A STUDY ON KALMAN-FILTER-BASED COUPLING STRATEGY FOR TURBULENCE DATA ASSIMILATION

The accurate prediction of complex separated flows in aerospace engineering remains a significant challenge in modern aircraft design. Data assimilation provides an effective approach for integrating experimental measurements and numerical simulations in order to improve the accuracy of flow-field reconstruction. In this study, a Kalman filtering method is employed to assimilate turbulence information, including full-field eddy-viscosity distributions, Spalart–Allmaras(S-A) model parameters, and the correction factor β in the production term of the S-A model. Both one-way and two-way coupling strategies are developed and systematically compared to evaluate their influence on computational accuracy and numerical stability. To verify the effectiveness of different coupling approaches, two typical airfoils, S809 and DU91-W2-250, are selected as test cases, and simulations are performed under large-angle-of-attack separated-flow conditions. The pressure-coefficient distribution, lift-coefficient errors, and flow-field structures are analyzed and compared with experimental results. The results show that all three assimilation strategies can successfully integrate experimental data and significantly improve the accuracy of flow-field reconstruction compared with the standard S-A model. However, the one-way coupling approach tends to generate numerical oscillations under complex flow conditions, which may lead to unstable convergence. In contrast, the two-way coupling method, through iterative feedback between the turbulence model and the Navier–Stokes equations, exhibits faster convergence and better numerical robustness. It is capable of maintaining stable solutions even in highly separated flow regimes, demonstrating its superiority in reconstructing complex turbulent flows. This work provides methodological guidance and numerical evidence for the engineering application of turbulence data-assimilation techniques.