Whole field validation of numerical and experimental results

Abstract The kriging estimator and its associated covariance model are introduced as a means of describing the verisimilitude of spatial datasets describing flow-fields in their entirety, and further as a means of interpreting and blending said datasets. In this manner a means of comparing uncertain nodal data from numerical models and experimental flow-field anemometry is developed. For spatial datasets, this activity has heretofore been considered to be a simple extension of established methodologies in validation and verification, which have been developed with the validation of scalar data – lift, drag, point velocity components or pressure, in mind. It is demonstrated that a more complex and complete comparison arises when the entire fields of data are correlated via spatial covariance functions, instead. These spatial covariance functions then inform the subsequent estimation, smoothing and blending of velocity fields; known as cokriging. In this paper, the theoretical model underlying kriging estimation is elucidated, and the techniques are demonstrated with reference to Laser Doppler anemometry and Finite Volume modelling of a subsonic flow of air around an experimental model. It is proposed that by developing spatial correlations between datasets, a more rigorous and flexible model for spatial comparison and validation of flow-fields emerges.