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 macroscale phenomenon governed by complex flow interactions, but it can be controlled by microscale 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, referred to as adaptive sampling-based model predictive control, 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 method’s computational efficiency is sufficient for successful real-time experimental implementation.