Exploring Strategies for Closed-Loop Cavity Flow Control

Abstract

One of the current three main thrust areas of the Collaborative Center of Control Science (CCCS) at The Ohio State University is feedback control of aerodynamic flows. Synergistic capabilities of the flow control team include all of the required multidisciplinary areas of flow simulations, low-dimensional and reduced-order modeling, controller design, and experimental integration and implementation of the components along with actuators and sensors. The initial application chosen for study is closed-loop control of shallow subsonic cavity flows. We have made significant progress in the development of various components necessary for reduced-order model based control strategy, which will be presented and discussed in this paper. Stochastic estimation was used to show that surface pressure measurements along with the reduced-order model based on flow-field variables can be used for closed-loop control. Linear controllers such as H ∞ , Smith predictor, and PID were implemented experimentally with various degrees of success. The results showed limitations of linear controllers for cavity flow with inherent nonlinear dynamics. Detailed experimental work further explored the physics and showed the highly non-linear nature of the cavity flow and the effects of forcing on the flow structure.

Novel Neuronal Activation Functions for Feedforward Neural Networks

“…structured networks, which are potentially useful for hardware implementations and are generally more preferable than those introducing computational intensity for better performance. Then we consider the cases with R = 8, 10,12,14,16,18,20,25,40 and 100 to see how the performance is influenced as the number of neurons in the hidden layer increases.…”

“…2a,b. The goal of the modeling problem is to predict the floor pressures based on the pressure measurements from several critical locations of the cavity geometry [13][14][15]. These locations include the signal to the host computer (x 1 ), the output of the actuator (x 2 ), the measurement at the receptivity point (x 3 ), the measurement at cavity trailing edge (x 5 ) and the measurements before and after the rectangular cutout (x 4 , x 7 ) are also depicted in Fig.…”

Reduced-order Model-based Feedback Controller Design for Subsonic Cavity Flows

“…where α = π/6. The reader is referred to Samimy et al 2 for details. The coefficients of the POD model have been derived from CFD simulations conducted in absence of external input (V (t) = 0, i.e., the baseline case) and in presence of an external sinusoidal excitation of the form V (t) = A sin(2πf c t), with f c = 500 Hz and f c = 900 Hz respectively.…”

“…The flow control group within the Collaborative Center of Control Science at The Ohio State University has been working on a collaborative approach towards the design of reduced-order model-based feedback control of cavity flows. 1,2 The components include obtaining detailed data using numerical simulation of or experimental work in the cavity flow, derivation of reduced-order model of the flow dynamics, 3 controller design, 4,5 and experimental implementation of the controller. 6,7 Cavity flow control problem has received significant attention in recent years.…”

Nonlinear Multi-Time-Delay Stochastic Estimation: Application to Cavity Flow and Turbulent Channel Flow

“…Artificial Neural Networks have been employed in fluid mechanics problem in several contexts. For instance, ANNs have been used to develop blackbox dynamical models as an alternative to the well-known Galerkin projection method, [13,14], or for control design, [15,16]. Application of Neural Networks for control of turbulent channel flow is discussed by Lee et al [17].…”