Research Papers

Current Issues and Trends in Meshing and Geometric Processing for Computational Engineering Analyses

[+] Author and Article Information
Kenji Shimada

Department of Mechanical Engineering,  Carnegie Mellon University, Pittsburgh, PA

J. Comput. Inf. Sci. Eng 11(2), 021008 (Jun 22, 2011) (13 pages) doi:10.1115/1.3593414 History: Received November 25, 2010; Accepted April 03, 2011; Published June 22, 2011; Online June 22, 2011

This paper presents the current issues and trends in meshing and geometric processing, core tasks in the preparation stage of computational engineering analyses. In product development, computational simulation of product functionality and manufacturing process have contributed significantly toward improving the quality of a product, shortening the time-to-market and reducing the cost of the product and manufacturing process. The computational simulation can predict various physical behaviors of a target object or system, including its structural, thermal, fluid, dynamic, and electro-magnetic behaviors. In industry, the computer-aided engineering (CAE) software packages have been the driving force behind the ever-increasing usage of computational engineering analyses. While these tools have been improved continuously since their inception in the early 1960s, the demand for more complex computational simulation has grown significantly in recent years, creating some major shortfalls in the capability of current CAE tools. This paper first discusses the current trends of computational engineering analyses and then focuses on two areas of such shortfalls: meshing and geometric processing, critical tasks required in the preparation stage of engineering analyses that use common numerical methods such as the finite element method and the boundary element method.

Copyright © 2011 by American Society of Mechanical Engineers
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Figure 1

In the concurrent engineering process, design, manufacturing and analysis are integrated in a more iterative fashion

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Figure 2

Two examples of computational engineering analysis problems that require a combination of multiple types of physical behaviors. The first example, shown in (a) and (b), combines three: fluid, thermal and structural. The second example, shown in (c) and (d), combines two: fluid and structural.

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Figure 3

The size of FEM models has increased dramatically in the past two decades. Such larger-size models with increased nonlinearity and multiphysics make the modern computational engineering analysis challenging.

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Figure 12

One approach to extracting the midsurface of a thin-walled solid is to first generate a one-layer tetrahedral mesh and then split it in the wall-thickness direction

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Figure 13

Two examples of constraint edge detection for high-quality mesh generation

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Figure 14

The method takes as input CT images and the 3D shape template of a healthy abdominal aorta, and then deforms the template until the cross-sectional shapes of the deformed template matches the input images [37]

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Figure 15

The maximum stress location and magnitude change significantly depending on how the wall thickness distribution, which is too small to measure from images, is defined [(38),39]

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Figure 4

Mesh size, anisotropy, and directionality can be specified by a 2 × 2 tensor field for a 2D meshing problem and a 3 × 3 tensor field for a 3D meshing problem [17]

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Figure 5

Properly controlled anisotropy can yield superior solution convergence and accuracy [22]

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Figure 6

Computational resources are best used when an appropriate anisotropy is specified in mesh generation [23]

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Figure 7

The solution convergence of total dissipated plastic energy with respect to the total number of elements [24]

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Figure 8

Mesh directionality control is critical in car crash simulation. The desired directionality accommodates both the surface curvature direction and the domain boundary directions represented by a 2 × 2 tensor field [17]

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Figure 9

Based on the desired mesh directionality given as a 3 × 3 tensor field, a hex-dominant mesh with controlled directionality is generated automatically [19]

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Figure 10

An example of anisotropic adaptive meshing for supersonic flow simulation [27]

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Figure 11

An example of adaptive mesh generation applied to a highly nonlinear, large deformation FEM simulation. The simulation works best with a skewed, graded, anisotropic mesh [28].




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