Modeling with Regression Analysis and Artificial Neural Networks the Resistance and Trim of Series 50 Experiments with V-Bottom Motor Boats

Abstract

Mathematical representations for the resistance, trim, and wetted length of the Experimental Model Basin Series 50 have been developed using conventional regression analysis techniques as well as artificial neural networks. Series 50 is a standard series of 20 V-bottomed motor boats tested in 1941. These hulls could be representative of today's semidisplacement hulls. Recently, the series has been reanalyzed and published using contemporary planing coefficients, enabling resistance prediction in design stages. In the present study, mathematical representations are developed for the Series 50 as an alternative to using charts or data tables. Two methods are used, regression analysis and artificial neural networks. This study provides a useful resistance prediction method for designers and an opportunity to compare and contrast regression analysis and artificial neural networks applied to standard series. The main finding of the study is that both techniques were capable of developing stable and accurate models. A detailed quantification of the differences between methods is provided.