2results about How to "Avoid iterative calculations" patented technology

This invention relates to the field of high-altitude work equipment technology and discloses a method for reconstructing the flow field in a supersonic isolation section by integrating CNN and FCNN. The method includes the following steps: acquiring wall pressure data and schlieren image sequences in a wind tunnel test system; using synchronous light signals for time alignment and preprocessing; constructing a training dataset containing normalized pressure feature vectors and a matrix of real flow field schlieren images; constructing a parallel dual-branch deep learning model; using a convolutional neural network branch to reshape the pressure data into a matrix to extract spatial structure features; and using a fully connected neural network branch to directly extract numerical correlation features. The outputs of both are merged in a feature fusion layer. After optimizing the model parameters using the training dataset, the real-time acquired wall pressure is input into the trained model, and the reconstructed flow field grayscale image is output. This invention solves the problem of difficulty in reconstructing complex flow fields from sparse pressure data and achieves high-precision real-time monitoring of the flow field in a supersonic isolation section.

This invention discloses a method for evaluating the self-cleaning performance of a product's microstructure surface. The method includes: treating the product's microstructure surface as a micropillar array surface; constructing a spreading model of a composite droplet on the micropillar array surface; the spreading model taking the micropillar size and composite droplet properties as inputs; calculating the spreading radius and maximum spreading diameter of the composite droplet on an ideal smooth surface over time; correcting for these parameters using the surface influence coefficient of the micropillar array; and obtaining the spreading radius and maximum spreading diameter of the composite droplet after impacting the micropillar surface over time as outputs; setting a set of operating conditions; and evaluating and determining the self-cleaning performance of each condition based on the spreading model. This method can be used to pre-evaluate the self-cleaning effect of a certain micropillar surface design, reducing the experimental verification workload of candidate solutions and thus shortening the R&D cycle.