Meshing is the process of converting a continuous geometric model, typically created in CAD, into a discrete representation made up of small elements such as triangles, quadrilaterals, tetrahedra, or hexahedra.
This discretized representation, called a mesh, is required for numerical simulation, analysis, and certain visualization workflows, as it enables physical phenomena to be computed using mathematical methods.
Meshing is a critical step in CAE workflows, forming the bridge between precise CAD geometry and simulation-ready models.
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In engineering analysis, physical problems such as stress, heat transfer, or fluid flow cannot be solved directly on exact CAD geometry.
Meshing converts this geometry into a finite set of elements that approximate the original shape, allowing numerical solvers to compute results at each element and node.
The structure, density, and quality of the mesh directly influence simulation accuracy, stability, and performance.
Several types of meshes are used depending on the simulation objective, the most common ones are:
Surface meshing
Uses 2D elements (triangles or quadrilaterals) to represent boundaries.
Commonly used for visualization, CFD boundary conditions, and lightweight analysis.
Volume meshing
Uses 3D elements (tetrahedra, hexahedra, prisms, pyramids, etc) to fill a solid volume.
Required for most FEA and CFD simulations.
Structured meshing
Follows a regular, grid-like topology.
Offers high numerical accuracy but is difficult to apply to complex geometries.
Unstructured meshing
Uses irregular element layouts that adapt to complex shapes.
Widely used in industrial CAE due to flexibility and automation.
In both Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA), the mesh defines how physical equations are solved.
A high-quality mesh:
Accurately captures geometric features and curvature
Provides sufficient resolution in critical regions
Enables stable and convergent numerical solutions
Poor mesh quality can lead to inaccurate results, solver instability, or excessive computation time.
As a result, meshing is often one of the most critical—and time-consuming—steps in simulation workflows.
Mesh quality is evaluated using several criteria, including:
Element size and distribution
Aspect ratio and skewness
Smooth transitions between fine and coarse regions
Boundary conformity to the original CAD geometry
Careful control of these parameters helps ensure reliable, repeatable and performant simulation results.
Mesh optimization focuses on achieving the best balance between accuracy and computational efficiency.
Rather than simply increasing mesh density everywhere, optimization strategies aim to place elements where they matter most.
Common mesh optimization techniques include:
Local refinement in regions of high stress, curvature, or flow gradients
Adaptive meshing, where the mesh evolves based on simulation results
Element quality improvement, such as smoothing or re-shaping poorly formed elements
Geometry simplification (defeaturing) to remove small details that do not affect simulation outcomes
Optimized meshes reduce solver time and memory usage while maintaining accurate results.
Generating accurate yet lightweight meshes is a key objective in modern CAE workflows.
This typically involves:
Preparing CAD geometry
Simplifying or defeaturing geometry to remove unnecessary details while preserving functional features.
Controlling mesh density
Applying fine meshes only where required and coarser meshes elsewhere.
Preserving geometric fidelity
Reducing mesh size or using high order elements to ensure the mesh accurately follows CAD boundaries, curvature, and topology.
Balancing accuracy and performance
Reducing element count without compromising simulation reliability.
Accurate, lightweight meshes enable faster simulations, lower computational costs, and more efficient design iterations
Meshing is used across a wide range of engineering and industrial applications:
FEA – structural, thermal, vibration, and fatigue analysis
CFD – airflow, fluid dynamics, and heat transfer simulation
EDA – electromagnetic field, wave propagation, and electrostatic analysis
Manufacturing simulation – forming, molding, and machining processes
Digital mockups and visualization – lightweight representations of complex models
Industries such as aerospace, automotive, energy, and medical devices rely heavily on accurate meshing to validate designs before production.
Despite its importance, meshing presents several challenges:
Highly detailed CAD models may require extensive simplification
Poor element quality can compromise solver accuracy
Excessive mesh density increases computation time and memory usage
Inconsistent or corrupted CAD data can cause meshing failures
Achieving the right balance between robustness, accuracy, and performance is a core challenge in meshing.
Spatial provides technologies that support robust meshing directly from CAD geometry.
By combining accurate CAD interoperability, geometry preparation, and controlled mesh generation based on solver requirements, Spatial enables developers to build reliable CAD-to-CAE pipelines.
Spatial’s approach supports:
Direct meshing from exact B-Rep geometry
Generation of high-quality, simulation-ready meshes
Consistent results across complex and multi-CAD datasets
This ensures meshing remains a dependable foundation for simulation and engineering analysis.
We’re proud to have pioneered the development of geometry kernels with ACIS. Today, we offer solutions to support your 3D geometry representation needs. Whether you require a continuous or discrete B-Rep modeler, we have a solution for your application’s specific needs.
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