Improving the understanding of unsteady aerodynamic flows via high-fidelity simulations, analytical modeling and deep learning techniques

• Grant number: 22/09196-4
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• Start date: September 01, 2022
• End date: April 30, 2026
• Field of knowledge: Engineering - Aerospace Engineering - Aerodynamics
• Principal Investigator: William Roberto Wolf
• Grantee: Renato Fuzaro Miotto
• 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): 23/13431-1 - Analysis of unsteady fluid flows through flow imaging and neural implicit functions, BE.EP.PD

Abstract

Unsteady aerodynamic loads generated during dynamic stall play a critical role in determining both the mechanical life span and performance of unsteady lifting devices such as helicopter rotors and wind turbine blades. To control these loads, it is required both an understanding of the unsteady flow conditions as well as providing mechanisms for prescribing the ensuing flow-wing interactions. However, the temporal and spatial complexity of unsteady separated flows renders them difficult to fully characterize and understand. It comes as no surprise that, thus far, unsteady separated flows have defied obtaining general analytical solutions. Although potentially accurate, numerical simulations of unsteady separated flows require large amounts of computational time and the application of intricate numerical algorithms. Experiments are also complex and time consuming, besides requiring expensive apparatus. As such, the broad parameter space encompassed by unsteady separated flows hamper exhaustive characterization by computational or experimental means. Fortunately, the emerging of machine (deep) learning techniques could provide more ideas for leveraging models. The era of big data and the significantly improved computing power have laid a good foundation for employing machine learning techniques to unsteady fluid mechanics applications. For this reason, here, these techniques are used with the hope of finding the mapping relation between the flow structures and the underlying airfoil responses. Dynamic stall is taken here as the main application since the flow has a common structure owning certain complexity as well. Despite that, the concepts applied herein can be easily extended to other branches of unsteady fluid dynamics. That said, based on any fluid property from the unsteady flowfield, the network between the existing flow structures and some concerned flow feature will be constructed. Here, we also aim at reusing or transferring information from previously learned tasks to extract quantitative information from available flow visualizations. Despite substantial advances in experimental fluid mechanics, the use of measurements to reliably infer fluid properties, like density, velocity, pressure or stress components, is not a straightforward task. This information, though, comes natural to CFD practitioners. So, in our study, we address the question of using the information learned from numerical simulation datasets to extract fluid properties from experiments that until then would be very complicated or even impossible to obtain. (AU)

Scientific publications

(8)

(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)

MIOTTO, RENATO; WOLFT, WILLIAM. Prediction of airfoil dynamic stall response using convolutional neural networks. AIAA AVIATION 2023 FORUM, v. N/A, p. 10-pg., 2023-01-01. (13/07375-0, 13/08293-7, 22/09196-4, 21/06448-0)

MIOTTO, RENATO F.; WOLF, WILLIAM R.. Flow imaging as an alternative to non-intrusive measurements and surrogate models through vision transformers and convolutional neural networks. Physics of Fluids, v. 35, n. 4, p. 18-pg., 2023-04-01. (22/09196-4, 13/08293-7, 21/06448-0, 13/07375-0)

MIOTTO, RENATO; WOLF, WILLIAM; GAITONDE, DATTA; VISBAL, MIGUEL. Pitch-Plunge Equivalence in Dynamic Stall of Ramp Motion Airfoils. ENERGY AND BUILDINGS, v. 277, p. 14-pg., 2022-11-14. (22/09196-4, 13/08293-7, 21/06448-0, 17/10795-1, 19/02335-6, 13/07375-0)

MIOTTO, RENATO; WOLF, WILLIAM; GAITONDE, DATTA; VISBAL, MIGUEL. Pitch-Plunge Equivalence in Dynamic Stall of Ramp Motion Airfoils. AIAA JOURNAL, v. 61, n. 1, p. 15-pg., 2023-01-01. (22/09196-4, 13/08293-7, 21/06448-0, 17/10795-1, 19/02335-6, 13/07375-0)

MIOTTO, RENATO; ZIGUNOV, FERNANDO; WOLF, WILLIAM. Assessment of Pressure Estimation Methods Using Image Velocimetry Data: Insights from Unstructured Mesh Analysis. AIAA AVIATION FORUM AND ASCEND 2024, v. N/A, p. 13-pg., 2024-01-01. (13/08293-7, 22/09196-4, 21/06448-0, 23/13431-1)

FEITOSA, LUCAS; MIOTTO, RENATO; WOLF, WILLIAM. Analysis of dynamic stall for a simplified single blade vertical axis wind turbine configuration. AIAA AVIATION FORUM AND ASCEND 2024, v. N/A, p. 10-pg., 2024-01-01. (22/08567-9, 13/07375-0, 13/08293-7, 22/09196-4, 21/06448-0)

FEITOSA, LUCAS; MIOTTO, RENATO; WOLF, WILLIAM. Analysis of intermittent vortex pairing for a transitional flow past a NACA0012 airfoil via a multidimensional empirical mode decomposition. AIAA AVIATION FORUM AND ASCEND 2024, v. N/A, p. 9-pg., 2024-01-01. (13/08293-7, 22/09196-4, 21/06448-0, 22/08567-9)

FEITOSA, LUCAS; MIOTTO, RENATO; WOLF, WILLIAM. Analysis of coherent structures in dynamic stall via empirical mode decomposition and dynamic mode decomposition. AIAA AVIATION 2023 FORUM, v. N/A, p. 12-pg., 2023-01-01. (22/08567-9, 13/08293-7, 21/06448-0, 22/09196-4, 13/07375-0)