Design Optimization of the Lines of the Bulbous Bow of a Hull Based on Parametric Modeling and Computational Fluid Dynamics Calculation

Weilin Luo,Linqiang Lan

doi:10.3390/mca22010004

Weilin Luo, Linqiang Lan

Open Access

https://doi.org/10.3390/mca22010004

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Journal: Mathematical & computational applications	Publication Date: Jan 16, 2017
Citations: 6	License type: CC BY 4.0

Affiliation: Fuzhou University

Abstract

To reduce the ship wave-making resistance, the lines of the bulbous bow of a hull are optimized by an automatic optimization platform at the ship design stage. Parametric modeling was applied to the hull by using non-uniform rational basis spline (NURBS). The Rankine-source panel method was used to calculate the wave-making resistance. A hybrid optimization strategy was applied to achieve the optimization goal. A Ro-Ro ship was taken as an example to illustrate the optimization method adopted, with the objective to minimize the wave-making resistance. The optimization results show that wave-making resistance obviously reduces and the wave-shape of the near bow becomes gentle after the lines of the bulbous bow of the hull are optimized, which demonstrates the validity of the proposed optimization design strategy.

Highlights

Design of ship hull forms is one of the most important contents of the general ship design
During the last several decades, many optimization algorithms have been proposed such as successive quadratic programming (SQP) [17], genetic algorithm (GA) [18], simulated annealing (SA) [19], particle swarm optimization (PSO) [20], infeasibility driven evolutionary algorithm (IDEA) [21], and quasi-Newton method [10], among others
The combination of computational fluid dynamics (CFD) and computer aided design (CAD) improves the quality of ship hull form design

Summary

Introduction

Design of ship hull forms is one of the most important contents of the general ship design. During the last several decades, many optimization algorithms have been proposed such as successive quadratic programming (SQP) [17], genetic algorithm (GA) [18], simulated annealing (SA) [19], particle swarm optimization (PSO) [20], infeasibility driven evolutionary algorithm (IDEA) [21], and quasi-Newton method [10], among others These algorithms can be categorized into gradient-based and derivative-free methods [22].

Parametric Modeling of Hull Forms

Optimization Algorithms

Design Variable

Conclusions