Deep learning strategies applied to closed-loop control of unsteady flows

• Grant number: 22/00469-8
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• Start date: August 01, 2022
• End date: March 31, 2023
• Field of knowledge: Engineering - Aerospace Engineering - Aerodynamics
• Principal Investigator: William Roberto Wolf
• Grantee: Tarcísio Costa Déda Oliveira
• Supervisor: Scott Thomas McGregor Dawson
• Host Institution: Faculdade de Engenharia Mecânica (FEM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Institution abroad: Illinois Institute of Technology (IIT), United States
• Associated to the scholarship: 19/19179-7 - Flow control strategies for unsteady flows involving transition and turbulence, BP.DR

Abstract

The present proposal describes the research plan associated to the research internship (BEPE) of PhD student Tarcísio Costa Déda Oliveira. Tarcísio will develop his research in the Mechanical, Materials, and Aerospace Engineering Department at the Illinois Institute of Technology, Chicago, Illinois, USA. During this period, he will work under the supervision of Prof. Scott Dawson, who has extensive expertise in the fields of fluid mechanics, dynamical systems, control theory, and data science, which are topics related to the current research proposal. Our goal is to apply deep learning techniques to model complex nonlinear flow systems as well as to develop control strategies assisted by deep neural network models. Surrogate models can enable the design of control techniques that, in turn, can be tested in high-fidelity numerical environments for validation. The applications of closed-loop flow control encompass different objectives, such as lift enhancement, drag reduction, acoustic noise reduction and transition delay. In this study, we will apply neural networks to be trained as dynamic surrogate models of fluid flow systems with control inputs. Data from a high-fidelity solver is obtained to train reduced order models that can enable quick control tests and allow for model-based design of controllers. In this work, the control objective will consist in the attenuation of unsteady characteristics of the studied flows either by stabilizing systems that present oscillatory behavior due to nonlinearities leading to limit cycles; or by counteracting perturbations that grow in the flow field. (AU)

Scientific publications

(4)

(The scientific publications listed on this page originate from the Web of Science or SciELO databases. Their authors have cited FAPESP grant or fellowship project numbers awarded to Principal Investigators or Fellowship Recipients, whether or not they are among the authors. This information is collected automatically and retrieved directly from those bibliometric databases.)

DEDA, TARCISIO; WOLF, WILLIAM R.; DAWSON, SCOTT T. M.. Backpropagation of neural network dynamical models applied to flow control. THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS, v. 37, n. 1, p. 25-pg., 2023-02-25. (13/08293-7, 21/06448-0, 22/00469-8, 19/19179-7)

DEDA, TARCISIO C.; WOLF, WILLIAM R.; DAWSON, SCOTT T. M.. Application of Neural Network Surrogate Models for Flow Control. AIAA AVIATION 2023 FORUM, v. N/A, p. 10-pg., 2023-01-01. (13/08293-7, 19/19179-7, 21/06448-0, 22/00469-8)

DEDA, TARCISIO C.; WOLF, WILLIAM R.; DAWSON, SCOTT T. M.. Assessing the robustness of neural networks trained for closed loop flow stabilization. AIAA AVIATION FORUM AND ASCEND 2024, v. N/A, p. 10-pg., 2024-01-01. (19/19179-7, 13/08293-7, 21/06448-0, 22/00469-8)

DEDA, TARCISIO C.; WOLF, WILLIAM R.; DAWSON, SCOTT T. M.. Neural networks in feedback for flow analysis and control. PHYSICAL REVIEW FLUIDS, v. 9, n. 6, p. 35-pg., 2024-06-12. (22/00469-8, 13/08293-7, 21/06448-0, 19/19179-7)