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Fast NURBS Skinning Algorithm and Ship Hull Section Refinement Model

Published:04 June 2023Publication History

ABSTRACT

In the problem of calculating hull elements using the table of offsets, the sparsity between hull slices will bring uncertainty and error to the calculation. Therefore, this paper proposes a refinement algorithm of the ship hull based on the table of offsets: Firstly, the NURBS curve for the hull is constructed based on the table of offsets, and the hull's NURBS surface is obtained through the skinning algorithm. Secondly, the IR-BFS algorithm is used to inverse the knot parameters of the stations of the target station in the hull's NURBS surface. Thirdly, based on the knot parameters and the hull NURBS surface expression, the hull section, after refinement of the target station, is obtained. In constructing the hull's NURBS surface, the hull section is first expressed using the NURBS interpolation algorithm and the flattening algorithm of the NURBS based on the IR-BFS algorithm. Then the skinning algorithm is improved by fixing the -direction knot parameters to express the expressed hull NURBS cross-section as a hull's NURBS surface, which improves the computational efficiency. The effectiveness of the improved skinning algorithm is judged by comparing the increase in the number of control points and the computational time consumption in the expression of the hull NURBS surface before and after the improved skinning algorithm. The usability of the refinement algorithm of the hull section is verified by comparing the hull section based on the table of offsets with the refined hull section. The experimental results show that the improved skinning algorithm can effectively improve the speed of NURBS surface generation; The proposed refinement algorithm of the hull section can effectively generate refined sections through refinement intervals.

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      cover image ACM Other conferences
      ICMLSC '23: Proceedings of the 2023 7th International Conference on Machine Learning and Soft Computing
      January 2023
      219 pages
      ISBN:9781450398633
      DOI:10.1145/3583788

      Copyright © 2023 ACM

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      • Published: 4 June 2023

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