• CN:11-2187/TH
  • ISSN:0577-6686

›› 2008, Vol. 44 ›› Issue (12): 173-179.

• 论文 • 上一篇    下一篇

基于关联矩阵与设计结构矩阵的计算模型求解顺序规划

唐敦兵;彭义兵;刘政伟   

  1. 南京航空航天大学机电学院;华中科技大学机械学院
  • 发布日期:2008-12-15

Execution Sequence Planning of Computational Models Based on Incidence Matrix and Design Structure Matrix

TANG Dunbing;PENG Yibing;LIU Zhengwei   

  1. College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics & Astronautics School of Mechanical Engineering, Huazhong University of Science & Technology
  • Published:2008-12-15

摘要: 结合关联矩阵与设计结构矩阵,提出计算模型的求解顺序规划算法。关联矩阵可以描述计算模型与设计参数之间的关系,设计结构矩阵用来表示计算模型之间的依赖关系。基于少数已知设计参数,通过关联矩阵进行输入输出传播,可确定部分计算模型与相关设计参数的关系,并识别出耦合模型集。对耦合模型集进行解耦,使耦合集内计算模型之间的反馈量最小,通过遗传算法确定耦合集内计算模型的最优求解顺序。将耦合集内的计算模型当作一个宏计算模型,与其他的计算模型形成整体设计结构矩阵;通过对整体设计结构矩阵的下三角化,可得到所有计算模型的求解顺序。通过一个飞机重量计算例子,验证了以上提出的算法。

关键词: 关联矩阵, 计算模型, 设计参数, 设计结构矩阵, 遗传算法

Abstract: Based on incidence matrix (IM) and design structure matrix (DSM), an algorithm is proposed to obtain an optimal execution sequence of computational models in order to reduce computational cost and design time. The IM describes the relationship between design variables and equations/models. The DSM has been used to express the dependency relationships between the computational models and also, after manipulation, to produce the solution process. The designer specifies the independent (known) design variables first. Then the variable flow is modeled by using the IM. It determines how the data flows through the models, and also identifies any strongly connected models (SCM). The second step is to arrange all equations/models hierarchically in order to reduce the feedback loops in each of the identified SCMs. A GA-based algorithm is applied for resolving the couplings. Subsequently each SCM is grouped into a macro model to form a global DSM. The global DSM is further rearranged to obtain a lower triangular matrix which defines the final model execution sequence. A simple aircraft sizing example is presented to illustrate the proposed method and algorithm.

Key words: Computational model, Design structure matrix, Design variable, Genetic algorithm, Incidence matrix

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