Abstract: Data Assimilation (DA) has been introduced into the turbulence dynamics community in recent years. Coupling experimental measurements and numerical simulation, it improves the accuracy and scope of measurements and reduces the uncertainty of simulations. Experimental observations, predictive models and assimilation algorithms are three essential factors in DA. Observations in turbulent flows usually involve hot wire anemometer, Particle Image Velocimetry (PIV), pressure sensors and other measurement techniques. The predictive model refers specifically to flow governing equations and turbulence closures. The assimilation algorithm ranges from Bayesian inference, Ensemble Karman Filter (EnKF), to adjoint formulations. DA for steady-state flows has a combination of Reynolds-Averaged Navier-Stokes (RANS) turbulence models, aiming at model constant recalibration, equation form-error correction and Reynolds stress term reproduction, whilst the unsteady DA has two main categories, i.e., four-dimensional variational DA (4DVar) and sequential DA. Employing the forward and backward integration, 4DVar requires large storage space and high computational cost. Free from backward integration, sequential DA can intermittently couple with the observational data at selected instances in a forward direction. Additionally, fast development of machine learning and artificial intelligence pushes turbulence research towards the direction of intelligence. While the pure data-driven machine learning lacks physical constraints, the physical-informed machine learning is in consistent with DA in the essence of physics.

Key words: turbulent flows, data assimilation, experimental measurement, numerical simulation, machine learning