Flow control strategies for unsteady flows involving transition and turbulence

• Grant number: 19/19179-7
• Support Opportunities: Scholarships in Brazil - Doctorate
• Start date: October 01, 2019
• End date: November 30, 2023
• Field of knowledge: Engineering - Mechanical Engineering - Transport Phenomena
• Principal Investigator: William Roberto Wolf
• Grantee: Tarcísio Costa Déda Oliveira
• Host Institution: Faculdade de Engenharia Mecânica (FEM). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
• Associated research grant: 13/08293-7 - CCES - Center for Computational Engineering and Sciences, AP.CEPID
• Associated scholarship(s): 22/00469-8 - Deep learning strategies applied to closed-loop control of unsteady flows, BE.EP.DR

Abstract

The study of unsteady flows finds application in several industrial processes in mechanical and Aerospace Engineering. For example, typical flows developing over aircraft wings and wind turbine blades are turbulent and, therefore, unsteady. In certain configurations, transition to turbulence may be also an important flow feature leading to unsteadiness. For most flows, transition and turbulence are directly related to drag increase and noise generation, which may be undesired flow features in some engineering devices and processes. On the other hand, mixing and heat transfer enhancement are also achieved by turbulent flows. For some of these cases, it is important to develop flow control strategies to reduce drag and noise. In this work, we will investigate techniques of active flow control well suited for applications in unsteady flows involving transition and turbulence. These techniques will be employed both in high-fidelity numerical simulations and reduced-order models. While the former methods will be used to validate the strategies, the latter will be used for fast turnaround testing. Several control strategies will be analyzed including some recent techniques based on theory of chaos and deep learning. Applications will span a range of problems including boundary layers, cavity and channel flows which are found in wings, landing gears and propulsion systems. (AU)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

DEDA, TARCISIO C.; WOLF, WILLIAM R.. Extremum Seeking Control Applied to Airfoil Trailing-Edge Noise Suppression. AIAA JOURNAL, v. 60, n. 2, p. 21-pg., 2022-02-01. (19/19179-7, 13/08293-7)

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)

Academic Publications

(References retrieved automatically from State of São Paulo Research Institutions)

OLIVEIRA, Tarcísio Costa Déda. Controle de escoamentos não estacionários em malha fechada: abordagens não lineares via busca extremal e redes neurais. 2023. Doctoral Thesis - Universidade Estadual de Campinas (UNICAMP). Faculdade de Engenharia Mecânica Campinas, SP.