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Numerical investigations on selective detection of characteristic flow field patterns in a turbulent boundary layer flow

Subject Area Fluid Mechanics
Term from 2020 to 2024
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 446803563
 
Final Report Year 2025

Final Report Abstract

In this project, stiff, very fine bending fibres were considered, which protrude from a wall into the flow and act as wall sensors. The measuring principle is based on the flow resistance of these fibres, which can be easily estimated numerically in a flow field known from a direct numerical simulation. As a result, it was found that the bending moment of the sensors on the wall is better suited as a measurement signal than the deflection of the sensor tips. By comparison with the instantaneous flow field at different wall distances, the wall distance of maximum correlation with the measurement signal and the associated calibration curves were determined, with which both components of the wall-parallel velocity can be determined from the wall bending moments. By varying the sensor length, it is possible to penetrate areas at different distances from the wall. As the sensor data records simultaneous flow states in planes parallel to the wall, flow field structures such as high- and low-speed streaks and different Q events can be detected. The latter even allow conclusions to be drawn about local vortices. As an alternative to opposition control (OC), an artificial neural network (ANN) with deep reinforcement learning was trialled in a periodic channel flow. However, the approaches and parameters adopted from preliminary investigations did not lead to a successful result. It was then investigated whether the wall-parallel velocity components determined from the wall sensor signals could also be used to determine the wall-normal component for further tests with OC. This appears to be feasible.

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