Papers by Author: Xue Zheng Chu

Abstract: Ship design is a complex engineering effort required excellent coordination between the various disciplines and essentially applies iteration to satisfy the relevant requirements, such as stability, power, weight, and strengths. Through, all-in-one Multidisciplinary Design Optimization (MDO) approach is proposed to get the optimum performance of the ship considering three disciplines, power of propulsion, ship loads and structure. In this research a Latin Hypercube Sampling (LHS) is employed to improve the space filling property of the design and explore it to sample data for covering the design space. To avoid the problem of huge calculation time and saving the develop time, a quadratic Response Surface Method (RSM) is adopted as an approximation model to study the relation between a set of design variables and the system output for solving the system design problems. A genetic algorithm (GA) is adopted as search technique used in computing to find exact or approximate solutions to optimize and search problems and appropriate design result in MDO in ship design. Finally, the validity of the proposed approach is proven by a case study of a bulk carrier.

Abstract: This paper concentrates on the computational challenge in multidisciplinary design optimization (MDO) and a comprehensive strategy combining enhanced collaborative optimization (ECO) and kriging approximation models is introduced. In this strategy, the computational and organizational advantages of original collaborative optimization (CO) are inherited by ECO, which can satisfy the strengthened consistency requirements. Kriging approximation models are constructed to replace high-fidelity simulation models in individual disciplines and reduce the expensive computational cost in practical MDO problems. The proposed methodology is demonstrated by solving the classical speed reducer design problem. The better results indicate that ECO using kriging approximation models can achieve a considerable reduction of computational expense while guaranteeing the accuracy of optimal solutions with efficient convergence.