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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국유체기계학회 International Journal of Fluid Machinery and Systems International Journal of Fluid Machinery and Systems Vol.10 No.3
- 발행연도
- 2017.9
- 수록면
- 240 - 253 (14page)
- DOI
- 10.5293/IJFMS.2017.10.3.240
이용수
초록· 키워드
오류제보하기
Latin hypercube sampling is widely used design-of-experiment technique to select design points for simulation which are then used to construct a surrogate model. The exploration/exploitation properties of surrogate models depend on the size and distribution of design points in the chosen design space. The present study aimed at evaluating the performance characteristics of various surrogate models depending on the Latin hypercube sampling (LHS) procedure (sample size and spatial distribution) for a diverse set of optimization problems. The analysis was carried out for two types of problems: (1) thermal-fluid design problems (optimizations of convergent–divergent micromixer coupled with pulsatile flow and bootshaped ribs), and (2) analytical test functions (six-hump camel back, Branin-Hoo, Hartman 3, and Hartman 6 functions). The three surrogate models, namely, response surface approximation, Kriging, and radial basis neural networks were tested. The important findings are illustrated using Box-plots. The surrogate models were analyzed in terms of global exploration (accuracy over the domain space) and local exploitation (ease of finding the global optimum point). Radial basis neural networks showed the best overall performance in global exploration characteristics as well as tendency to find the approximate optimal solution for the majority of tested problems. To build a surrogate model, it is recommended to use an initial sample size equal to 15 times the number of design variables. The study will provide useful guidelines on the effect of initial sample size and distribution on surrogate construction and subsequent optimization using LHS sampling plan.
목차
Abstract
1. Introduction
2. Latin Hypercube Sampling
3. Surrogate Models
4. Cross-validation Error
5. Particle Swarm Optimization
6. Problem Formulation
7. Results and Discussion
8. Conclusion
References
참고문헌 (34)
참고문헌 신청
[학술지(정기간행물)] - Kim, H. M. / 2006 / Shape optimization of three-dimensional channel roughened by angled ribs with RANS analysis of turbulent heat transfer / International Journal of Heat and Mass Transfer 49 : 4013 ~ 4022
[학술지(정기간행물)] - Knill D. L. / 1999 / Response surface models combining linear and euler aerodynamics for supersonic transport design / Journal of Aircraft 36 (1) : 75 ~ 86
[학술지(정기간행물)] - Lee, S. Y. / 2000 / Design optimization of axial flow compressor blades with three-dimensional Navier-Stokes solver / KSME International Journal 14 (9) : 1005 ~ 1012
[학술지(정기간행물)] - Salman Bahrami / 2015 / Physics-based Surrogate Optimization of Francis Turbine Runner Blades, Using Mesh Adaptive Direct Search and Evolutionary Algorithms / International Journal of Fluid Machinery and Systems 8 (3) : 209 ~ 219
[학술지(정기간행물)] - Samad, A. / 2008 / Multiple surrogate modeling for axial compressor blade shape optimization / Journal of Propulsion and power 24 : 302 ~ 310
[학술지(정기간행물)] - Knill D. L. / 1999 / Response surface models combining linear and euler aerodynamics for supersonic transport design / Journal of Aircraft 36 (1) : 75 ~ 86
[학술지(정기간행물)] - Lee, S. Y. / 2000 / Design optimization of axial flow compressor blades with three-dimensional Navier-Stokes solver / KSME International Journal 14 (9) : 1005 ~ 1012
[학술지(정기간행물)] - Salman Bahrami / 2015 / Physics-based Surrogate Optimization of Francis Turbine Runner Blades, Using Mesh Adaptive Direct Search and Evolutionary Algorithms / International Journal of Fluid Machinery and Systems 8 (3) : 209 ~ 219
[학술지(정기간행물)] - Samad, A. / 2008 / Multiple surrogate modeling for axial compressor blade shape optimization / Journal of Propulsion and power 24 : 302 ~ 310
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