지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술대회자료
- 저자정보
- 발행연도
- 2013.8
- 수록면
- 733 - 743 (11page)
이용수
초록· 키워드
오류제보하기
Evolutionary algorithms (EA) are a unique and attractive approach to the real world multiobjective optimization design such as a axial flow pump impeller optimization design problem. In this paper, multi-objective evolutionary algorithm to the axial flow pump impeller optimization design is presented. In axial flow pump design process, in order to get high performance pump, designers usually try to increase the efficiency (η) and decrease the required NPSH (NPSHr) simultaneously. In this paper, multi-objective optimization of axial flow pump based on modified evolutionary algorithm Particle Swarm Optimization (MPSO) is performed. At first, the NPSHr and η in a set of axial flow pump are numerically investigated using commercial software ANSYS with the design variables concerning hub angle βh, chord angle βc, cascade solidity of chord σc, maximum thickness of blade H. And then, using the Group Method of Data Handling (GMDH) type neural networks in commercial software DTREG, the corresponding polynomial representation for NPSHr and η with respect to design variables are obtained. Finally, multi objective optimization based on modified Particle Swarm Optimization (MPSO) approach is used for Pareto based optimization. The result shows that an optimal solution of the axial flow pump impeller was obtained: NPSHr was decreased by 11.68% and efficiency was increased by 4.24% simultaneously. It means this optimization is feasible.
목차
Abstract
1. Introduction
2. Definition of variables and CFD simulation of axial flow pump
3. Meta-models building using GMDH-type neural network
4 Apply multi-objective optimization by using modified PSO method
5 CONCLUSIONS
References
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UCI(KEPA) : I410-ECN-0101-2015-500-000939015