Physics-Aware Meshing for CFD Grid Generation
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Solution Overview
Problem
Existing mesh generation techniques for Computational Fluid Dynamics (CFD) and Finite Element Methods (FEM) require significant human intervention, leading to increased computational time and potential errors, making it difficult for novice users to create high-quality meshes.
Innovation Solution
A physics-aware meshing system and method that automatically generates grids for objects by iteratively comparing original and meshed geometries, calculating surface sizes, layer parameters, and refinement zones, thereby reducing human intervention and improving mesh quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual mesh generation by subject matter experts is used, then mesh quality and accuracy are improved, but computational time and complexity increase
Solution Approach 1:
The system performs self-evaluation by automatically comparing the generated mesh geometry with the original input geometry using point cloud comparison and Hausdorff distance techniques. This self-service mechanism eliminates the need for manual expert review while maintaining mesh quality standards through automated convergence criteria and iterative refinement processes.
Solution Approach 2:
The system implements feedback loops where mesh generation results are continuously evaluated against quality metrics, and the process automatically iterates to improve mesh quality. The feedback mechanism uses automated comparison techniques and convergence criteria to guide successive refinements without requiring manual intervention at each step.
2Manufacturing precision
If manual mesh generation by subject matter experts is used, then mesh quality is improved, but device complexity and operation difficulty increase
Solution Approach 1:
The system autonomously performs mesh generation, evaluation, and refinement without requiring user expertise. It automatically compares generated meshes with original geometry, evaluates quality metrics, and iterates to produce high-quality results, making the process accessible to novice users while maintaining expert-level quality standards.
Solution Approach 2:
The complex mesh generation process is segmented into automated sub-tasks including geometry comparison, quality evaluation, and iterative refinement. Each segment is handled by specialized algorithms that work together to produce high-quality meshes, reducing the operational burden on users while maintaining precision.
3Measurement precision
If iterative mesh refinement is performed, then mesh independence and accuracy are improved, but computational time increases
Solution Approach 1:
The system performs preliminary automated evaluations using point cloud comparison and Hausdorff distance techniques to assess mesh quality early in the process. This preliminary action allows the system to identify areas requiring refinement and proceed efficiently, avoiding unnecessary iterative cycles while ensuring mesh independence is achieved.
Solution Approach 2:
The system applies refinement selectively to specific regions where quality metrics indicate improvement is needed, rather than uniformly refining the entire mesh. This partial action approach reduces computational overhead while maintaining mesh independence in critical areas through targeted iterative refinement.
Data Source
AI summary
Computational Fluid Dynamic, Finite Element Methods (FEM), and other engineering modelling and simulation requires meshing (numerical grid generation) of the geometry. The meshing is done by meshing software and the CFD expert need to provide meshing parameters. Existing approaches involve a lot of human intervention which leads to additional computational time and may be prone to errors. Present disclosure provides system and method that construct meshed geometry based a first mesh generated using meshing parameters wherein the meshed geometry is compared with original geometry of an object under consideration to estimate the surface size. Further, the first mesh is simulated in an iterative manner to obtain parameters such as domain size, refinement zone, and layer parameters of the object under consideration until these parameters reach an associated threshold and a mesh independent grid is obtained for the object based on the above-mentioned parameters.