Mesh generation and optimization of ship hull dirty geometry based on multi-constraint advancing front technique.
Saved in:
| Title: | Mesh generation and optimization of ship hull dirty geometry based on multi-constraint advancing front technique. |
|---|---|
| Authors: | Zhang, Peixin1 (AUTHOR), Tan, Junwei1 (AUTHOR), Ni, Xinyun2 (AUTHOR), Sun, Yuanqing3 (AUTHOR), Huang, Limin1,3 (AUTHOR) huanglimin@hrbeu.edu.cn |
| Source: | Engineering with Computers. Feb2026, Vol. 42 Issue 1, p1-22. 22p. |
| Abstract: | Computational Fluid Dynamics simulations of ship hydrodynamics are highly sensitive to the quality of the surface mesh, which is often compromised by dirty geometry—such as narrow gaps, surface intersections, and misalignments—introduced during multi-patch hull design. Conventional Advancing Front Technique mesh generators frequently fail under these conditions, producing defective initial meshes that require extensive manual repair. To overcome these limitations, this paper introduces a fully automated, feature-aware mesh generation and optimization framework founded on a novel Multi-Constraint Advancing Front Technique. The proposed methodology directly processes defective initial meshes without accessing the original Computer Aided Design parameters, integrating innovative algorithms for feature-conformal boundary extraction, spatial-hash-accelerated point cloud optimization, local density adaptation, and topology-aware normal consistency enforcement. Extensive validation on multiple hull models demonstrates that the method consistently produces watertight, high-quality surface meshes with an average Jacobian determinant exceeding 0.9. Remarkably, it completely eliminates normal vector errors and achieves a mesh transition ratio as low as 1.08, significantly outperforming conventional tools like Gmsh. This approach eliminates the need for manual intervention and provides a robust, efficient solution for high-fidelity mesh generation from complex, dirty ship hull geometries. [ABSTRACT FROM AUTHOR] |
| Copyright of Engineering with Computers is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
Be the first to leave a comment!
