Heat transfer enhancement in turbulent boundary layers with a pulsed slot jet in crossflow

Castellanos, Rodrigo; Gianfranco, Salih,; Raiola, Marco; Ianiro, Andrea; Discetti, Stefano

doi:10.1016/j.applthermaleng.2022.119595

Cited by 5 publications

(1 citation statement)

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“…Therefore, active flow control strategies are ideal for optimization methods. Recently, new developments in machine learning have been successfully transferred to the field of flow-control, with both evolutionary algorithms [16] and deep reinforcement learning [17] successfully tested on zero-pressure gradient TBLs and channel flow, respectively. However, their application to real-world problems, such as the flow around wings, remains difficult due to the large number of flow realizations required by the optimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Bayesian optimization of wall-normal blowing and suction-based flow control of a NACA 4412 wing profile

Mallor,

Semprini-Cesari,

Mukha

et al. 2023

Preprint

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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.

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Section: Introductionmentioning

confidence: 99%