Traditional super-resolution reconstruction methods for flow fields use end-to-end mapping to determine the relationship between high- and low-resolution flow field data. The reconstruction quality of these methods depends on the accuracy of the low-resolution data. Ensuring the accuracy of low-resolution data has, thus, become a precondition for super-resolution tasks, and it imposes strict limitations on the applicability of super-resolution reconstruction methods in practical engineering applications. This paper proposes a flow field super-resolution reconstruction method coupled with feature recognition (FRNet) to reduce the dependence on the accuracy of low-resolution data. FRNet uses a feature extractor with identification capabilities to determine the effectiveness of low-resolution flow field characteristics. It recognizes the effective characteristics using a feature distance distribution. Meanwhile, a representation of the obstacle shape and freestream information is introduced to compensate for invalid features and to suppress the influence of low-precision flow field characteristics on the reconstruction results. Different downsampling factors, different density grids, and noise are used to simulate a variety of engineering application scenarios to verify the effectiveness and applicability of the proposed method. The results demonstrate that FRNet has significant advantages over traditional super-resolution reconstruction methods. Our method does not rely on the accuracy of low-resolution data and can effectively mitigate the impact of low-resolution flow field data that do not conform to physical phenomena. This characteristic allows FRNet to exhibit outstanding performance when handling flow field data affected by noise from wind tunnel wall and rack interferences. Consequently, FRNet should prove highly beneficial for the optimization of complex flow fields using super-resolution reconstruction methods.

At present, most e-commerce recommendation systems act only as just a single tool and provide a single recommendation model. However, due to the complexity of e-commerce system itself, different occasions and customers of different identities require different types of recommendation services. In the paper, we study the architecture of complex e-commerce which can collect multiple types of data, use a variety of recommendation algorithms order to meet the needs of different types of recommendation.

The introduction of deep learning has resolved the high-cost issues associated with traditional methods in handling complex aerodynamics problems and is commonly used for simulating fluid behavior and optimizing aircraft design. However, flow field prediction based on deep learning typically encodes the freestream conditions and geometric information into the neural network model concurrently. This encoding scheme makes it difficult for the model to distinguish and deal with the intrinsic differences between these two types of information. As a result, the ability of the model to capture complex flow field features decreases and the difficulty of model fitting increases, which in turn reduces the effectiveness of the model. To solve these problems, this paper proposes the Operator-Convolution MultiModal Fusion Network (OCMMFNet), a new neural network architecture to predict the flow fields of airfoils with various geometries and freestream conditions. The proposed network architecture uses a freestream generalization network to encode the input freestream conditions. The resulting approximate flow field information is combined with the airfoil geometry information and fed into a shape feature compensation network to improve the prediction accuracy. We compare the performance of OCMMFNet with those of a deep operator network(DeepONet) and a vision transformer(ViT) model. When generalizing both freestream conditions and airfoil shapes, OCMMFNet reduces the prediction error in the pressure field by 9.71% and 3.76% compared to DeepONet and ViT, respectively. In tests involving extrapolation of Reynolds numbers, OCMMFNet significantly reduces the prediction error in the pressure field by 13.73% and 11.84% compared to DeepONet and ViT, respectively. The results show that OCMMFNet achieves better prediction accuracy than both DeepONet and ViT and displays superior robustness and generalization ability.