Bulletin of the American Physical Society
63rd Annual Meeting of the APS Division of Fluid Dynamics
Volume 55, Number 16
Sunday–Tuesday, November 21–23, 2010; Long Beach, California
Session RJ: Flow Control VII |
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Chair: Hamid Johari, California State University, Northridge Room: Long Beach Convention Center 201A |
Tuesday, November 23, 2010 3:05PM - 3:18PM |
RJ.00001: Detection and estimation of the instantaneous flow topology on an airfoil using proper orthogonal decomposition Jurgen Seidel, Casey Fagley, Tom McLaughlin For the control of the lift distribution on a wing, the local flow state has to be known instantaneously, in particular, the location of critical points in the flow topology such as stagnation point, separation point and reattachment point. Unsteady CFD simulations are used to determine the flow field around a Naca 0018 airfoil at moderate Reynolds number. These simulations are then analyzed using Proper Orthogonal Decomposition (POD) to develop a database of flow states at a wide range of angles of attack. In addition, POD is performed using data on the airfoil surface. A mapping between the two databases is used to develop a global flow state estimator. Robustly estimating the critical points in the flow topology in real time allows for the~formulation~of a Reduced Order Model (ROM) which relates flow field characteristics and surface data, including the effect of controlled forcing input. The accuracy of this model and its efficacy for developing feedback control strategies for the control of the lift distribution are determined. [Preview Abstract] |
Tuesday, November 23, 2010 3:18PM - 3:31PM |
RJ.00002: Linear proportional-integral control of turbulent channel flow for drag reduction Euiyoung Kim, Haecheon choi Choi, Moin \& Kim (1994, JFM) applied an opposition control, $v_w = - v_{y^+\approx 10}$, to turbulent channel flow and obtained about 25 \% drag reduction, where $v_w$ is the blowing and suction at the wall, and $v$ is the wall-normal velocity. The idea in that study was to provide a distributed blowing/suction at the wall opposite to the induced motion by the near-wall streamwise vortices and to reduce their strength, resulting in drag reduction. In the present study, we reconsider this control problem from the view point of linear proportional- integral-differential control. The opposition control by Choi et al. (1994) is a proportional control and thus contains steady- state errors. In other words, the target sensing velocity does not go to zero ($v_{y^+\approx 10} \ne 0$) even after control. To reduce this steady-state errors, we introduce a proportional- integral (PI) control, $v_w = - \alpha~ v_{y^+_s} - \beta \int v_ {y^+_s} dt$, where $\alpha$ and $\beta$ are the feedback gains, and $y^+_s$ is the sensing location above the wall. As a result of applying the PI control, the steady-state errors are significantly reduced and the effective sensing region becomes wide. The detailed results by varying the feedback gains and sensing location will be shown in the presentation. [Preview Abstract] |
Tuesday, November 23, 2010 3:31PM - 3:44PM |
RJ.00003: Adaptive Observation with Vehicle Dynamics David Zhang, Thomas Bewley Adaptive Observation (AO) studies mobile sensor deployment strategies to improve the estimation and forecast of various physical systems. Of the many approaches to the AO problem, few incorporate the dynamics of moving sensors into the trajectory planning algorithm. We propose a new AO algorithm which plans trajectories such that vehicle dynamics are inherently satisfied. [Preview Abstract] |
Tuesday, November 23, 2010 3:44PM - 3:57PM |
RJ.00004: Experimental study on a three-dimensional riblet with particle image velocimetry Hideyuki Miki, Kaoru Iwamoto, Akira Murata Experimental study on a new three-dimensional (3-D) blade riblet is carried out in a two-dimensional channel. The lateral spacing of our 3-D riblet surface is periodically changed in the streamwise direction. The flow structure over the 3-D riblet was analyzed in the turbulent flow field by using 2-D Particle Image Velocimetry (PIV) on a vertical ($x-y)$ and a horizontal ($x-z)$ plane. The turbulence statistics were compared with the corresponding flow over the flat surface in an attempt to identify the drag-reduction mechanism. Under a drag-reducing condition, the mean velocity profile showed upward shift in the log-law region. The streamwise, spanwise velocity fluctuations and the Reynolds shear stress were decreased, whereas the wall-normal velocity fluctuation was increased. The quadrant analysis of the Reynolds shear stress provides detailed information on the contributions to the total turbulence production from various events occurring in the flows. The 3-D riblets intensified the Reynolds shear stress producing event (second and forth quadrants). On the other hand, it was interesting to note that the first (outward) and third (inward) quadrants are dramatically increased compared with the smooth surface, leading to the drag-reducing effect. [Preview Abstract] |
Tuesday, November 23, 2010 3:57PM - 4:10PM |
RJ.00005: Real-time turbulent plume estimation with mobile sensors Thomas Bewley, Christopher Colburn, David Zhang, Joseph Cessna, Nicholas Morozovsky, Andrew Cavender, Christopher Schmidt-Wetekam Ensemble methods for estimating turbulent fluid systems are efficient methods for quantifying uncertainties in nonlinear, high-dimensional systems. Many real-time estimation algorithms for large-scale fluid systems have been tested and validated by the weather/oceanic forecasting communities, but (generally speaking) these methods have not been used for short time-scale and short length-scale models. We present estimation results for a contaminant plume release experiment. In this experiment, a passive scaler is released at a known location in a small domain and a turbulent environment. Mobile robots are deployed to measure wind velocity and plume concentration. Measurements are assimilated to estimate the wind field and quantify the uncertainty in the estimate, which is then used to plan waypoints for future measurements. [Preview Abstract] |
Tuesday, November 23, 2010 4:10PM - 4:23PM |
RJ.00006: Game-theoretic Kalman Filter Christopher Colburn, Thomas Bewley The Kalman Filter (KF) is celebrated as the optimal estimator for systems with linear dynamics and gaussian uncertainty. Although most systems of interest do not have linear dynamics and are not forced by gaussian noise, the KF is used ubiquitously within industry. Thus, we present a novel estimation algorithm, the Game-theoretic Kalman Filter (GKF), which intelligently hedges between competing sequential filters and does not require the assumption of gaussian statistics to provide a ``best" estimate. [Preview Abstract] |
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