Assessment of Turbulent Spectral Estimators in LDV

LDV의 난류 스펙트럼 추정치 평가

Numerical simulations have been performed to investigate various spectral estimators used in LDV signal processing. In order to simulate a particle arrival time statistics known as the doubly stochastic poisson process, an autoregressive vector model was adopted to construct a primary velocity field. The conditional Poisson process with a random rate parameter was generated through the rescaling time process using the mean value function. The direct transform based on random sampling sequences and the standard periodogram using periodically resampled data by the sample and hold interpolation were applied to obtain power spectral density functions. For low turbulent intensity flows, the direct transform with a constant Poisson intensity is in good agreement with the theoretical spectrum. The periodogram using the sample and hold sequences is better than the direct transform in the view of the stability and the weighting of the velocity bias for high data density flows. The high Reynolds stress and high fluctuation of the transverse velocity component affects the velocity bias which increases the distortion of spectral components in the direct transform.

본 연구에서는 상술한 특성을 갖는 유동자에 대하여 신뢰성이 보장된 스펙트 럼 추정법의 모색과 화립을 위해 의사 난류신호(turbulent-like signal)를 자기회기 모형(autoregressive model:AR model)으로 생성하고 추출간격이 유동장에 영향을 받는 비주기적 확률과정을 수치적으로 모사한다. 이 비주기적 실현 신호로 부터 현재 가 장 많이 사용되고 있는 Roberts와 Gaster의 직접 변화법과 추출 및 유지신호의 피리오 도그램(periodogram)법에 대해 데이터 밀도와 난류강도의 다양한 변화에 따른 속도편 의의 영향 등을 살펴보는데 목적을 둔다.