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The reconstruction of the flow behind a backward facing step at a Reynolds number of 60,000 using Linear Stochastic Estimation (LSE) and modified is investigated. In particular, the turbulent spatial integral length scales reconstructed for several sensor configurations are studied. The reconstruction of the Proper Orthogonal Decomposition (POD) modes is also performed in order to show the limitations of the LSE reconstruction for complex flows, for which taking into account only a few POD modes is not enough to represent the flow dynamics. The importance of the sensors location on the reconstruction is also emphasized and the opportunity to use a sensor location optimization algorithm investigated.

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Single hot-wire velocity measurements have been conducted along a three-dimensional measurement grid to capture the flow-field induced by a 45°inclined slotted pulsed jet. Based on the periodic behavior of the flow, two different estimation methods have been implemented. The first one, considered as the reference base-line, is the conditional approach which consists in the redistribution of the experimental data into spaceand time-resolved three-dimensional velocity fields. The second one uses a neural network to estimate 3D velocity fields given spatial coordinates and time. This paper compares the two methods for a complete flow-field estimation based on hot-wire measurements. Results suggest that the neural network is tailored to capture the phase-averaged dynamic response of the jet induced by the actuator, and identify the coherent structures in the flow field. Interesting performances are also observed when degrading the learning database, meaning that neural networks can be used to drastically improve the temporal or spatial resolution of a flow field estimation compared to the experimental data resolution.

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