Abstract:The aerodynamic database construction for conventional aircraft configurations typically employs full sampling across inflow dimensions. Given the substantial distance and weak interference between control surfaces, the strategy of superimposing control effectiveness increments is widely used, which avoids the need for a full combinatorial sampling of all control surface deflections. However, for flying-wing aircraft, the presence of strong aerodynamic interference among multiple closely-spaced control surfaces makes full combinatorial sampling that accounts for these interactions prohibitively expensive. This paper focuses on the critical challenge of constructing an aerodynamic model capable of capturing nonlinear interference between control surfaces under limited combinatorial sample. Firstly, an intelligent aerodynamic modeling method is proposed that integrates a convolutional neural network (CNN) with an engineering model, specifically for scenarios with dense angle-of-attack sampling such as wind tunnel tests. Secondly, to characterize the aerodynamic forces generated by 3 trailing-edge control surface combinations on a low-speed flying-wing configuration, high-fidelity CFD simulations are used to obtain aerodynamic data for single and dual control surface deflections. Finally, a low-fidelity engineering model is constructed using a method that linearly superimposes individual control effectiveness and interference increments between adjacent control surfaces. Then, by introducing an angle-of-attack sequence modeling mechanism, a CNN is applied to further characterize nonlinear interference effects both among the control surfaces and across the angle-of-attack dimension. Results indicate that the proposed method not only improves accuracy by approximately 40% compared to the engineering model, but also reduces the standard deviation of prediction error by over 50% relative to a direct deep neural network model without embedded engineering knowledge. This research significantly enhances both accuracy and robustness of strongly nonlinear aerodynamic modeling for flying-wing aircraft under high-angle-of-attack and large-control-surface-deflection conditions.
