The application of data assimilation to combine experimental data and LES for improved state-estimation.

In the current study, data assimilation techniques are investigated to integrate high-speed high-resolution experimental data into a Large Eddy Simulation (LES). LES of an inert jet is performed without data assimilation and shown to accurately reproduce statistical flow-field quantities. To capture the transient dynamics, assimilation of experimental data is performed using an Ensemble Kalman Filter (EnKF) algorithm and the performance of the method is investigated to understand its impact on the state estimation. Our first objective is to investigate the impact that data assimilation has on the resulting flow field for this inert jet. This is accomplished by comparing transient predictions and instantaneous flow structures obtained from a baseline LES without data assimilation to those obtained via EnKF. The second objective is to identify the impact that data localization has on the resulting predictions. Following this, we investigate how the state-estimation is affected by changes in experimental uncertainty, assimilation frequency and sparsity of experimental data.