Bulletin of the American Physical Society
71st Annual Meeting of the APS Division of Fluid Dynamics
Volume 63, Number 13
Sunday–Tuesday, November 18–20, 2018; Atlanta, Georgia
Session A17: Flow Control: Drag Reduction |
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Chair: Matthew Juniper, University of Cambridge Room: Georgia World Congress Center B304 |
Sunday, November 18, 2018 8:00AM - 8:13AM |
A17.00001: On the physical mechanism of turbulent boundary layer drag reduction under pulsed-dc plasma actuation Samaresh Midya, Flint Owen Thomas, Thomas Charles Corke Experiments are reported which utilize a novel method of active flow control explicitly designed to intervene in the process of near wall streamwise vortex generation, which is a key component in turbulence production in wall-bounded flows. The flow control method utilizes flush mounted pulsed-DC plasma actuator arrays in a ZPG turbulent boundary layer at Reτ = 3200. The control flow produced by the actuator consists of a series of near-wall, spanwise opposed wall jets. These have been shown to produce significant reductions in turbulent drag. The focus of the reported experiments is to clarify the mechanism of drag reduction. Hot-wire measurements utilizing the quadrant splitting technique are performed downstream of the actuator. These are used to characterize and contrast both the duration of & time interval between quadrant 2 & 4 events in the actuated and non-actuated flows. The quadrant contributions to the Reynolds stress are compared for natural and actuated cases. The Cf for the actuated flow is computed using the FIK identity. By performing measurements over a range of distances downstream of the actuator, the characteristic streamwise distance over which the near wall organized structures relax back to the natural condition is also determined. |
Sunday, November 18, 2018 8:13AM - 8:26AM |
A17.00002: Mach number effect on drag control via spanwise wall oscillation in wall-bounded turbulent flows Jie Yao, Fazle Hussain The spanwise wall oscillation (SWO) is known to substantially reduce the skin-friction drag in wall-bounded turbulent flows. To understand the scope and limitations of this flow control for compressible flows, direct numerical simulations of isothermal channel flow with SWO are performed. At a fixed bulk Reynolds number Reb of 3000, we investigate subsonic and supersonic flows at bulk Mach numbers Mab of 0.8 and 1.5, respectively. The drag reduction DR as a function of the control parameters (namely, maximum wall velocity A+ and oscillation period T+; + denotes wall units scaling) has similar trend as the incompressible case: DR increases monotonically with A+, but varies non-monotonically with T+. For a given maximum wall velocity A+(=12), similar maximum DR is achieved as in the incompressible cases at the same Reτ. However, the optimal oscillation period T+ is found to slightly increase with Ma. The flow statistics and physics are examined to explain the mechanism for drag reduction. Similar to the incompressible case, the coherent structures are highly suppressed by SWO. These results suggest that significant DR can still be achieved via SWO even when the compressibility effect is considered. The effectiveness of SWO at higher Re’s and Ma’s is being explored. |
Sunday, November 18, 2018 8:26AM - 8:39AM |
A17.00003: Effects of superhydrophobicity on transition in a spatially developing boundary layer Shao-Ching Huang, John Kim Superhydrophobic surfaces have exhibited achieving significant skin-friction drag reduction in wall-bounded turbulent flows. In fully-developed turbulent channel flow, it has been reported that drag reduction can be characterized by the slip length over a range of Reynolds numbers and surface geometrical properties. The effects of superhydrophobic surfaces on flow transition to turbulence are also of great interest for engineering applications. This study investigates the effects of superhydrophobic surface on transition to turbulence in a spatially developing boundary layer. In our direct numerical simulations, small disturbances containing unstable frequencies are introduced to an incoming Blasius boundary layer to trigger transition, with and without a superhydrophobic wall downstreams. The computational domain covers the laminar, transition and fully-developed turbulent regions. The superhydrophobic surface consists of alternating air pockets, supported by surface tension between the gas the liquid, and the no-slip wall. A number of superhydrophobic surface geometries are considered and their effects on transition to turbulence are compared. Flow statistics are examined, and the effects of grid resolution on the superhydrophobic transition simulations are discussed. |
Sunday, November 18, 2018 8:39AM - 8:52AM |
A17.00004: Effect of flow and polymer properties on near wall mean and fluctuating velocity profiles Yasaman Farsiani, Brian R Elbing Polymer additives are known to reduce the local skin friction drag within a turbulent boundary layer (TBL) with a corresponding modification of the near wall velocity profile. The classical view of the modification is that the log region slope is unchanged though the intercept constant increases in proportion to the drag reduction level until the maximum drag reduction (MDR) asymptote is achieved. However, recent experimental and numerical studies have demonstrated that the slope does vary at high drag reduction levels. These deviations from the classical view must be due to flow properties and/or polymer properties. The current study uses a homogeneous concentration of polymer solution within a developing TBL to enable precise control of the polymer properties. The near-wall modified TBL velocity profiles were acquired with particle image velocimetry with the flow (Reynolds number) and polymer (Weissenberg number) properties well characterized. Mean and fluctuating velocity profiles will be presented and analyzed to assess their sensitivity to these parameters. Note that the impact of polymer degradation on molecular weight will be quantified and accounted for when estimating polymer properties. |
Sunday, November 18, 2018 8:52AM - 9:05AM |
A17.00005: Opposition control of turbulent channel flow with wall pressure using deep neural network Jinhyuk Yun, Jungil Lee The opposition feedback control proposed by Choi et al. (JFM, 1994) successfully reduces the skin-friction of wall-bounded turbulent flow by using blowing/suction at the wall in opposition to the near wall velocity. The motivation of this study is to explore the possibility of using the wall pressure as a control input for the opposition control instead of the near wall velocity. For this purpose, we build a deep neural network (DNN) to predict the near wall velocity from the information of wall pressure in turbulent channel flow. For the learning process to build DNN, instantaneous flow data sets are obtained from direct numerical simulation of turbulent channel flow at Reτ = 178. We examine the prediction performance of DNN according to the plane size of wall pressure as an input, type of DNN model, number of learning data sets, and etc. It is found that the near wall velocity can be successfully predicted by DNN with the wall pressure input. Also, we conduct opposition control of turbulent channel flow based on the DNN constructed, and it is shown that its control performance of skin-friction reduction is similar to that of the original opposition control. |
Sunday, November 18, 2018 9:05AM - 9:18AM |
A17.00006: Prediction and control of turbulent channel flow with deep learning Jonghwan Park, Haecheon Choi We apply deep learning to predict, and then control turbulent channel flow for drag reduction. A well-known turbulence control problem, opposition control (Choi et al., 1994, JFM), is adopted for applying deep learning. We consider several deep learning techniques based on neural networks. Deep learning models are trained to predict wall-normal velocity at y+=10 from wall variables such as wall pressure and shear stresses with database of uncontrolled turbulent channel flow. Among various deep learning techniques, convolutional neural network trained with generative adversarial framework has the best prediction capability. Simple rescaling is applied to predict the flow being controlled, because deep learning models are trained with uncontrolled flow. With the wall-normal velocity predicted by the deep learning based on the wall variable measurements, we conduct active control and obtain a significant amount of skin friction reduction. |
Sunday, November 18, 2018 9:18AM - 9:31AM |
A17.00007: Numerical Analysis of Drag Reduction Effect in Turbulent Pipe Flow with Traveling Wavy Blowing and Suction Shinya Koganezawa, Akihiko Mitsuishi, Takaaki Shimura, Kaoru Iwamoto, Hiroya Mamori, Akira Murata Skin friction drag greatly increases in turbulent flows compared with that in laminar flows. Therefore, reduction of the skin friction drag by flow control is of great significance to contribute to energy saving. It is well known that traveling wave control shows large drag reduction. In the present study, DNS of a turbulent pipe flow was performed in order to clarify mechanisms of drag reduction by traveling wave control. The traveling wave was implemented by blowing and suction at the wall and is expressed as the boundary condition of the wall-normal velocity. Pathlines were analyzed in order to investigate the flow field generated by the control. The analysis indicated that injected particles return toward the wall by the suction flow. Therefore, the flow is “closed” in the region near the wall. Drastic reduction of the random component of Reynolds shear stress in the closed flow suggests that there is no turbulence in the closed flow and it contributes to drag reduction. |
Sunday, November 18, 2018 9:31AM - 9:44AM |
A17.00008: Airfoil Aerodynamic Drag Reduction using Controlled Trapped Vorticity Concentrations Michael DeSalvo, Ari Glezer The aerodynamic performance of an airfoil at low angles of attack (with a fully attached base flow) is improved using fluidic modification of its “apparent” shape by superposition of controlled trapped vorticity concentrations near the surface. In the present wind tunnel investigations, a controlled trapped vorticity concentration is formed on the pressure surface of a NACA 4415 airfoil at x/c = 0.2 using a hybrid flow control actuator consisting of a passive obstruction of scale O(0.01c) with an integral synthetic jet actuator. The actuation frequency [Stact ~ O(10)] is selected to be at least an order of magnitude higher than the characteristic unstable frequencies of the airfoil wake, thereby decoupling the actuation from the global instabilities of the base flow. Jet actuation is used to regulate vorticity accumulation, shedding, and advection in the vicinity of the actuator with minimal changes in skin friction, as shown by detailed PIV measurements. In turn, actuation causes the local static pressure to be altered, leading to a significant reduction in drag with minimal lift penalty (owing to the sense of the trapped vorticity). For example, at α = 4° and Re = 6.1·105, the drag is reduced by 30% of the airfoil profile drag while the lift is reduced by 2%. |
Sunday, November 18, 2018 9:44AM - 9:57AM |
A17.00009: Manipulation of 3D blunt body turbulent wakes: drag reduction and wake equilibrium Yann Haffner, Jacques Borée, Thomas Castelain, Andreas Spohn Combination of passive and active flow control are used to manipulate the turbulent wake of a 3D blunt body in order to analyse the impact of flow asymmetries on aerodynamic drag. An Ahmed-like body with a square-back is mounted in the test section of a wind tunnel to produce a canonical turbulent wake at Re = 500000. By using passive perturbations around the model or by changing its ground clearance, the large-scale asymmetry and dynamics of the unforced recirculation region are modified almost at will. Depending on the initial unforced equilibrium, additional pulsed blowing along all or selected edges of the base produces a very different impact on the drag. On top of a global boat-tailing effect resulting in drag reduction (up to 10%), the reorganization of the recirculation region equilibrium plays a key role in the observed drag changes. In particular the symmetrisation of the wake appears to be one of the main mechanisms involved in drag reduction. This provides key ingredients to adapt forcing strategies for drag reduction in presence of various wake asymmetries and topologies typical of the flow around such body. The change between wake types being essentially caused by minor geometric and flow conditions changes, this is essential for industrial automotive applications. |
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