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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.3 No.4
- 발행연도
- 2010.12
- 수록면
- 369 - 378 (10page)
- DOI
- http://dx.doi.org/10.5293/IJFMS.2010.3.4.369
이용수
초록· 키워드
오류제보하기
The creation of the hydraulic turbine flow factor map will undoubtedly benefit its design by decreasing both the design cycle time and product cost. In this paper, the geometry and flow variables, which effectively affect the flow factor, are proposed, analyzed and determined. These flow variables are further used to create the operating condition maps by using different model approaches categorized into Response Surface Method (RSM) and Artificial Neural Network (ANN). The accuracies of models created by different approaches are compared and the performances of model approaches are analyzed. The influences of chosen variables and the combination of Principle Component Analysis (PCA) and model approaches are also studied. The comparison results between predicted and actual flow factors suggest that two-hidden-layer Feed-forward Neural Network (FFNN), and one.hidden-layer FFNN with PCA has the best performance on forming this mapping, and are accurate sufficiently for hydraulic turbine design.
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UCI(KEPA) : I410-ECN-0101-2018-554-001908710