| Author(s): |
Deda, Tarcisio C.
;
Wolf, William R.
;
Dawson, Scott T. M.
Total Authors: 3
|
| Document type: | Journal article |
| Source: | AIAA AVIATION FORUM AND ASCEND 2024; v. N/A, p. 10-pg., 2024-01-01. |
| Abstract | |
We explore the application of neural networks for developing surrogate models and controllers aiming for closed-loop stabilization. Leveraging a grid of velocity sensors in a confined cylinder wake, convergence to equilibrium is demonstrated. The methodology, developed in previous work, involves training neural network surrogate models (NNSMs) that are leveraged for designing neural network controllers (NNCs). During the training of NNSMs, sensor selection minimizes the number of probes required by implementing a sparsity layer within the neural network architecture. Equilibrium estimation, required for providing a control setpoint, is achieved through the Newton method applied to the NNSMs. The networks are trained iteratively, with progressive improvements as more data near equilibrium becomes available. The present work focuses on analyses conducted to assess the robustness of these trained NNCs. First, we show the main results involving a series of tests conducted with the Lorenz system, in which we verify the behavior of the closed-loop system subject to phenomena such as plant variations, measurement noise and disturbances. A controller is then trained to stabilize a confined cylinder flow at Reynolds number Re = 150. We subsequently demonstrate that the controller is able to stabilize unstable flows at lower and higher Reynolds numbers than that for which the NNC was trained. As the Reynolds number increases, we find that reducing the timestep at which control is applied can be required to achieve complete stabilization. (AU) | |
| FAPESP's process: | 19/19179-7 - Flow control strategies for unsteady flows involving transition and turbulence |
| Grantee: | Tarcísio Costa Déda Oliveira |
| Support Opportunities: | Scholarships in Brazil - Doctorate |
| FAPESP's process: | 13/08293-7 - CCES - Center for Computational Engineering and Sciences |
| Grantee: | Munir Salomao Skaf |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 21/06448-0 - High-fidelity numerical simulations applied in unsteady aerodynamics, turbulence and aeroacoustics |
| Grantee: | William Roberto Wolf |
| Support Opportunities: | Research Grants - Young Investigators Grants - Phase 2 |
| FAPESP's process: | 22/00469-8 - Deep learning strategies applied to closed-loop control of unsteady flows |
| Grantee: | Tarcísio Costa Déda Oliveira |
| Support Opportunities: | Scholarships abroad - Research Internship - Doctorate |