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We propose a goal-oriented mesh optimization algorithm which ends in an optimized mesh with respect to a chosen quantity of interest.
A preprocessing step of mesh optimization can alleviate this problem.
The coarse-to-fine technique of CTFEA can advantageously solve large mesh optimization problems.
Tangling can occur, for example, during mesh optimization and mesh morphing.
This gives rise to a constrained optimization problem for mesh optimization, which is solved whenever the mesh quality deteriorates.
It consists of two procedures: skeleton based as-rigid-as-possible (ARAP) shape modeling and detail-preserving mesh optimization.
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On the contrary, a gridless algorithm can be used to simulate various physical systems without the need to perform grid-mesh optimization.
The modeling strategy provided includes meshing optimization, solver settings, comparison between different turbulence models and, mainly, the calibration of the local correlation parameters of the transition turbulence model by Menter, which was found to be the most accurate model for the simulation of transitional flows.
Motivated by lack of non-heuristic and mesh independent optimization algorithms to obtain the optimum distribution of short fibers through a design domain, Non-Uniform Rational B-spline (NURBS) basis functions have been implemented to define continuous and smooth mesh independent fiber distribution function as well as domain discretization.
Optimization of automatic remeshing is achieved through mesh pre-optimization based on the discretization error.
This paper deals with the concept of mesh pre-optimization.
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