摘要 :
One of the outcomes of the 1st AIAA Geometry and Mesh Generation Workshop was a recognition of the need for an improved and more widespread understanding of the principles underpinning contemporary geometric modelling techniques t...
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One of the outcomes of the 1st AIAA Geometry and Mesh Generation Workshop was a recognition of the need for an improved and more widespread understanding of the principles underpinning contemporary geometric modelling techniques throughout the computational simulation stakeholder community. To this end, a Geometry Modelling Working Group (GMWG) has been formed under the auspices of the AIAA Meshing, Visualization and Computational Environments Technical Committee. This paper is a preliminary output from GMWG and will form an initial basis for its work in preparing an AIAA Guide on the subject. In seeking to facilitate understanding and promote improved communication, the fundamental building blocks associated with computational geometry modelling are introduced. The ways in which their manner of application can be influenced by the intended use are then outlined and problems that are commonly encountered in contemporary computational simulation contexts are reviewed. A suite of recommended practices and accompanying challenges to be addressed in the future is also presented. To complement the main text and to provide an initial foundation for what is hoped will become a handy point of reference for all aerospace engineers engaged in computational simulation, an appendix is included. This provides a brief comparative overview of five distinctly different approaches that are currently taken towards representing geometry in computational simulations.
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摘要 :
One of the outcomes of the 1st AIAA Geometry and Mesh Generation Workshop was a recognition of the need for an improved and more widespread understanding of the principles underpinning contemporary geometric modelling techniques t...
展开
One of the outcomes of the 1st AIAA Geometry and Mesh Generation Workshop was a recognition of the need for an improved and more widespread understanding of the principles underpinning contemporary geometric modelling techniques throughout the computational simulation stakeholder community. To this end, a Geometry Modelling Working Group (GMWG) has been formed under the auspices of the AIAA Meshing, Visualization and Computational Environments Technical Committee. This paper is a preliminary output from GMWG and will form an initial basis for its work in preparing an AIAA Guide on the subject. In seeking to facilitate understanding and promote improved communication, the fundamental building blocks associated with computational geometry modelling are introduced. The ways in which their manner of application can be influenced by the intended use are then outlined and problems that are commonly encountered in contemporary computational simulation contexts are reviewed. A suite of recommended practices and accompanying challenges to be addressed in the future is also presented. To complement the main text and to provide an initial foundation for what is hoped will become a handy point of reference for all aerospace engineers engaged in computational simulation, an appendix is included. This provides a brief comparative overview of five distinctly different approaches that are currently taken towards representing geometry in computational simulations.
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The traditional aircraft design process is typically split into three stages: conceptual, preliminary, and detailed design. This three-stage process usually proceeds sequentially from stage to stage, and major design decisions are...
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The traditional aircraft design process is typically split into three stages: conceptual, preliminary, and detailed design. This three-stage process usually proceeds sequentially from stage to stage, and major design decisions are frozen between stage transitions. But this model does not capture the more complex reality, where design decisions are subject to frequent iteration at all stages of the process. This paper presents the Engineering Sketch Pad (ESP) Phasing capability that captures this more complex design workflow by decomposing the process into atomic portions called Phases. Each phase is intended to branch from any completed phase and answer a specific design question, allowing the designer to make design decisions non-sequentially. The use of this Phasing capability is demonstrated with the sizing of an aircraft wing while simultaneously optimizing an airfoil with increasing aerodynamic and geometric model fidelity. A number of cases are presented, beginning with a low fidelity aerodynamic model and a NACA 24XX airfoil geometry, and culminating in a Kulfan CST4 representation of geometry with MSES to perform the airfoil analysis.
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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 pro...
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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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摘要 :
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 pro...
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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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We implement a finite-element method for the linearized incompressible potential and full potential flow equations on boundary conforming unstructured grids. The intended purpose for the full potential solver is as a component in ...
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We implement a finite-element method for the linearized incompressible potential and full potential flow equations on boundary conforming unstructured grids. The intended purpose for the full potential solver is as a component in a medium fidelity multi-disciplinary analysis and design tool kit. The finite-element method is a continuous Galerkin formulation following established procedures for wake boundary conditions and Kutta condition. For the full potential equation, a two-field formulation is used where density serves as a second primary variable. This allows for a shock capturing scheme based on density upwinding without extending the stencil of the finite element discretization. Procedures are described for configuration, wake and farfield surface tessellation, as well as volume tessellation. The effect of different grid generation procedures and packages on the accuracy of the resulting solution is demonstrated. Finally, to aid design and optimization, the implementation of discrete adjoint based output functional sensitivities with respect to solution parameters is demonstrated.
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摘要 :
We implement a finite-element method for the linearized incompressible potential and full potential flow equations on boundary conforming unstructured grids. The intended purpose for the full potential solver is as a component in ...
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We implement a finite-element method for the linearized incompressible potential and full potential flow equations on boundary conforming unstructured grids. The intended purpose for the full potential solver is as a component in a medium fidelity multi-disciplinary analysis and design tool kit. The finite-element method is a continuous Galerkin formulation following established procedures for wake boundary conditions and Kutta condition. For the full potential equation, a two-field formulation is used where density serves as a second primary variable. This allows for a shock capturing scheme based on density upwinding without extending the stencil of the finite element discretization. Procedures are described for configuration, wake and farfield surface tessellation, as well as volume tessellation. The effect of different grid generation procedures and packages on the accuracy of the resulting solution is demonstrated. Finally, to aid design and optimization, the implementation of discrete adjoint based output functional sensitivities with respect to solution parameters is demonstrated.
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Multi-disciplinary analysis and optimization (MDAO) has been a long-standing goal in the aerospace community. In order to employ MDAO effectively, one needs to be able to compute the sensitivity of the objective function with resp...
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Multi-disciplinary analysis and optimization (MDAO) has been a long-standing goal in the aerospace community. In order to employ MDAO effectively, one needs to be able to compute the sensitivity of the objective function with respect to the driving parameters in a robust and efficient manner. Over the past decade there have been considerable efforts towards the generation of "adjoint" versions of flow solvers in order to help in this process. Unfortunately, the corresponding efforts have not been expended in the geometry and grid generation processes, especially when the geometries are generated parametrically with a modern computer-aided design (CAD) or CAD-like system. Contained herein is a pair of complementary techniques for computing configuration sensitivities directly on parametric, CAD-based geometries. One technique computes the configuration sensitivity analytically by differentiating the geometry-generating process; the other employs a new finite-difference technique that overcomes the difficulties previously encountered. Modifications to the Engineering Sketch Pad (ESP) (which is built on top of OpenCSM, EGADS, and OpenCASCADE) are described. Then the use of these configuration sensitivities in the computation of the sensitivity of grid-points is discussed. The results of these new techniques are shown on several configurations.
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摘要 :
Multi-disciplinary analysis and optimization (MDAO) has been a long-standing goal in the aerospace community. In order to employ MDAO effectively, one needs to be able to compute the sensitivity of the objective function with resp...
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Multi-disciplinary analysis and optimization (MDAO) has been a long-standing goal in the aerospace community. In order to employ MDAO effectively, one needs to be able to compute the sensitivity of the objective function with respect to the driving parameters in a robust and efficient manner. Over the past decade there have been considerable efforts towards the generation of "adjoint" versions of flow solvers in order to help in this process. Unfortunately, the corresponding efforts have not been expended in the geometry and grid generation processes, especially when the geometries are generated parametrically with a modern computer-aided design (CAD) or CAD-like system. Contained herein is a pair of complementary techniques for computing configuration sensitivities directly on parametric, CAD-based geometries. One technique computes the configuration sensitivity analytically by differentiating the geometry-generating process; the other employs a new finite-difference technique that overcomes the difficulties previously encountered. Modifications to the Engineering Sketch Pad (ESP) (which is built on top of OpenCSM, EGADS, and OpenCASCADE) are described. Then the use of these configuration sensitivities in the computation of the sensitivity of grid-points is discussed. The results of these new techniques are shown on several configurations.
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A scheme is presented for the automatic generation of structured overset surface meshes on geometries that are defined by Boundary Representation (BRep) solids. The resulting surface mesh system consists of two types of meshes: fa...
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A scheme is presented for the automatic generation of structured overset surface meshes on geometries that are defined by Boundary Representation (BRep) solids. The resulting surface mesh system consists of two types of meshes: face meshes and edge meshes. Face meshes are tessellations of the untrimmed BRep faces in parameter space. Point distribution on these meshes is governed by constraints on turning angle, cell size, chordal deviation, grid spacing stretching ratio, and grid clustering at sharp edges. Edge meshes are grown from the BRep edges via a marching scheme or transfinite-interpolation. For the marching scheme, an algebraic or hyperbolic method is automatically selected with a locally variable marching distance. Special treatment is needed around thin face regions such as those found in finite thickness trailing edges of airplane components. Grid points on the face and edge meshes that are outside the valid regions of the geometry are excluded from the computational domain and are marked by an integer iblank array. Excessive face mesh points in the overlap regions with the edge meshes are automatically removed. Surface domain connectivity is performed on the combined face and edge mesh system resulting in hole cuts and fringe point donor connectivity information. Test cases from a variety of fixed and rotary wing aeronautical vehicles are presented.
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