Enabling virtual topology for high quality CFD surface meshing of complex CAD geometry

Gammon, Mark; Fellows, Chris D.; Whyman, Sam H.

doi:10.2514/6.2018-3721

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…The details of the ARAP can be found in [30]. Here, the implementation of ARAP in CADfix [31] [32] is used, which includes an untangle function to guarantee a bijective mapping. This function is called through the CADfix python API.…”

Section: B Parameterization Of a Superset Facementioning

confidence: 99%

Automated Mesh Deformation for Computer-Aided Design Models That Exhibit Boundary Topology Changes

et al. 2020

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This paper describes an automated approach to deform the analysis mesh applied to a CAD model in response to a change in the shape of the CAD model during the shape optimization process. It is an advance over existing techniques because it works even when the boundary topology of the CAD model changes during the shape change, which other approaches cannot generally accommodate. To achieve the deformation, an analysis topology representation is created for each model, such that there is a one-to-one mapping between the analysis topology representing the models before and after the shape change. The surface mesh is deformed using constrained Delaunay triangulation and it is shown how the domain mesh can be updated based on the surface mesh deformation calculated. Here the approach used is Linear Elasticity.

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Section: B Parameterization Of a Superset Facementioning

confidence: 99%

Automated Mesh Deformation for Computer-Aided Design Models That Exhibit Boundary Topology Changes

et al. 2020

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“…Virtual topology operators [15] or surface quilts and edge chains [16] collect or split [17] the low level topological faces and edges into virtual faces and edges. A quilt can have a global parameterization [18][19][20], but constructing a well-behaved (continuous and orthogonal) parameterization can be difficult. Quilts have the potential for preventing topology changes to virtual topology during model deformation [17,21], but only to a certain point.…”

Section: Introductionmentioning

confidence: 99%

Geometry Modeling for Unstructured Mesh Adaptation

Park

Kleb

Jones

et al. 2019

AIAA Aviation 2019 Forum

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The quantification and control of discretization error is critical to obtaining reliable simulation results. Adaptive mesh techniques have the potential to automate discretization error control, but have made limited impact on production analysis workflow. Recent progress has matured a number of independent implementations of flow solvers, error estimation methods, and anisotropic mesh adaptation mechanics. However, the poor integration of initial mesh generation and adaptive mesh mechanics to typical sources of geometry has hindered adoption of adaptive mesh techniques, where these geometries are often created in Mechanical Computer-Aided Design (MCAD) systems. The difficulty of this coupling is compounded by two factors: the inherent complexity of the model (e.g., large range of scales, bodies in proximity, details not required for analysis) and unintended geometry construction artifacts (e.g., translation, uneven parameterization, degeneracy, self-intersection, sliver faces, gaps, large tolerances between topological elements, local high curvature to enforce continuity). Manual preparation of geometry is commonly employed to enable fixed-grid and adaptive-grid workflows by reducing the severity and negative impacts of these construction artifacts, but manual process interaction inhibits workflow automation. Techniques to permit the use of complex geometry models and reduce the impact of geometry construction artifacts on unstructured grid workflows are presented. Two complex MCAD models from the AIAA Sonic Boom and High Lift Prediction Workshop are shown to demonstrate the utility of the current approach.

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