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Efficient Parallel Optimization for Approximating CAD Curves Featuring Super-convergence
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DOI：10.1016/j.cad.2023.103513.png)
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We present an efficient, parallel, constrained optimization technique for approximating CAD curves with super-convergent rates. The optimization function is a disparity measure in terms of a piece -wise polynomial approximation and a curve re-parametrization. The constrained problem solves the disparity functional fixing the mesh element interfaces. We have numerical evidence that when the solver attains a minimum, the constrained disparity preserves the original super-convergence: 2p order for planar curves and L32(p - 1) + 2 for 3D curves, p being the mesh polynomial degree. Our optimization scheme consists of a globalized Newton method with a nonmonotone line search, and a log barrier function preventing element inversion in the curve re-parametrization. Moreover, we introduce a Julia interface to the EGADS geometry kernel and a parallel optimization algorithm. We test the potential of our curve mesh generation tool on a computer cluster using several aircraft CAD models. We conclude that the solver is well-suited for parallel computing, producing super-convergent approximations to CAD curves.(c) 2023 Published by Elsevier Ltd.
Keyword:
Curve approximation
High-order meshes
Super-convergence
Distance optimization
Parallel computing
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C
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3.1
论文数:
3.2K
被引数:
6.4K
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