Hexahedral Mesh Feature Recovery And Adaptive Optimization Method

In recent years,many scholars have carried out structural simplification work on hexahedral meshes to improve the accuracy and efficiency of the finite element analysis process.Such work improves the mesh quality by reducing the redundant singular structures in the mesh.However,the simplification work at this stage can’t guarantee good geometric features and high simplification rate at the same time.To address this problem,we can prioritize maintaining a high simplification rate,and then perform feature recovery later.This approach improves the mesh quality and ensures the validity of the geometry at the same time.To this end,this thesis proposes a hexahedral mesh feature recovery method based on a weighted soft-assignment strategy.This method treats the feature recovery problem as a point set alignment problem,effectively matching the feature points of the mesh with lost features to the the original mesh’s features.Moreover,this thesis proposes a hexahedral mesh optimization method with adaptive complex sheet inflation.The works in this thesis is summarized as follows:1.This thesis propose a hexahedral mesh feature recovery method based on a weighted soft-assignment strategy to recover the the mesh’s features quickly.This method designs a feature classification and cleaning method to extract highquality features,reduce the noise of the point set,and provide guidance for the weighted soft-assignment strategy with the class of features.The weighted softassignment strategy refers to using the feature description operator for point set coarse matching,using the results and feature types,assign different weighting coefficients to the constructed weighting terms to initialize the mixing coefficients of the Gaussian mixture model reasonably.This strategy can effectively improve the accuracy of the soft-assignment calculation,better maintain the local structural features,realize the effective fitting of the model,and improve the convergence speed of the point set alignment.In the solution stage,this method fully exploits the topological information of the hexahedral mesh.It uses the base-complex structure of the hexahedral mesh to compress the features,further improves the convergence speed.2.This thesis propose a hexahedral mesh optimization method with adaptive complex sheet inflation.This method adaptively uses a quadrilateral expansion algorithm based on concave-convex constraint or maximum-flow minimax-cut algorithm to expand the quadrilateral set from feature lines.Then,the quadrilateral set is optimized by chord-based optimization to obtain a highquality inflatable quadrilateral set for sheet inflation,it can expand the optimization space of the mesh.This method can also optimize other types of hexahedral meshes.