Topology And Shape Optimization With Adaptive Mesh Solving
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Solution Overview
Problem
Existing computer design systems struggle to operate effectively in complex environments, lacking advanced capabilities for topology and shape optimization of physical objects.
Innovation Solution
A computer-implemented simulation method that performs topology and shape optimization of physical objects using graphical user interfaces, allowing for iterative operations to achieve optimized solutions based on user inputs and material properties, with features like mesh generation and solver settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If topology optimization is performed on complex models, then optimization accuracy is improved, but memory requirements and computational workload increase
Solution Approach 1:
The patent applies segmentation by dividing the complex model into multiple mesh blocks or domains that can be processed independently. This allows the optimization problem to be broken down into smaller sub-problems, reducing the memory footprint while maintaining overall optimization accuracy through coordinated solving of the segmented domains.
Solution Approach 2:
The patent introduces a hierarchical dimension to the optimization process, implementing multi-scale optimization where global optimization operates at one level and local optimization operates at another level. This dimensional approach allows complex models to be optimized without requiring all computational resources to be allocated simultaneously, thus reducing peak memory requirements.
2Manufacturing precision
If iterative optimization operations are performed, then optimization quality is improved, but computation time increases
Solution Approach 1:
The patent applies preliminary action by performing preprocessing operations such as mesh generation, material property assignment, and boundary condition definition before the iterative optimization begins. This preparation work is done once upfront, allowing the iterative optimization loop to focus only on the essential computational tasks, thereby reducing the time penalty of multiple iterations.
Solution Approach 2:
The patent implements feedback mechanisms where optimization results from previous iterations are used to inform and adjust subsequent iteration parameters. This feedback loop enables the system to converge faster by learning from previous computational results, reducing the total number of iterations needed to achieve high optimization quality.
3Measurement precision
If detailed mesh generation is performed, then model accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent applies local quality by implementing non-uniform mesh generation where fine mesh resolution is applied only in critical regions requiring high accuracy, while coarser mesh is used in less critical areas. This approach maintains model accuracy where needed while significantly reducing the overall number of elements and associated processing complexity.
Solution Approach 2:
The patent introduces dynamic mesh adaptation where the mesh structure can be refined or coarsened during the optimization process based on local stress concentrations or optimization needs. This dynamic approach allows the model to maintain high accuracy in critical regions while keeping processing complexity manageable through adaptive resolution.
Data Source
AI summary
Simulation methods and systems are described for topology and shape optimization of a geometrical representation of a physical object being modeled. An initial geometry of the physical object is represented. Equation data representing physical properties for portions of an initial geometry are defined. User inputs are received through a GUI for optimization settings and user selections of portions of the geometry being optimized. For topology optimization, various material properties and solver settings may be defined. For shape optimization, various solver settings and optimization objective may be defined. A discretized model is generated of the physical object. Solutions of the topology or shape optimization are generated and updated values are determined. Iterative operations are then performed for solving the topology or shape optimization objective expressions. The solutions are stored and graphical representations of the solutions may be generated.