The guideline of selecting the number of snapshot dataset, N<SUB>s</SUB> in proper orthogonal decomposition(POD) was presented via the analysis of Eigen values based on the singular value decomposition(SVD). In POD, snapshot datasets from ...

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https://www.riss.kr/link?id=A103044476
2017
Korean
559
KCI등재
학술저널
59-66(8쪽)
0
0
상세조회0
다운로드다국어 초록 (Multilingual Abstract)
The guideline of selecting the number of snapshot dataset, N<SUB>s</SUB> in proper orthogonal decomposition(POD) was presented via the analysis of Eigen values based on the singular value decomposition(SVD). In POD, snapshot datasets from ...
The guideline of selecting the number of snapshot dataset, N<SUB>s</SUB> in proper orthogonal decomposition(POD) was presented via the analysis of Eigen values based on the singular value decomposition(SVD). In POD, snapshot datasets from the solutions of Euler or Navier-Stokes equations are utilized to SVD and a reduced order model(ROM) is constructed as the combination of Eigen vectors. The ROM is subsequently applied to reconstruct the flowfield data with new set of flow conditions, thereby enhancing the computational efficiency. The overall computational efficiency and accuracy of POD is dependent on the number of snapshot dataset; however, there is no reliable guideline of determining N<SUB>s</SUB>. In order to resolve this problem, the order of maximum to minimum Eigen value ratio, O(R)from SVD was analyzed and presented for the decision of N<SUB>s</SUB>: in case of steady flow, N<SUB>s</SUB> should be determined to make O(R) be 10<SUP>9</SUP>. For unsteady flow, N<SUB>s</SUB> should be increased to make O(R) be 10<SUP>11~12</SUP>. This strategy of selecting the snapshot dataset was applied to two dimensional NACA0012 airfoil and vortex flow problems including steady and unsteady cases and the numerical accuracies according to N<SUB>s</SUB> and O(R) were discussed.
참고문헌 (Reference)
1 정성기, "적합직교분해를 이용한 로터 블레이드의 차수축소모델 구축 및 공력특성 분석" 한국항공우주학회 37 (37): 1073-1079, 2009
2 Park, K, "Reduced-order model with an artificial neural network for aerostructural design optimization" 50 (50): 1106-1116, 2013
3 Lucia, D.J., "Reduced-Order Modeling: New Approaches for Computational Physics" 40 (40): 51-117, 2004
4 전상욱, "Reduced order model of three-dimensional Euler equations using proper orthogonal decomposition basis" 대한기계학회 24 (24): 601-608, 2010
5 구본찬, "MRA와 POD를 적용한 공력특성 최적설계" 한국전산유체공학회 20 (20): 7-15, 2015
6 Lee, K., "Examples of Reduced Order Modeling for a 3D Backward Facing Step Flow using POD Technique" 2010
1 정성기, "적합직교분해를 이용한 로터 블레이드의 차수축소모델 구축 및 공력특성 분석" 한국항공우주학회 37 (37): 1073-1079, 2009
2 Park, K, "Reduced-order model with an artificial neural network for aerostructural design optimization" 50 (50): 1106-1116, 2013
3 Lucia, D.J., "Reduced-Order Modeling: New Approaches for Computational Physics" 40 (40): 51-117, 2004
4 전상욱, "Reduced order model of three-dimensional Euler equations using proper orthogonal decomposition basis" 대한기계학회 24 (24): 601-608, 2010
5 구본찬, "MRA와 POD를 적용한 공력특성 최적설계" 한국전산유체공학회 20 (20): 7-15, 2015
6 Lee, K., "Examples of Reduced Order Modeling for a 3D Backward Facing Step Flow using POD Technique" 2010
Penalized VIC 방법에서 장시간 유동 해석을 위한 원거리 와도 입자 처리
평판 휜 열교환기의 열 수력학적 성능에 대한 고속 바이패스 영향의 수치적 연구
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|---|---|---|---|
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| 2005-06-16 | 학술지명변경 | 외국어명 : Jpurnal of Computatuonal Fluids Engineering -> Korean Society of Computatuonal Fluids Engineering | ![]() |
| 2005-01-01 | 평가 | 등재후보 1차 PASS (등재후보1차) | ![]() |
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학술지 인용정보
| 기준연도 | WOS-KCI 통합IF(2년) | KCIF(2년) | KCIF(3년) |
|---|---|---|---|
| 2016 | 0.2 | 0.2 | 0.19 |
| KCIF(4년) | KCIF(5년) | 중심성지수(3년) | 즉시성지수 |
| 0.16 | 0.15 | 0.405 | 0.05 |