Structure Analysis Device Algorithm Selection
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Solution Overview
Problem
In structure analysis using the finite element method, existing methods face challenges with long analysis times due to high computational complexity and memory requirements, particularly with the direct method requiring more memory and the iterative method struggling with convergence issues depending on the model size and boundary conditions.
Innovation Solution
A structure analysis device that evaluates model size and boundary condition strength to selectively choose between direct and iterative methods for solving simultaneous linear equations, optimizing memory usage and reducing analysis time by determining the appropriate method based on these factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the direct method is used to solve simultaneous linear equations, then solution accuracy is improved, but memory usage increases and analysis time increases
Solution Approach 1:
The patent implements dynamic selection between direct and iterative methods based on real-time evaluation of model characteristics (sparsity, condition number, model size). The system transitions from static method selection to dynamic adaptation, choosing the optimal algorithm for each specific problem instance to balance accuracy and memory efficiency.
Solution Approach 2:
The patent changes the parameter of algorithm selection criteria by introducing multiple evaluation dimensions (sparsity ratio, condition number, model size) rather than using a single fixed criterion. This allows the system to adapt parameter selection based on the specific characteristics of each structural analysis model.
2Measurement precision
If the direct method is used to solve simultaneous linear equations, then solution accuracy is improved, but analysis time increases
Solution Approach 1:
The system dynamically selects between direct and iterative methods based on model characteristics, enabling adaptive time-accuracy optimization. For models where the direct method would be excessively time-consuming, the system switches to iterative methods that provide sufficient accuracy with reduced computation time.
Solution Approach 2:
The patent introduces parameter-based selection criteria (sparsity ratio thresholds, condition number ranges, model size limits) that automatically determine whether the direct or iterative method is more time-efficient for a given problem, eliminating the need for manual time-accuracy trade-off decisions.
3Quantity of substance
If the iterative method is used to solve simultaneous linear equations, then memory usage is reduced, but convergence issues occur depending on model size and boundary conditions
Solution Approach 1:
The patent performs preliminary evaluation of model characteristics (sparsity, condition number, size) before selecting the solution method. This advance assessment prevents convergence issues by identifying models unsuitable for iterative methods and directing them to the direct method, ensuring reliability before computation begins.
Solution Approach 2:
The system incorporates feedback mechanisms where the evaluated model characteristics directly influence algorithm selection. The sparsity ratio, condition number, and model size serve as feedback parameters that determine whether iterative or direct methods will achieve reliable convergence for the specific problem at hand.
4Quantity of substance
If model size increases, then analysis completeness is improved, but computational complexity increases and analysis time increases
Solution Approach 1:
The patent implements dynamic algorithm selection that adapts to model size. For larger models where computational complexity would be prohibitive with the direct method, the system switches to iterative methods that scale more efficiently, maintaining analysis completeness while managing computational complexity.
Solution Approach 2:
The patent uses model size as a key parameter in the selection criteria, establishing size thresholds that trigger method switching. This parameter-based approach automatically adjusts computational strategy based on the scale of the analysis model, optimizing the balance between completeness and complexity.
5Loss of time
If computational complexity is reduced, then analysis time is reduced, but solution accuracy may deteriorate
Solution Approach 1:
The system dynamically matches computational complexity with required accuracy by selecting algorithms based on model characteristics. The evaluation criteria ensure that when simpler iterative methods are chosen, the model properties are suitable for maintaining accuracy, thus reducing time without sacrificing precision.
Solution Approach 2:
The patent changes the selection parameters to include multiple dimensions (sparsity, condition number, model size) that collectively determine both time efficiency and accuracy preservation. This multi-parameter approach ensures that reduced computational complexity does not come at the cost of solution accuracy.
Data Source
AI summary
A structure analysis device includes a memory and a processor configured to obtain model information, evaluate a size of a model in accordance with the model information, select, in accordance with the evaluated size, either a direct method or an iterative method as a first algorithm of a simultaneous linear equation of a structure analysis solver that uses a finite element method, and execute structure analysis of the model by using the first algorithm.