Automated Simulation Model Validation Using Error Metrics
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Solution Overview
Problem
Existing methods for validating simulation models are inefficient in accounting for uncertainties and often require real measurements, leading to conservative results and unnecessary component oversizing in virtual design.
Innovation Solution
An automated method using area and modified area validation metrics to assess simulation models by determining model-form errors based on deviations between simulated and reference values, considering both epistemic and aleatory uncertainties, allowing for the evaluation of simulation model accuracy without real measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If real measurements are used for validation, then measurement precision is improved, but loss of time and productivity are worsened due to the need for physical testing
Solution Approach 1:
The patent creates virtual copies of physical measurement systems through simulation models. These digital twins replicate the behavior of physical components, allowing validation to be performed on virtual models rather than requiring physical prototypes. The simulation model generates virtual measurement data that can be compared against reference values, eliminating the need for time-consuming physical testing while maintaining validation accuracy.
Solution Approach 2:
The patent performs validation actions in advance by testing simulation models against reference data before actual product development and manufacturing. By validating the simulation model upfront, the system establishes confidence in the virtual design tools, enabling subsequent virtualized validation without requiring repeated physical measurements during the development process.
2Productivity
If simulation models are used for virtual design, then productivity is improved, but manufacturing precision is worsened due to conservative design approaches
Solution Approach 1:
The patent implements a feedback mechanism where the simulation model is continuously validated against reference measurement data. The validation results provide feedback on the model's accuracy, allowing for refinement of the simulation parameters and uncertainty quantification. This feedback loop ensures that the virtual design tools maintain high precision by correcting deviations between simulated and actual behavior, enabling confident use of simulation results for manufacturing decisions.
Solution Approach 2:
The patent employs parameter changes by adjusting simulation model parameters based on validation results and uncertainty analysis. By modifying parameters such as material properties, boundary conditions, and tolerance ranges according to validated data, the simulation model achieves more accurate predictions of component behavior, reducing conservative oversizing while maintaining reliability.
3Reliability
If uncertainty quantification is performed, then reliability is improved, but device complexity is worsened due to additional computational requirements
Solution Approach 1:
The patent segments the uncertainty quantification process into distinct components: epistemic uncertainty (related to model knowledge) and aleatory uncertainty (inherent variability). By separating these uncertainty types, the system can apply appropriate validation and analysis methods to each, managing complexity through structured decomposition rather than treating uncertainty as a monolithic problem.
Solution Approach 2:
The patent creates a universal validation framework that can assess simulation models across different applications and industries. The validation metric and uncertainty quantification approach are designed to be application-agnostic, working with various types of simulation models (structural, thermal, fluid dynamics, etc.) through a common methodology, thereby reducing the need for application-specific complexity while maintaining reliability.
Data Source
AI summary
A method for the automated assessment, especially validation, of a simulation model which is used to simulate measured values of a quantity that is defined by one fixed parameter and one varying parameter. Multiple simulation values are determined for the quantity with the aid of the simulation model. Multiple associated reference values are determined for the quantity. For each of multiple values of the varying parameter, in each case a model-form error is determined as deviation between the simulation value with respect to this value of the varying parameter and the reference value with respect to this value of the varying parameter. On the basis of the model-form errors for the multiple values of the varying parameter, a function of the model-form error is determined depending on the varying parameter and used for the assessment, especially validation, of the simulation model.