Parametric Mesh Generation for Optimization

2019 ◽  
pp. 219-249
Author(s):  
S. Ratnajeevan H. Hoole ◽  
Yovahn Yesuraiyan R. Hoole
Keyword(s):  
1986 ◽  
Vol 3 (5) ◽  
pp. 190 ◽  
Author(s):  
T.I. Boubez ◽  
W.R.J. Funnell ◽  
D.A. Lowther ◽  
A.R. Pinchuk ◽  
P.P. Silvester

2008 ◽  
Vol 58 (5-6) ◽  
pp. 461-473 ◽  
Author(s):  
Jonathan Lambrechts ◽  
Richard Comblen ◽  
Vincent Legat ◽  
Christophe Geuzaine ◽  
Jean-François Remacle
Keyword(s):  

2021 ◽  
Vol 9 (6) ◽  
pp. 572
Author(s):  
Luca Di Di Angelo ◽  
Francesco Duronio ◽  
Angelo De De Vita ◽  
Andrea Di Di Mascio

In this paper, an efficient and robust Cartesian Mesh Generation with Local Refinement for an Immersed Boundary Approach is proposed, whose key feature is the capability of high Reynolds number simulations by the use of wall function models, bypassing the need for accurate boundary layer discretization. Starting from the discrete manifold model of the object to be analyzed, the proposed model generates Cartesian adaptive grids for a CFD simulation, with minimal user interactions; the most innovative aspect of this approach is that the automatic generation is based on the segmentation of the surfaces enveloping the object to be analyzed. The aim of this paper is to show that this automatic workflow is robust and enables to get quantitative results on geometrically complex configurations such as marine vehicles. To this purpose, the proposed methodology has been applied to the simulation of the flow past a BB2 submarine, discretized by non-uniform grid density. The obtained results are comparable with those obtained by classical body-fitted approaches but with a significant reduction of the time required for the mesh generation.


Author(s):  
Jiing-Yih Lai ◽  
Jia-Wei Wu ◽  
Pei-Pu Song ◽  
Tzu-Yao Chou ◽  
Yao-Chen Tsai ◽  
...  

2021 ◽  
Vol 7 (3) ◽  
pp. 289-318
Author(s):  
Xiao-Ming Fu ◽  
Jian-Ping Su ◽  
Zheng-Yu Zhao ◽  
Qing Fang ◽  
Chunyang Ye ◽  
...  

AbstractA geometric mapping establishes a correspondence between two domains. Since no real object has zero or negative volume, such a mapping is required to be inversion-free. Computing inversion-free mappings is a fundamental task in numerous computer graphics and geometric processing applications, such as deformation, texture mapping, mesh generation, and others. This task is usually formulated as a non-convex, nonlinear, constrained optimization problem. Various methods have been developed to solve this optimization problem. As well as being inversion-free, different applications have various further requirements. We expand the discussion in two directions to (i) problems imposing specific constraints and (ii) combinatorial problems. This report provides a systematic overview of inversion-free mapping construction, a detailed discussion of the construction methods, including their strengths and weaknesses, and a description of open problems in this research field.


Author(s):  
Longmin Ran ◽  
Houman Borouchaki ◽  
Abdallah Benali ◽  
Chakib Bennis

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