Abstract
These last years several research works have studied the application of Micro-Electro-Mechanical Systems (MEMS) for aerodynamic active flow control. Controlling such MEMS-based systems remains a challenge. Among the several existing control approaches for time varying systems, many of them use a process model representing the dynamic behavior of the process to be controlled. The purpose of this paper is to study the suitability of an artificial neural network first to predict the flow evolution induced by MEMS, and next to optimize the flow w.r.t. a numerical criterion. To achieve this objective, we focus on a dynamic flow over a backward facing step where MEMS actuators velocities are adjusted to maximize the pressure over the step surface. The first effort has been to establish a baseline database provided by computational fluid dynamics simulations for training the neural network. Then we investigate the possibility to control the flow through MEMS configuration changes. Results are promising, despite slightly high computational times for real time application.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
