Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning

• URL: http://arxiv.org/abs/2510.07106v1
• Date: Wed, 08 Oct 2025 14:59:29 GMT
• Title: Active Control of Turbulent Airfoil Flows Using Adjoint-based Deep Learning
• Authors: Xuemin Liu, Tom Hickling, Jonathan F. MacArt,
• Abstract summary: We train active neural-network flow controllers to optimize lift-to-drag ratios in turbulent airfoil flows at Reynolds number $5times104$ and Mach number 0.4.<n>The trained flow controllers significantly improve the lift-to-drag ratios and reduce flow separation for both two- and three-dimensional air flows.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We train active neural-network flow controllers using a deep learning PDE augmentation method to optimize lift-to-drag ratios in turbulent airfoil flows at Reynolds number $5\times10^4$ and Mach number 0.4. Direct numerical simulation and large eddy simulation are employed to model compressible, unconfined flow over two- and three-dimensional semi-infinite NACA 0012 airfoils at angles of attack $\alpha = 5^\circ$, $10^\circ$, and $15^\circ$. Control actions, implemented through a blowing/suction jet at a fixed location and geometry on the upper surface, are adaptively determined by a neural network that maps local pressure measurements to optimal jet total pressure, enabling a sensor-informed control policy that responds spatially and temporally to unsteady flow conditions. The sensitivities of the flow to the neural network parameters are computed using the adjoint Navier-Stokes equations, which we construct using automatic differentiation applied to the flow solver. The trained flow controllers significantly improve the lift-to-drag ratios and reduce flow separation for both two- and three-dimensional airfoil flows, especially at $\alpha = 5^\circ$ and $10^\circ$. The 2D-trained models remain effective when applied out-of-sample to 3D flows, which demonstrates the robustness of the adjoint-trained control approach. The 3D-trained models capture the flow dynamics even more effectively, which leads to better energy efficiency and comparable performance for both adaptive (neural network) and offline (simplified, constant-pressure) controllers. These results underscore the effectiveness of this learning-based approach in improving aerodynamic performance.

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