为提高船舶设计质量管理水平,以某满足《协调共同结构规范》(Harmonized Common Structure Rule,HCSR)的巴拿马型散货船为研究对象,利用计划、执行、检查、处理(Plan,Do,Check,Act,PDCA)循环法基本原则对设计质量进行管控,并建立同一船...为提高船舶设计质量管理水平,以某满足《协调共同结构规范》(Harmonized Common Structure Rule,HCSR)的巴拿马型散货船为研究对象,利用计划、执行、检查、处理(Plan,Do,Check,Act,PDCA)循环法基本原则对设计质量进行管控,并建立同一船型设计质量管理体系。此外,对船舶能效指数(Energy Efficiency Design Index,EEDI)提升和结构轻量化设计的研讨方法进行分析。研究成果可为船舶设计质量管理提供一定参考。展开更多
In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of sh...In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.展开更多
基金Supported by the Project of Ministry of Education and Finance (No.200512)the Project of the State Key Laboratory of Ocean Engineering (GKZD010053-10)
文摘In this paper a hybrid process of modeling and optimization, which integrates a support vector machine （SVM） and genetic algorithm （GA）, was introduced to reduce the high time cost in structural optimization of ships. SVM, which is rooted in statistical learning theory and an approximate implementation of the method of structural risk minimization, can provide a good generalization performance in metamodeling the input-output relationship of real problems and consequently cuts down on high time cost in the analysis of real problems, such as FEM analysis. The GA, as a powerful optimization technique, possesses remarkable advantages for the problems that can hardly be optimized with common gradient-based optimization methods, which makes it suitable for optimizing models built by SVM. Based on the SVM-GA strategy, optimization of structural scantlings in the midship of a very large crude carrier （VLCC） ship was carried out according to the direct strength assessment method in common structural rules （CSR）, which eventually demonstrates the high efficiency of SVM-GA in optimizing the ship structural scantlings under heavy computational complexity. The time cost of this optimization with SVM-GA has been sharply reduced, many more loops have been processed within a small amount of time and the design has been improved remarkably.
