• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.12 no.4
• /
• pp.551-561
• /
• 1999

본 논문에서는 쉘 최적화에 대한 연구 결과를 기술하였다. 본 연구의 주목적은 쉘 구조물의 최적형상과 두께 분포를 찾는데 있다. 쉘의 변형에너지를 목적함수로 사용하고 초기 쉘의 부피를 제약조건을 고려하였다. 본 연구에서는 Computer-Aided Geometric Design (CAGD) 기법을 이용하여 쉘의 형상과 그 두께 분포를 표현하였고 쉘의 변형에너지를 측정하기 위해서 가변형 도를 채용한 퇴화 쉘 요소(Degenerated Shell Element)를 도입하였다. 최적 값을 구하기 위해서 세 가지 수학적 프로그래밍 기법을 제공하는 프로그램 DOT를 사용하였다. 마지막으로 새로이 개발된 쉘 최적화시스템의 효율성을 최적화예제로써 증명하였다.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.13 no.2
• /
• pp.221-230
• /
• 2000

Sweep geometric models are based on the notion of moving a curve, surface or solid along some path. Sweeping allows definition of prismatic shell surfaces in a simple way, This paper describes an application of sweep geometric models for the optimization of prismatic shells. This geometric model is integrated with finite element formulations. A nine-node degenerated shell element is adopted to calculate the response of prismatic shells. Several examples we presented to demonstrate the process of optimization. From numerical examples, it is observed that sweep geometric models provide an efficient and reliable way of obtaining optimal solutions for a large class of prismatic shell structures.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.32 no.4
• /
• pp.317-326
• /
• 2008

The main purpose of the present study is to perform shape optimizations of transonic compressor blade in order to enhance its performance. In this study, the Latin hypercube sampling of design of experiments and the weighted average surrogate model with the help of a gradient based optimization algorithm are used within design space by the lower and upper limits of each design variable and for finding optimum designs, respectively. 3-D Reynolds-averaged Navier-Stokes solver is used to evaluate the objective functions of adiabatic efficiency and pressure ratio. Six variables from lean and airfoil thickness profile are selected as design variables. The results show that the adiabatic efficiency is enhanced by 1.43% by efficiency optimization while the pressure ratio is increased very small, and pressure ratio is increased by 0.24% by pressure ratio optimization.