A knowledge-based computational design tool for determining preliminary stability particulars of naval ships

Abstract

The preliminary design stage of a ship constitutes an important base from which to develop the preliminary ship characteristics to be used as goals in the following stages of the ship design process. Thus, reliable and efficient design tools are required by ship designers to determine ship design particulars that can satisfy various performance measures of stability for safety requirements, as well as resistance, volume, and load capacity to set economic targets. In this study, a robust neural network (NN) structure is established and using principle design data from 22 naval ships (Bartholomew et al. 1992), a reliable design tool for ship designers for determining ship preliminary stability particulars and as well as load capacity is developed. In the NN structure the classical back-propagation (CBA) (Rumelhart et al. 1986) and fast back-propagation (FBA) (Karayiannis and Venetsanopoulas 1991, 1992, 1993) algorithms are employed.