Surrogate Model Parameter Selection for Physical System Simulation
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Solution Overview
Problem
Testing for failure modes of complex physical systems is often destructive, expensive, and time-consuming due to the need for exhaustive testing across numerous variables.
Innovation Solution
A method and system for physical system simulation that involves generating initial simulation data using a reduced number of parameter sets, developing surrogate models to identify significant input parameters, and then generating additional simulation data focused on these parameters to create more reliable and efficient simulation models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If exhaustive testing across numerous variables is performed to analyze failure modes, then measurement precision and reliability are improved, but loss of time and productivity deteriorate
Solution Approach 1:
The patent creates virtual copies of the physical system through simulation models. These digital twins replicate the behavior and failure modes of the actual system, allowing exhaustive testing to be performed on the virtual model rather than the physical system. This copying approach enables comprehensive variable analysis without the time and resource costs of physical testing.
Solution Approach 2:
The patent performs preliminary simulation tests to identify critical failure modes and influential variables before conducting physical testing. By预先 (in advance) determining which parameters have the most significant impact on system failure, the methodology focuses subsequent physical testing on these critical factors, reducing the overall number of tests needed while maintaining measurement precision.
2Reliability
If exhaustive testing across numerous variables is performed to analyze failure modes, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The patent segments the testing process into two distinct phases: a virtual simulation phase for comprehensive variable analysis, and a physical testing phase for validation. This segmentation allows the computationally intensive exhaustive testing to occur in the virtual environment, while physical testing is limited to verifying critical failure modes, thereby maintaining reliability while significantly improving overall testing productivity.
Solution Approach 2:
By creating virtual replicas of the physical system, the patent enables comprehensive reliability analysis to be performed on the digital model. This copying allows all possible variable combinations to be tested virtually, ensuring thorough failure mode analysis without compromising physical system productivity, since virtual tests can be run in parallel and do not consume physical resources.
3Measurement precision
If multiple tests are performed to analyze the effect of variable changes on system components, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex physical testing apparatus with virtual simulation models. The digital twin of the system can systematically vary parameters and measure component effects without requiring complex physical test setups. This virtual copying maintains measurement precision while eliminating the need for increasingly complex physical testing equipment as the number of variables increases.
4Reliability
If exhaustive testing is performed across numerous variables, then reliability is improved, but loss of substance and cost increase
Solution Approach 1:
The patent performs exhaustive testing on virtual copies rather than physical systems. This allows comprehensive reliability analysis including extreme and failure conditions that would be resource-intensive or impossible to test physically. The virtual simulation consumes computational resources instead of physical materials, eliminating wear and tear on test equipment and samples while maintaining or improving reliability assessment.
Data Source
AI summary
A device includes one or more processors configured to generate simulation data by providing first sets of first values of first input parameters to a simulation of a physical system. The processors are configured to develop at least one surrogate model based on the simulation data, and to select second input parameters from the first input parameters based on the at least one surrogate model. A count of the second input parameters is less than a count of the first input parameters. The processors are configured to generate second simulation data by providing second sets of second values of the second input parameters to the simulation. The processors are configured to develop at least a second surrogate model based on the second simulation data.