The conceptual design phase of aircraft requires extensive analysis and comparison of the aerodynamic performance for numerous configuration candidates. High-fidelity methods like Computational Fluid Dynamics (CFD) are computationally prohibitive. Rapid analysis tools like the Vortex Lattice Method (VLM) often fail to meet accuracy requirements. Single-fidelity data-driven surrogate models, which directly map geometric design variables to aerodynamic characteristics, suffer from prediction accuracy that is heavily dependent on the size and selection of training samples. To address the trade-off between accuracy, efficiency, and sample size, a rapid aerodynamic prediction method that fuses multi-fidelity data from VLM and CFD is proposed. VLM is employed to obtain low-fidelity aerodynamic parameters of a simplified wing surface model efficiently. These parameters, together with the original design variables, Mach number, and wetted surface area, form the enhanced input vector to improve the model's understanding of aerodynamic characteristics. A Radial Basis Function Neural Network (RBFNN) is employed. Based on a correction strategy using multi-fidelity difference functions, a mapping is established from low-fidelity data of simplified wing surface geometries to high-fidelity data of complete aircraft geometries, achieving the goal of reducing training sample size and improving simulation efficiency. The method is validated using a Lambda-wing UAV dataset. The results show that with 25 training samples, the high-fidelity aerodynamic parameters of UAV can be accurately predicted. The prediction accuracy for all parameters outperforms that of single-fidelity data-driven surrogate models, and is comparable to the co-Kriging method, with certain parameters even demonstrating superior prediction performance. Test samples are designed for typical engineering scenarios, with the relative prediction error of each aerodynamic coefficient not exceeding 3%, and the calculation time reduced by two orders of magnitude compared with CFD calculations. The general applicability of the method is further verified through the testing of extrapolated samples. The proposed fusion method is applied to the optimization of the Lambda-wing UAV scheme. The cruise lift-to-drag ratio of the optimal scheme is increased by 13.2%, and the predicted relative error is less than 1.2%. This effectively enhanced the design efficiency and holds certain prospects for engineering application.

Ski-jump ramp is of great significance to improve the takeoff performance of carrier based aircraft. However, the ski-jump ramp produces stronger air-wake than that of the flat deck, resulting in unstable air flow above the deck and near the glide line, that can affect the takeoff and landing safety of the carrier based aircraft. Using the DES turbulence model mixed with RANS and LES, CFD computations were performed to simulate air wakes of the flat and ski-jump decks. The comparison shows that there is a huge separation bubble behind the ski-jump ramp, which has a great impact on the air flow on the upper surface of the deck and near the landing glide line, leading to the reduction of the x-velocity of the wind over the deck as well as the increase of the takeoff and landing speeds of the carrier aircraft. Meanwhile, the amplitude of velocity fluctuations is also increased by the separation bubble, and this brings difficulty in accurate track control of the carrier aircraft. In order to effectively suppress the air wake caused by the ski-jump ramp, the control based on cylindrical vortex generators (CVG) has been proposed. Due to the existence of CVG, the flow around the upper surface of the ski-jump ramp is changed from separated flow to attached flow, and it greatly reduces the fluctuation amplitude of the air flow on the upper surface of the ski-jump deck. Compared to the case without CVG, the x-velocity of the wind over the deck is increased, and the PSD of the wind velocity fluctuation is reduced by 20 to 40 dB, restored to the same level as that of the flat deck, thus effectively improving the take-off and landing safety of the carrier aircraft.