Fermin Mallor

Thermal anemometry sensors for time-resolved velocity measurements average the measured signal over the length of their sensor, thereby attenuating fluctuations stemming from scales smaller than the wire length. Several compensation methods have emerged for wall turbulence, the most prominent ones relying on the small-scale universality in canonical flows or on the reconstruction based on two attenuated variance profiles obtained with sensors of different length. To extend these methods to non-canonical flows, the present work considers various adverse-pressure gradient (APG) turbulent boundary layer (TBL) flows in order to explore how the small-scale energy is affected in the inner and outer layer and how the two prominent correction methods perform as function of wall-distance, wire length and flow condition. Our findings show that the increased levels of small-scale energy in the inner, but also outer layer associated with APG TBLs reduces the applicability of empirical methods based on the universality of small-scale energy. On the other hand, a correction based on the relationship between the spanwise Taylor microscale and the two-point streamwise velocity correlation function, is able to correct the attenuated profiles of non-canonical cases. Combining the strength of both methods, a composite profile for the spanwise Taylor microscale is suggested, which then is used for the correction of probe-length attenuation effects across a multitude of flow conditions.

Active flow-control techniques have shown promise for achieving high levels of drag reduction. However, these techniques are often complex and involve multiple tunable parameters, making it challenging to optimize their efficiency. Here, we present a Bayesian optimization (BO) approach based on Gaussian process regression (GPR) to optimize a wall-normal blowing and suction control scheme for a NACA 4412 wing profile at two angles of attack: 5 and 11 degrees, corresponding to cruise and high-lift scenarios, respectively. An automated framework is developed by linking the BO code to the CFD solver OpenFOAM. RANS simulations (validated against high-fidelity LES and experimental data) are used in order to evaluate the different flow cases. BO is shown to provide rapid convergence towards a global maximum, even when the complexity of the response function is increased by introducing a model for the cost of the flow control actuation. The importance of considering the actuation cost is highlighted: while some cases yield a net drag reduction (NDR), they may result in an overall power increase. Furthermore, optimizing for NDR or net power reduction (NPR) can lead to significantly different actuation strategies. Finally, by considering losses and efficiencies representative of real-world applications, still a significant NPR is achieved in the 11° case, while net power reduction is only marginally positive in the 5° case.