Session L22: Flow Control: Drag Reduction

Underlying mechanisms of drag reduction and enhancement in turbulent flow via external body forces

The effects of externally-applied traveling-wave body forces on turbulent flows are investigated using direct numerical simulations at a friction Reynolds number of 134. In this study, we aim at elucidating the underlying mechanisms behind drag reduction (DR) and drag enhancement (DE) caused by the external body force. It is found that the low forcing frequency results in DE, while the drag continues to decrease with increasing frequency. At sufficiently high forcing frequencies, DR is ultimately achieved. To elucidate DR/DE mechanisms, the wall shear stress of DR, DE, and no-control cases are compared. Using the frequency analysis, it is observed that as expected, the DR case exhibits the forcing and intrinsic bursting frequencies, while the no-control case presents only the bursting frequency. Interestingly, the DE case shows three frequencies: the bursting frequency, a frequency close to the forcing frequency, and a very high frequency. It is assumed that this very high frequency is responsible for drag enhancement. In addition, we investigate the wall shear stress characteristics. Counter-intrusively, the DR case shows a significantly large increase during bursting compared to no-control and DE cases. However, the bursting magnitude of the DE case is very similar to the no-control case. Reynolds number dependence of DR/DE mechanisms will be discussed.   

Passive Attenuation of Tollmien-Schlichting Waves by Phononic Subsurfaces

The drag reduction through the delay of laminar-to-turbulent transition in wall-bounded flows has been a focus of the flow control community for decades. One path previously investigated for achieving these benefits is through active opposition control of Tollmien-Schlichting (TS) waves using a piezoelectrically driven oscillating surface (PDOS, Amitay et al. 2016). However, the dynamic interaction of the fluid and the solid surface can also be exploited through passive coupling. Hussein et al. (2015) computationally demonstrated a solid phononic subsurface (PSub) flow stabilization technique to attenuate or amplify the growth of TS waves. In this method, the interaction between the fluid and the PSub passively generates a spatio-temporal elastic deformation profile at the surface of the solid which counters the growth of the TS-waves. In the current work, this technique is explored experimentally within the University of Colorado Boulder low-speed wind tunnel. TS waves are seeded into the flow by an upstream PDOS within the laminar boundary layer of a 3.5 m long flat plate assembly at a freestream speed of 15 m/s. Time-resolved particle image velocimetry measurements are used to assess the growth and mitigation of the TS waves for both the baseline flow and the case with a PSub installed downstream for comparison with the prior computational results of Hussein et al. (2015).   

Optimizing Skin-Friction Drag Reduction via Low-Amplitude Wall-Normal Blowing Techniques Using a Bayesian Optimization Framework

In this research, we used low-amplitude wall-normal blowing to reduce the skin-friction drag of zero-pressure-gradient turbulent boundary-layer flows in a wind tunnel, optimized using a novel Bayesian optimization framework, NUBO (Newcastle University Bayesian Optimization). NUBO was used to optimize control parameters including amplitude, frequency, and wavelength of actuation to identify control strategies which give high skin-friction drag reduction with a continual change in wind speed between 5 m/s and 20 m/s. A hot-wire probe was used to measure the streamwise velocity across the linear sublayer in the turbulent boundary layer to calculate skin friction coefficients. Comparisons to high-fidelity simulations matching the wind tunnel experiments were made to investigate the changes in flow physics during control. It is shown that NUBO can find various sets of parameters to achieve significant drag reduction corresponding to different fluid flow mechanisms. This highlights the potential of using machine learning approaches for flow control applications.   

Opposition control of turbulent spots

Opposition control of artificially initiated turbulent spots in a laminar boundary layer was carried out in a low-turbulence wind tunnel with the aim to delay transition to turbulence by modifying the turbulent structure within the turbulent spots. The timing and duration of control, which was carried out using wall-normal jets from a spanwise slot, were pre-determined based on the baseline measurements of the transitional boundary layer. The results indicated that the high-speed region of the turbulent spots was cancelled by opposition control, which was replaced by a carpet of low-speed fluid. The application of the variable-interval time-averaging technique on the velocity fluctuation signals demonstrated a reduction in both the burst duration and intensity within the turbulent spots, but the burst frequency was increased.   

investigation of microbubble dynamics on superhydrophobic surfaces through direct numerical simulation with nek5000 and euler-lagrange approach

The microbubble injection technology for a slow-speed ship, tanker, and underwater vehicle is one of the promising techniques to reduce skin friction resistance. In this study, the behavior of microbubbles over the superhydrophobic surface was simulated using the Nek5000 code based on the spectral element method. The 4-way coupling Euler-Lagrange code ppiclF was adopted to predict the microbubble dynamics with the assumption of a non-deformable and spherical one^[1]. The non-linear collision force model^[2] was implemented to consider the effect of wall-bubble and bubble-bubble interactions. The simulation domain is horizontal channel flow, and one of the channel surfaces was set as a superhydrophobic surface. The present work adopted two types of superhydrophobic surfaces^[3], ridge type and post type. The microbubbles induce drag reduction only in post type superhydrophobic surfaces. The drag reduction effect by the void fraction of microbubble and superhydrophobic surface type was investigated in detail.   

Bayesian Optimisation of Wall-Normal Blowing Control for Skin-Friction Drag Reduction in a Turbulent Boundary Layer

Skin-friction drag is a key contributor to inefficiencies across a broad range of industries. Strategies aimed at minimising skin friction, therefore, have the potential to significantly reduce operating costs and assist in meeting emission targets. Active flow control techniques are among the most promising approaches to robust and effective skin-friction drag reduction. However, they are difficult to design and optimise. Furthermore, the drag reduction must be weighed against the energy required to power the control. Bayesian optimisation is a derivative-free, non-intrusive optimisation technique that is well-suited to problems with a moderate number of input dimensions and where the objective function is expensive to evaluate, such as with high-fidelity computational fluid dynamics simulations. With this in mind, the present work explores the potential of low-intensity wall-normal blowing for skin-friction drag reduction in turbulent boundary layers by combining a high-order flow solver (Incompact3d) with a Bayesian optimisation framework. The control is composed of streamwise-varying wall-normal blowing, parameterised by a cubic spline. The inputs to the optimiser are the amplitudes of the spline knots, whereas the objective function is the net-energy saving (NES), which accounts for both the skin-friction drag reduction and the input power for the control (which is estimated from a real-world device). The results are mixed, with significant drag reduction reported but no improvement in NES over the canonical case. Selected cases are chosen for further analysis and the drag reduction mechanisms are highlighted. The results demonstrate that low-intensity wall-normal blowing is an effective strategy for skin-friction drag reduction and that Bayesian optimisation is a useful tool for optimising such strategies. Furthermore, the results show that even a minor improvement in the blowing efficiency of the device used in the present work will lead to meaningful NES.   

Machine-learned wall oscillations for drag reduction in turbulent channel flows

Imposing stream-wise travelling waves (STW) of spanwise velocity in a channel flow is a known technique for obtaining Drag Reduction (DR). In numerical simulations, the STW is enforced by using a boundary condition on the spanwise velocity component: w=A sin(λ z – ω t). For relatively low Reynolds numbers and steady parameters (ω and λ constant), DR is obtained by generating a Stokes layer next to the wall (Quadrio M. Phil. Trans. of the Royal Soc. 369, 2011) that damps the action of the coherent structures (Jimenez J. and Pinelli A. J. Fluid Mech 389, 1999). Quadrio and co-workers (Quadrio M. et al. J. Fluid Mech 627, 2009), have compiled a drag reduction/increase map for various combinations of λ and ω. Here, we explore a different route towards DR based on the conceptual idea that time variations of λ and ω can lead to local and instantaneous manipulation of the wall regeneration cycle. To "discover" λ(t) and ω (t) (typical time-scales expected to be within the typical burst period) that lead to DR and to an effective manipulation of the wall cycle, we have used a Deep Reinforcement Learning methodology, that uses the skin friction reduction as a reward and (λ, ω) as actions. We will give further details and physical interpretations of the results during the presentation.   

Drag reduction in azimuthally oscillating turbulent pipe flow

One promising method of significant drag reduction for turbulent flows is the implementation of spanwise wall oscillations at specially tuned frequencies and amplitudes. This mechanism typically uses high-frequency spanwise oscillations at time-scales that can be coupled to the small eddies near the surface.  In the past this research has been limited to low friction Reynolds number (Re_τ) findings (defined here as Re_τ < 2,000); however, recent studies have shown that tuning frequencies to timescales that couple with the larger eddies in the flow may be a more effective way to reduce drag in high Re_τ applications, where these larger-scale motions begin to account for an increasing share of drag.  We report results from an experimental study in an azimuthally oscillating pipe to observe drag reduction at Re_τ values up to ~6000. The experimental set up allows for a large range of amplitude of oscillation, frequency of oscillation, and Re_τ in order to elucidate a new relationship between these factors and the resulting drag reduction in both high and low Re_τ flows.    

Drag and wake regime control of flow past tandem circular cylinders using hinged splitter plate: A numerical investigation

In the present numerical study, the drag and wake regime control of two-dimensional tandem identical circular cylinders (TCC) with a hinged splitter plate (HSP) at a Reynolds number of Re = 100 is investigated using the inhouse developed flexible forcing immersed boundary scheme-based one-step simplified lattice Boltzmann method. The gap G between the centres of the cylinders is set at G = 5.0D, where D is the diameter of the cylinder. The rigid splitter plate is hinged at the rear base point of the upstream cylinder, and it undergoes a sinusoidal pitching motion for the pitching amplitudes (θ_m) of 15º. In addition, the Strouhal number (St_f) and the splitter plate length (L_f) are varied between 0.1 – 0.4 and 0.0D – 1.0D, respectively. It is observed that the total drag of TCC with HSP configuration is always less compared to the stationary splitter plate of identical size. Moreover, the total drag of TCC-HSP arrangement decreases for increasing St_f at all L_f except when L_f = 1.0D, and a maximum of 35% drag reduction is achieved at St_f = 0.40 and L_f = 0.75D. However, beyond St_f ≥ 0.20, the total drag of TCC increases for L_f = 1.0D. The relationship between drag and the near wake flow structure will be thoroughly discussed in the presentation for varied St_f and L_f.   

Drag Reduction using particles as Shear Free Surfaces in a turbulent pipe flow

A novel passive drag reduction technique using solid additives termed "Shear Free Surfaces"(SFS) is studied experimentally. It works by modifying the large scales of the turbulent flow. The lateral transport of momentum is attenuated due to kinematic wall blocking effect bringing about the effect of drag reduction at the global scale. In the present study, solid particles that move with the local flow velocity, and hence, essentially have no shear on the mean, are considered as Shear Free Surfaces. The SFSes for the present study are solid spheroids and organic seeds of different sizes and densities procured locally (ABS, Brassica Nigra and Brassica Juncea). Experiments are performed in a horizontal gravity driven pipe flow at low volume fractions to determine the drag reducing characteristics of the SFSes under consideration. A consistent 2% drag reduction, measured from the "carrier only" discharge which discounts the particle volume fraction, is observed. It is seen that the scaling of the particle dimension with the inner wall units essentially determines if the particle is able to reduce drag in the flow. When the particle size is less than the distance of region of peak production from the wall, ideally half, the particles are able to bring about significant drag reduction. It is also observed that when the particles' settling velocity is more than the turbulence velocity scale which is the friction velocity, the settling effects dominate and the particles cause additional drag.