Digital Twin Simulation Using Representative Parameter Updates
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current digital twin simulation technologies require domain experts to re-implement simulations for new domains, struggle to reflect real model characteristics, and incur significant time and cost due to probabilistic models and high simulation engine execution times, limiting real-time analysis and scenario analysis.
Innovation Solution
A simulation device and method that identifies intrinsic parameters, analyzes correlations, selects representative parameters, updates them with real sensor data, and applies these updates to the simulation model, enabling efficient simulation and real-time predictions across various domains.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If iterative simulation is performed using probabilistic models, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing simulation results for various parameter combinations in a lookup table before actual use. When simulation is needed, the system quickly retrieves pre-computed results rather than performing iterative probabilistic simulations, thus maintaining measurement precision while dramatically reducing simulation time.
Solution Approach 2:
The patent uses partial action by selecting and storing only the most critical simulation scenarios and parameter combinations in the lookup table, rather than computing all possible iterations. This partial pre-computation provides sufficient accuracy for practical applications while avoiding the excessive time cost of complete iterative simulation.
2Manufacturing precision
If domain-specific simulators are developed directly by domain experts, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The patent implements universality by creating a domain-agnostic digital twin framework that can be applied across multiple domains (manufacturing, healthcare, finance, etc.). The system uses universal components including parameter identification modules, correlation analysis engines, and lookup table structures that work across different application areas, reducing the need for domain-specific custom development while maintaining precision through domain-adapted parameter selection.
Solution Approach 2:
The patent applies copying by using pre-defined simulation model templates and parameter structures that can be copied and adapted to different domains. Rather than building simulation models from scratch for each domain, the system copies proven model architectures and adapts them with domain-specific parameters, reducing complexity while preserving manufacturing precision.
3Productivity
If simulation engines are used to execute simulation models, then productivity is improved, but loss of time increases
Solution Approach 1:
The patent resolves this contradiction by pre-executing simulations for various parameter combinations and storing results in lookup tables. The simulation engine is used during the pre-computation phase to generate accurate results, but during actual operation, the system retrieves pre-computed results instantly, eliminating the need for repeated engine execution while maintaining productivity.
Solution Approach 2:
The patent extracts the time-consuming simulation engine execution from the operational workflow by separating model execution from parameter querying. The simulation engine runs once during setup to populate lookup tables, then the system extracts and uses only the results without re-invoking the engine, thus maintaining productivity while eliminating execution time delays.
Data Source
AI summary
A simulation device configured to perform a simulation based on a digital twin service includes a creation unit configured to create a simulation model based on model design data; an identification unit configured to identify at least one intrinsic parameter of the created simulation model and at least one input value related to the intrinsic parameter; an output unit configured to output an estimated output value which is produced from the simulation model depending on the input value based on the intrinsic parameter; a correlation analysis unit configured to analyze a correlation between the intrinsic parameter and the estimated output value; a selection unit configured to select at least one representative intrinsic parameter from among the at least one intrinsic parameter depending on the analyzed correlation; a collection unit configured to collect real sensor data related to the representative intrinsic parameter from a real model constructed based on the model design data; an update unit configured to update the at least one representative intrinsic parameter based on the collected real sensor data and the estimated output value; and a simulation unit configured to perform a simulation by applying the updated representative intrinsic parameter to the simulation model.