• 제목/요약/키워드: Two-point Reciprocal Quadratic Approximation

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구조최적설계시 근사법의 정확도를 이용한 이동한계 전략의 개발 (A development of move limit strategy based on the accuracy of approximation for structural optimization)

  • 박영선;박경진
    • 대한기계학회논문집A
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    • 제21권8호
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    • pp.1218-1228
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    • 1997
  • The move limit strategy is used to avoid the excessive approximation in the structural optimization. The size of move limit has been obtained by engineering experience. Recently, efforts based on analytic methods are performed by some researchers. These methods still have problems, such as prematurity or oscillation of the move limit size. The existing methods usually control the bound of design variables based on the magnitude. Thus, they can not properly handle the configuration variables based on the geometry in the configuration optimization. In this research, the size of move limit is calculated based on the accuracy of approximation. The method is coded and applied to the two-point reciprocal quadratic approximation method. The efficiency is evaluated through examples.

불연속구조물의 배치최적설계를 위한 이점역이차근사법의 개발 (A Development of Two-Point Reciprocal Quadratic Approximation Mehtod for Configuration Optimization of Discrete Structures)

  • 박영선;임재문;양철호;박경진
    • 대한기계학회논문집A
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    • 제20권12호
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    • pp.3804-3821
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    • 1996
  • The configuration optimization is a structural optimization method which includes the coordinates of a structure as well as the sectional properties in the design variable set. Effective reduction of the weight of discrete structures can be obrained by changing the geometry while satisfying stress, Ei;er bickling, displacement, and frequency constraints, etc. However, the nonlinearity due to the configuration variables may cause the difficulties of the convergence and expensive computational cost. An efficient approximation method for the configuration optimization has been developed to overcome the difficulties. The method approximates the constraint functions based onthe second-order Taylor series expansion with reciprocal design variables. The Hessian matrix is approzimated from the information on previous design points. The developed algotithms are coded and the examples are solved.