://dmg.org/pmml/pmml-v4-2-1.html). The model is stored in the pmml file: (“ann_3-16_r2_0.998.pmml”). The neural network has 3 hidden layers of 16 neurons. The details of the step by step procedures to use the predictive model are available in the paper referenced here: https://doi.org/10.1016/j.oceaneng.2022.111642.
Steps to reproduce
The details of the step by step procedures to use the predictive model are available in the paper referenced here: https://doi.org/10.1016/j.oceaneng.2022.111642.