Modelling the unsteady lift of a pitching NACA 0018 aerofoil using state-space neural networks

Abstract

The development of simple, low-order and accurate unsteady aerodynamic models represents a crucial challenge for the design optimisation and control of fluid dynamical systems. In this work, wind tunnel experiments of a pitching NACA 0018 aerofoil conducted at a Reynolds number $Re = 2.8 \times 10^5$ and at different free-stream turbulence intensities are used to identify data-driven nonlinear state-space models relating the time-varying angle of attack of the aerofoil to the lift coefficient. The proposed state-space neural network (SS-NN) modelling technique explores an innovative methodology, which brings the flexibility of artificial neural networks into a classical state-space representation and offers new insights into the construction of reduced-order unsteady aerodynamic models. The work demonstrates that this technique provides accurate predictions of the nonlinear unsteady aerodynamic loads of a pitching aerofoil for a wide variety of angle-of-attack ranges and frequencies of oscillation. Results are compared with a modified version of the Goman–Khrabrov dynamic stall model. It is shown that the SS-NN methodology outperforms the classical semi-empirical dynamic stall models in terms of accuracy, while retaining a fast evaluation time. Additionally, the proposed models are robust to noisy measurements and do not require any pre-processing of the data, thus involving only a limited user interaction. Overall, these features make the SS-NN technique an excellent candidate for the construction of accurate data-driven models from experimental fluid dynamics data, and pave the way for their adoption in applications entailing design optimisation and real-time control of systems involving lift.

Proposal of composite material to improve the efficiency of wind turbine blades in Cuba

“…Para realizar el estudio, se decidió emplear perfiles preexistentes y ampliamente validados. Entre estos, el perfil NACA 0018 se destacó por su versatilidad, siendo adecuado tanto para turbinas eólicas de eje vertical (VAWT) como para turbinas eólicas de eje horizontal (HAWT) (Damiola et al, 2024;Güler et al, 2024;Lam & Leung, 2018;Lesovoy et al, 2024;Michna & Rogowski, 2022). Esta elección nos brindó un acceso directo a información crucial desde el punto de vista aerodinámico, evitando la necesidad de realizar ensayos propios, que resultan fundamentales al diseñar un perfil desde cero, lo cual permitió a los autores la caracterización del material desde el punto de vista mecánico, además de evaluar su desempeño mediante simulación computacional.…”