High-Resolution (HR) data on flow fields are critical for accurately evaluating the aerodynamic performance of aircraft. However, acquiring such data through large-scale numerical simulations or wind tunnel experiments is highly resource intensive. This paper proposes a FlowViT-Diff framework that integrates a Vision Transformer (ViT) with an enhanced denoising diffusion probabilistic model for the Super-Resolution (SR) reconstruction of HR flow fields based on low-resolution inputs. It provides a quick initial prediction of the HR flow field by optimizing the ViT architecture, and incorporates this preliminary output as guidance within an enhanced diffusion model. The latter captures the Gaussian noise distribution during forward diffusion and progressively removes it during backward diffusion to generate the flow field. Experiments on various supercritical airfoils under different flow conditions show that FlowViT-Diff can robustly reconstruct the flow field across multiple levels of downsampling. It obtains more consistent global and local features than traditional SR methods, and yields a 3.6-fold increase in its training speed via transfer learning. Its accuracy of reconstruction of the flow field is 99.7% under ultra-low downsampling. The results demonstrate that FlowViT-Diff not only exhibits effective flow field reconstruction capabilities, but also provides two reconstruction strategies, both of which show effective transferability.

To address the convergence challenges faced by traditional deep learning models in the inverse design of three-dimensional aircraft under supersonic conditions, a generative gated Denoising Diffusion Probabilistic Model (DDPM) based on a priori prediction model guidance is proposed. This model integrates aerodynamic performance prediction and aircraft shape inverse design through the gating mechanism, aiming to enhance both prediction accuracy and design efficiency while overcoming the training difficulties encountered in handling highly nonlinear problems with traditional models. A Deep Neural Network (DNN) is constructed to serve as the a priori prediction model to obtain preliminary aerodynamic performance predictions and geometric shape estimates. Based on these initial results, the gated DDPM uses the predictive information from the DNN and employs diffusion and reverse diffusion processes to generate design outcomes. In this process, Gaussian noise is gradually added during the diffusion phase, and data distribution is restored in the reverse diffusion phase. This mechanism enhances both convergence and predictive accuracy in complex aerodynamic design tasks. The effectiveness of the gated DDPM is validated using an axisymmetric aircraft dataset. Compared to the prior model, the gated DDPM can control the relative error of most aerodynamic parameters within 0.75% in inverse design tasks, significantly outperforming the DNN model. The results demonstrate that the proposed method can effectively enhance the accuracy of aerodynamic performance prediction and shape inverse design in aircraft design.

To optimize the aerodynamic shape of high-speed helicopter rotor airfoils, a multi-objective optimization framework is proposed based on deep learning. Firstly, a deep neural network is constructed as a surrogate model to predict the aerodynamic coefficients of rotor airfoils. The rotor airfoil SC1095 is selected as the baseline airfoil. The Class function/Shape function Transformation (CST) method is employed to parameterize the airfoil, and the Latin hypercube sampling method is used to generate the airfoil dataset for training deep neural networks. Then, comprehensively considering the aerodynamic performance of multiple design points such as forward flight, maneuvering and hover of the helicopter, a multi-objective aerodynamic shape optimization of the high-speed helicopter rotor airfoil is conducted by combining the deep neural network surrogate model with the multi-island genetic algorithm. The optimization results show that compared with the baseline airfoil, the optimized airfoil can significantly improve its forward flight performance without compromising hover and maneuvering performance. Finally, a rigid coaxial rotor is generated using the baseline and optimized airfoil respectively. The aerodynamic performance of these rotors in forward flight is computed and analyzed. The results indicate that the optimized airfoil significantly enhances the aerodynamic performance of the high-speed helicopter rotor.

The decrease in aerodynamic performance caused by the shock-induced dynamic stall of an advancing blade and the dynamic stall of a retreating blade at low speed and high angles of attack limits the flight speed of a helicopter. However, little research has been carried on the flow control methods employed to suppress both the dynamic stall induced by a shock wave and the dynamic stall occurring at high angles of attack. The dynamic stall suppression of a rotor airfoil by Co-Flow Jet (CFJ) is numerically investigated in this work. The flowfield of the airfoil is simulated by solving Reynolds Averaged Navier-Stokes equations based on the sliding mesh technique. Firstly, to improve the effect of a traditional CFJ on suppressing rotor airfoil shock-induced dynamic stall, an improved CFJ—a CFJ-sloping slot is proposed. Research shows that the CFJ-sloping slot suppresses the shock-induced dynamic stall more effectively than a traditional CFJ. Moreover, the improved CFJ can also suppress the dynamic stall of rotor airfoil at low speed and high angles of attack. The improved CFJ proposed in this paper is an effective flow control method that simultaneously suppresses the dynamic stall of the advancing and retreating blades. The mechanism of the improved CFJ in suppressing the dynamic stall of the rotor airfoil is studied, and a comparison is made between the improved CFJ and the traditional CFJ in terms of dynamic stall suppression at high and low speed. Finally, the effect of improved CFJ parameters (the jet momentum coefficient, the position of the injection/suction slot, and the size of the injection/suction slot) on shock-induced dynamic stall suppression is analyzed.