This course gives information about the mathematical basis of model calibration. Furthermore, the issue of relevance and quality of the identified parameters will be discussed. These methods can be applied easily for any RDO task with the help of Ansys optiSLang® process integration and design optimization software. A key role is the definition of signals and signal functions as well as the sensitivity analysis using the Metamodel of Optimal Prognosis (MOP).
Model calibration means to adapt the results of simulation models to actual measurement data. Here, a measured response curve, e.g., a load displacement curve, is taken as a reference and parameters of the simulation model will be modified until the best correlation between reference and simulation is obtained. This method is also known as "reverse engineering". Using this methodology, parameters that cannot be measured directly, such as material parameters, are identified. Therefore, this method is called parameter identification.
Learning Outcomes:
Following completion of this workshop, you will be able to:
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- Find the best fit between simulation and given measurements.
- Definition of calibration task, signals, and signal functions.
Prerequisites:
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- Completion of Ansys optiSLang Getting Started.
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Cost: $450.00
- Course Duration: 6+ HOURS
- Skill Level: Expert
- Skills Gained: Model Calibration and Validation, Parameter Identification Techniques, Sensitivity Analysis Using MOP
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