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적합직교분해 기법에서의 효율적인 스냅샷 선정을 위한 고유값 분석

강형민(H.M. Kang),전상욱(S.O. Jun),이관중(K. Yee) 한국전산유체공학회 2017 한국전산유체공학회지 Vol.22 No.1
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The guideline of selecting the number of snapshot dataset, Ns 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 Ns. 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 Ns: in case of steady flow, Ns should be determined to make O(R) be 109. For unsteady flow, Ns should be increased to make O(R) be 1011~12. 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 Ns and O(R) were discussed.
2
RDAPS Sea Wind Model을 이용한 해상풍력발전단지 최적 Macro-Siting

이기학(K.H. Lee),전상욱(S.O. Jun),박경현(K.H. Park),이동호(D.H. Lee),박종포 한국전산유체공학회 2011 한국전산유체공학회 학술대회논문집 Vol.2011 No.5
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This paper introduces the optimum macro-siting of a potential site for an offshore wind farm around Jeju Island using the RDAPS sea wind model. The statistical model was developed by analyzing the sea wind data from RDAPS model, and the meso-scale digital wind map was prepared. To develop the high resolution spatial calibration model, Artificial Neural Network (ANN) models were used to construct the wind and bathymetric maps. Accuracy and consistency of wind/bathymetric spatial calibration models were obtained using analysis of variance. The optimization problem was defined to maximize the energy density satisfying the criteria of maximum water depth and maximum distance from the coastline. The candidate site was selected through Genetic Algorithm(GA). From the results, it is possible to predict roughly a candidate site location jar the installation of the offshore wind farm, and to evaluate the wind resources of the proposed site.

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