Carbon-fiber reinforced polymer material impeller is designed for the centrifugal pump to deliver corrosive, toxic, and abrasive media in the chemical and pharmaceutical industries. The pressure-velocity coupling fields in the pump are obtained from the CFD simulation. The stress distribution of the impeller couple caused by the flow water pressure and rotation centrifugal force of the blade is analyzed using one-way fluid-solid coupling method. Results show that the strength of the impeller can meet the requirement of the centrifugal pumps, and the largest stress occurred around the blades root on a pressure side of blade surface. Due to the existence of stress concentration at the blades root, the fatigue limit of the impeller would be reduced greatly. In the further structure optimal design, the blade root should be strengthened.
Fused deposition molding (FDM) is one of the most widely used three‐dimensional (3D) printing technologies. This paper explores the influence of the forming angle on the tensile properties of FDM specimens. Orthogonal layering details were studied through experiments, theory, and finite element simulations. The stiffness and strength of the specimens were analyzed using the classical laminated plate theory and the Tsai–Wu failure criterion. The experimental process was simulated using finite element simulations. Results show that it is feasible to predict the stiffness and strength of FDM specimens using classical laminated plate theory and the Tsai–Wu failure criterion. A molding angle of 45° leads to specimens with maximized tensile properties. Numerical simulations show that changing the molding angle changes the internal stress and deformation fields inside samples, leading to FDM samples with different mechanical properties due to the orthogonal layers at different molding angles.
The traditional matrix factorization model cannot effectively extract the features of users and items, but the feature information can be extracted well based on the deep learning model. At present, the mainstream recommendation algorithms based on deep learning only make recommendation prediction in the form of the product of neural network output or item features and user features, and cannot fully mine the relationship between users and items. Based on this, this paper proposes a recommendation algorithm based on the combination of text convolution neural network and singular value decomposition (Bias SVD) with biased terms. The text convolution neural network (Text CNN) is used to fully extract the feature information of users and items, and then the singular value decomposition method is used to make recommendations to deeply understand the document context information and further improve the accuracy of recommendation. The algorithm is widely evaluated and analyzed on two real data sets of MovieLens, and the accuracy of recommendation is obviously better than that of ConvMF algorithm and mainstream deep learning recommendation algorithm.
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