Geometrically Nonlinear Design Optimization with Artificial Forces
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Solution Overview
Problem
Existing optimization methods for designing real-world objects struggle with post-buckling behavior, particularly in large displacement and geometrical non-linear modeling, leading to challenges in predicting the global buckling point and increasing computational costs.
Innovation Solution
The method involves adding artificial forces to a computer-based model representing a real-world object to suppress post-buckling behavior, allowing for iterative optimization that converges to an optimized design by counteracting deformations and capturing the pre-buckling and global buckling points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing optimization methods are used for large displacement and geometrical non-linear modeling, then the global buckling point can be predicted, but post-buckling behavior causes computational costs to increase and convergence difficulties arise
Solution Approach 1:
The patent applies preliminary anti-action by introducing artificial forces that counteract post-buckling deformations before they can cause convergence issues. These artificial forces act in opposition to the buckling mode shapes, preventing the simulation from entering the problematic post-buckling regime where computational costs increase and convergence becomes difficult.
Solution Approach 2:
The patent uses artificial forces as an intermediary mechanism to bridge the gap between pre-buckling and post-buckling analysis. These forces serve as a mediator that allows the optimization to proceed through the buckling point without being destabilized by post-buckling behavior, enabling continuous optimization iterations.
2Measurement precision
If existing optimization methods are used for large displacement and geometrical non-linear modeling, then the global buckling point can be predicted, but optimization convergence becomes difficult
Solution Approach 1:
The artificial forces are applied in advance to counteract the destabilizing post-buckling effects before they can disrupt optimization convergence. By acting against the buckling mode shapes, these forces maintain numerical stability throughout the optimization process, ensuring reliable convergence even in the presence of large displacements and geometrical non-linearities.
Solution Approach 2:
The patent implements feedback by using the buckling mode shapes to determine the direction and magnitude of artificial forces. The optimization algorithm continuously monitors the structural response and adjusts the artificial forces based on the current deformation state, creating a feedback loop that maintains convergence stability throughout the optimization iterations.
3Loss of information
If post-buckling behavior is fully simulated, then complete structural response is captured, but computational efficiency decreases
Solution Approach 1:
The patent extracts and isolates the problematic post-buckling behavior by introducing artificial forces that specifically target and counteract post-buckling deformations. This allows the simulation to capture the essential pre-buckling and buckling response while excluding the computationally expensive post-buckling regime, achieving a balance between accuracy and efficiency.
Solution Approach 2:
The patent applies partial action by using artificial forces only where and when needed—to counteract post-buckling deformations in specific mode shapes. Rather than modifying the entire simulation, the artificial forces are applied selectively to the buckling modes, achieving computational efficiency while maintaining accuracy in the critical pre-buckling and buckling regions.
Data Source
Figure 1A~1D
Figure 2
Figure 3A~3C
AI summary
Embodiments determine optimized designs of real-world objects. A computer-based model representing a real-world object is defined and the computer-based model is modified to include at least one artificial force. The at least one artificial force is defined as a function of physics-based behavior. The real-world object is iteratively optimizing with respect to load using the computer-based model modified. A result of the iterative optimization is an optimized design of the real-world object.