다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Statistics for Spatial Data.

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

Adaptive sparse polynomial chaos expansion based on least angle regression

To improve the performance of particle image velocimetry in measuring instantaneous velocity fields, direct cross-correlation of image fields can be used in place of auto-correlation methods of interrogation of double- or multiple-exposure recordings. With improved speed of photographic recording and increased resolution of video array detectors, cross-correlation methods of interrogation of successive single-exposure frames can be used to measure the separation of pairs of particle images between successive frames. By knowing the extent of image shifting used in a multiple-exposure and by a priori knowledge of the mean flow-field, the cross-correlation of different sized interrogation spots with known separation can be optimized in terms of spatial resolution, detection rate, accuracy and reliability.

Theory of cross-correlation analysis of PIV images : Image analysis as measuring technique in flows

Implicit Time Integration Schemes for the Unsteady Compressible Navier–Stokes Equations: Laminar Flow

Complex o w and uid-structure interaction simulations require ecien t uncertainty quantication methods. In addition, a good property of an uncertainty quantication method is non-intrusiveness, meaning that existing deterministic solvers can be used for uncertainty quantication. In this paper the Probabilistic Collocation method is introduced, which combines the non-intrusiveness of the Stochastic Collocation method with the exponential convergence for arbitrary probability distributions of the Galerkin Polynomial Chaos method. Due to the non-intrusiveness and exponential convergence, the Probabilistic Collocation method requires only a few collocation points for a high accuracy. For the one dimensional piston problem the eciency of the Probabilistic Collocation method is compared with the Galerkin Polynomial Chaos method, the Non-Intrusive Polynomial Chaos method and the Stochastic Collocation method. The strength of the Probabilistic Collocation method is demonstrated by solving steady o w around a NACA0012 airfoil with an uncertain free stream velocity using a commercial o w solver. Dieren t possibilities of representing the stochastic solution are demonstrated to show the potential use of uncertainty quantication.

Hester Bijl

Papers

Implicit Time Integration Schemes for the Unsteady Compressible Navier–Stokes Equations: Laminar Flow

Probabilistic Collocation: An Efficient Non-Intrusive Approach for Arbitrarily Distributed Parametric Uncertainties

Bayesian estimates of parameter variability in the k-ε turbulence model

A Unified Method for Computing Incompressible and Compressible Flows in Boundary-Fitted Coordinates

Modeling physical uncertainties in dynamic stall induced fluid-structure interaction of turbine blades using arbitrary polynomial chaos