Nonlinear Adaptive Approach to Microjet-Based Flow Separation Control

Abstract

Boundary layer separation, a critical phenomenon in the operation of aerodynamic surfaces, limits the performance of compressor and turbine blades, fixed and rotary wings, as well as bluff bodies moving through a fluid. Flow separation leads to increased drag, decreased lift, and unpredictable vibrations due to unsteadiness. On these systems, effective control of separation could provide greater maneuverability and performance, and reduced vibration. Separated flow is a macro-scale phenomenon governed by complex flow interactions but can be controlled by micro-scale actuation. Recently, the emergence of closed loop methods has enhanced robustness. Modern processors enable the use of sophisticated adaptive control methods that achieve separation control with adaptive models. This paper considers control of flow separation over a NACA-0025 airfoil using microjet actuators. Experimental results are presented for a novel approach to Nonlinear Model Predictive Control, Adaptive Sampling Based Model Predictive Control (Adaptive SBMPC), which applies the Minimal Resource Allocation Network algorithm for nonlinear system identification and the Sampling Based Model Predictive Optimization algorithm to achieve effective nonlinear control. Through pressure data and flow characterization from wind tunnel experiments, effective and robust separation control is demonstrated. The methods computational efficiency is sufficient for successful real time experimental implementation.

Mitigation of Shock-Induced Separation Using Square-Shaped Micro-Serrations—A Preliminary Study

“…Passive control includes the use of a micro-vortex generator (MVG) [13][14][15], a local wall modification in the form of a bump [16,17], and a backward facing step [18,19]. Active control includes the application of boundary layer bleed/suction ahead of the shock-induced interaction [20,21], steady microjets [22,23], plasma jets [24,25], and spark jets [26]. Active control has a range of advantages, and researchers also prefer complex flow control systems that can be actively controlled with feedback.…”

Experimental Investigation on Boundary Layer Control and Pressure Performance for Low Reynolds Flow with Chemical Reaction

“…Pour et al [10] introduced a fluid microjet into the boundary layer to increase fluid momentum and numerically analyzed the control of a separation bubble behind a ramp. Reese et al [11] experimentally considered the control of flow separation over a NACA-0025 airfoil using microjet actuators, and the experimental results are presented for a novel approach to nonlinear model predictive control. Petha et al [8] focused on the self-excited shock train flow in an isolator and its control by partial removal of the boundary layer to examine the variation in the inlet to outlet pressure ratio with and without suction flow control.…”

Adaptive separation control of a laminar boundary layer using online dynamic mode decomposition

“…(Platt 1991; Akers & Bernstein 1997; Reese et al. 2016). Alternatively, a probabilistic approach can be adopted to model and control fluid flows, such as cluster-based reduced-order modelling (Kaiser et al.…”

“…Dynamical models can be identified by input/output parameter sweeps using system identification techniques (neural network, ARMARKOV, etc.) (Platt 1991;Akers & Bernstein 1997;Reese et al 2016). Alternatively, a probabilistic approach can be adopted to model and control fluid flows, such as cluster-based reduced-order modelling (Kaiser et al 2014(Kaiser et al , 2017.…”