TY - GEN
T1 - Geometry modeling for unstructured mesh adaptation
AU - Park, Michael A.
AU - Kleb, Bil
AU - Jones, William T.
AU - Krakos, Joshua A.
AU - Michal, Todd
AU - Loseille, Adrien
AU - Haimes, Robert
AU - Dannenhoffer, John F.
N1 - Publisher Copyright:
© 2019, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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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U2 - 10.2514/6.2019-2946
DO - 10.2514/6.2019-2946
M3 - Conference contribution
AN - SCOPUS:85092661667
SN - 9781624105890
T3 - AIAA Aviation 2019 Forum
BT - AIAA Aviation 2019 Forum
PB - American Institute of Aeronautics and Astronautics Inc, AIAA
T2 - AIAA Aviation 2019 Forum
Y2 - 17 June 2019 through 21 June 2019
ER -