Hull form optimization research based on multi-precision back-propagation neural network approximation model

Abstract

In order to shorten the optimization cycle of ship design optimization and solve the time-consuming problem of computational fluid dynamics (CFD) numerical calculation, this paper proposes a multi-precision back-propagation neural network (MP-BP) approximation technology. Fewer high-precision ship samples and more low-precision ship samples were used to construct an approximate model, back-propagation (BP) neural network was used to train multi-precision samples. So that the approximate model is as close as possible to the real model, and achieving the effect of high-precision approximation model. Subsequently, numerical verification and typical hull form verification are given. Based on CFD and Rankine theory, the multi-objective design optimization framework for ship comprehensive navigation performance is constructed. The multi-objective approximation model of KCS ship is constructed by MP-BP approximation technology, and optimized by particle swarm optimization (PSO) algorithm. The results show that the multi-objective optimization design framework using the MP-BP approximation model can capture the global optimal solution and improve the efficiency of the entire hull form design optimization. It can provide a certain degree of technical support for green ship and low-carbon shipping.