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The study of turbulent flows calls for measurements with high resolution bothin space and in time. We propose a new approach to reconstructHigh-Temporal-High-Spatial resolution velocity fields by combining two sourcesof information that are well-resolved either in space or in time, theLow-Temporal-High-Spatial (LTHS) and the High-Temporal-Low-Spatial (HTLS)resolution measurements. In the framework of co-conception between sensing anddata post-processing, this work extensively investigates a Bayesianreconstruction approach using a simulated database. A Bayesian fusion model isdeveloped to solve the inverse problem of data reconstruction. The model uses aMaximum A Posteriori estimate, which yields the most probable field knowing themeasurements. The DNS of a wall-bounded turbulent flow at moderate Reynoldsnumber is used to validate and assess the performances of the present approach.Low resolution measurements are subsampled in time and space from the fullyresolved data. Reconstructed velocities are compared to the reference DNS toestimate the reconstruction errors. The model is compared to other conventionalmethods such as Linear Stochastic Estimation and cubic spline interpolation.Results show the superior accuracy of the proposed method in allconfigurations. Further investigations of model performances on various rangeof scales demonstrate its robustness. Numerical experiments also permit toestimate the expected maximum information level corresponding to limitations ofexperimental instruments.

A Bayesian fusion model for space-time reconstruction of finely resolved velocities in turbulent flows from low resolution measurements