This paper reviews turbulent boundary-layer control strategies for skin-friction reduction of aerodynamic bodies. The focus is placed on the drag-reduction mechanisms by two flow control techniques—spanwise oscillation and spanwise travelling wave, which were demonstrated to give up to 45 per cent skin-friction reductions. We show that these techniques can be implemented by dielectric-barrier discharge plasma actuators, which are electric devices that do not require any moving parts or complicated ducting. The experimental results show different modifications to the near-wall structures depending on the control technique.

The flow field around an asymmetric dielectric-barrier-discharge (DBD) plasma actuator in quiescent air is studied using particle image velocimetry (PIV) and smoke-flow visualization. On initiation of DBD plasma a starting vortex is created, which rolls up to form a coherent structure. The starting vortex becomes self-similar when the maximum velocity induced by the DBD plasma actuator reaches a steady state. Here, the plasma jet momentum increases linearly with time, suggesting that the DBD plasma actuator entrains and accelerates the surrounding fluid with a constant force. The wall-parallel and wall-normal distances of the vortex core are observed to scale with ${t}^{2/ 3} $ as it travels at $3{1}^{\circ } $ to the wall. The velocity of the starting vortex is found to scale with ${t}^{- 1/ 3} $ , while the circulation induced by the plasma actuator scales with ${t}^{1/ 3} $ .

The characteristics of convective heat transfer and fluid flow within a square cross-section serpentine channel are experimentally studied for two groups of polymeric viscoelastic fluids, shear-thinning and constant-viscosity Boger solutions. The elastic turbulence can be created by the non-linear interaction between elastic stresses generated within the flowing high-molecular-weight polymer solutions and the streamline curvature. In order to confirm elastic turbulence in this geometry, pressure drop across the serpentine channel was measured. The findings indicate that the measurements of non-dimensional pressure-drop increase approximately from 1.48 to 4.82 for viscoelastic solutions compared with the Newtonian fluid over a range of Weissenberg number from 4 to 211. The convective heat transfer enhances due to elastic turbulence by up to 200% for low polymer concentration (dilute) solutions and reaches up to 380% for higher polymer concentration (semi-dilute) solutions under creeping-flow conditions in comparison to that achieved by the equivalent Newtonian fluid flow at low Graetz number (up to 14.6). We propose a modified Weissenberg number which is able to approximately collapse the mean Nusselt number data for each solution group

Bayesian optimization (BO) provides an effective method to optimize expensive-to-evaluate black box functions. It has been widely applied to problems in many fields, including notably in computer science, e.g., in machine learning to optimize hyperparameters of neural networks, and in engineering, e.g., in fluid dynamics to optimize control strategies that maximize drag reduction. This paper empirically studies and compares the performance and the robustness of common BO algorithms on a range of synthetic test functions to provide general guidance on the design of BO algorithms for specific problems. It investigates the choice of acquisition function, the effect of different numbers of training samples, the exact and Monte Carlo (MC) based calculation of acquisition functions, and both single-point and multi-point optimization. The test functions considered cover a wide selection of challenges and therefore serve as an ideal test bed to understand the performance of BO to specific challenges, and in general. To illustrate how these findings can be used to inform a Bayesian optimization setup tailored to a specific problem, two simulations in the area of computational fluid dynamics (CFD) are optimized, giving evidence that suitable solutions can be found in a small number of evaluations of the objective function for complex, real problems. The results of our investigation can similarly be applied to other areas, such as machine learning and physical experiments, where objective functions are expensive to evaluate and their mathematical expressions are unknown.