Nonlinear Analysis Residual Threshold Prediction
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Solution Overview
Problem
In structural and fluid analyses, setting an optimal residual threshold for non-linear analysis is challenging, leading to increased calculation time due to the iterative nature of methods like Newton's method, where determining the right threshold beforehand is difficult.
Innovation Solution
Implementing an AI-driven approach that learns from residual transitions over time to dynamically adjust the residual threshold, using inter-process communication between simulation and machine learning programs to optimize convergence conditions, thereby reducing the number of iterations and calculation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an iterative method like Newton's method is used for non-linear analysis, then the analysis can be performed, but the calculation time increases due to the need to determine optimal residual threshold
Solution Approach 1:
The patent applies preliminary action by pre-calculating residual transition data through multiple linear analyses with different residual thresholds before the actual non-linear analysis. This pre-computed data is stored and used during the non-linear analysis to quickly determine optimal convergence thresholds without performing time-consuming iterative calculations in real-time, thus resolving the contradiction between reliable convergence determination and calculation time.
2Measurement precision
If the residual threshold is set too low to ensure accurate convergence determination, then the precision improves, but the number of iterations increases leading to longer processing time
Solution Approach 1:
The patent implements dynamics by making the residual threshold adaptive rather than fixed. The system dynamically adjusts the residual threshold during non-linear analysis based on pre-calculated residual transition data and the current analysis state. This allows the threshold to be high (for speed) when appropriate and low (for precision) when needed, resolving the contradiction between convergence precision and analysis speed.
Solution Approach 2:
The patent changes the parameter of residual threshold from a static value to a dynamically adjustable parameter based on pre-computed residual transition characteristics. By storing residual transition data for multiple threshold values and selecting the appropriate threshold based on current analysis conditions, the system achieves both high precision convergence determination and fast analysis speed.
3Measurement precision
If multiple experimental values are used to build a prediction model for residual threshold, then the accuracy of convergence determination improves, but the complexity of the system increases
Solution Approach 1:
The patent applies segmentation by dividing the complex task of optimal threshold selection into separate phases: (1) pre-computation phase where multiple linear analyses with different thresholds generate residual transition data, and (2) execution phase where the prediction model quickly selects thresholds based on pre-stored data. This segmentation reduces real-time computational complexity while maintaining high prediction accuracy through the use of multiple experimental values in the pre-computation phase.
Data Source
AI summary
An information processing method includes executing a first process of performing a non-linear analysis by iterating a linear analysis, and executing a second process of predicting a residual threshold to be used for determination of convergence of the linear analysis by a prediction model, based on a residual transition and calculation time for each iteration of the linear analysis obtained for each residual threshold using a plurality of experimental values by the first process. The information processing method further includes performing passage of data between the first process and the second process through an inter-process communication using a shared memory set in a memory.