Zonal Reduced-Order Modelling Toward Prediction of Transitional Flow Fields

The utilization of measurement data is becoming attractive in various fields due to the massive growth of sensing and networking technologies. It is expected to utilize such a data-rich environment to improve engineering simulations in computer-aided engineering (CAE). Data assimilation is one of methodologies to statistically integrate a numerical model and measurement data, and it is expected to be a key technology to take advantage of measured data in CAE. However, the additional cost of data assimilation is not always affordable in CAE simulations. In this study, we consider the cost reduction of numerical flow simulation by the help of a reduced-order model. Since the prediction accuracy of existing ROMs are limited in transitional flow problems such as two- to three-dimensional flow transition, we investigate here a zonal hybrid approach of a full-order model and a reduced-order model.