A high-fidelity finite element model automatic modeling method for blade disk structure

By using leaf disc cloud data and multi-feature clustering analysis algorithm, combined with mesh deformation algorithm, the high-fidelity finite element model is directly generated, which solves the problems of insufficient precision and cumbersome operation in the existing technology, and achieves efficient and accurate finite element modeling.

CN114970247BActive Publication Date: 2025-04-15CHONGQING UNIV
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Patent Information

Application Number
CN202210496292.7
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2022-05-09
Publication Date
2025-04-15
Estimated Expiration
2042-05-09

AI Technical Summary

Technical Problem

In the prior art, the process of generating a CAD model from machine scanning data and then dividing the grid cannot guarantee the accuracy of the grid model, and the operation is cumbersome and time-consuming, lacks robustness, and it is difficult to realize batch finite element modeling.

Method used

The high-fidelity finite element model is automatically generated through the point cloud data of the leaf disk. Using multi-feature clustering analysis algorithm and grid deformation algorithm, blade features are directly extracted from the point cloud data measured by the optical scanner, grid correction and deformation are performed, and geometric detuning finite element model matching the actual leaf disk is generated.

Benefits of technology

It realizes the rapid and accurate conversion of detuned blade scanning data into a high-fidelity finite element model, with high robustness and accuracy, and can realize batch conversion, greatly improving efficiency.

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Abstract

The present invention relates to the field of aeroengine blade disk modeling, and specifically discloses a high-fidelity finite element model automatic modeling method for blade disk structure, including obtaining the point cloud data of the whole blade disk measured by an optical scanner; extracting the point cloud data of the blade, and distinguishing the point cloud data set of each blade by using a blade recognition algorithm; step 3, using a multi-feature clustering analysis algorithm to perform feature classification on each blade point cloud data, and respectively obtain the subdivided data sets of the blade top, pressure surface, and suction surface; step 4, using a mesh deformation algorithm to move the corresponding nodes in the standard finite element model to fit the classified subdivided data set, and using a radial basis kernel function to maintain the unit shape and mapping, and obtain a geometric detuned finite element model matching the measured whole blade disk. The method can quickly convert the subdivided scanning data into a high-fidelity finite element model, while ensuring high robustness and accuracy.
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