Technical and Press Articles
Getting Started with CAE-basierter RDO
RDO...
Model Calibration
An automated model calibration efficiently identifies relevant or non-measurable parameters to achieve the best possible match between simulation results and test data. Sensitivity analyses play a decisive role for the prognosis quality of the simulation models.
Best Practice
- Sensitivity analysis to check unknown parameters for significant influence on the model response
- CoP supports the identification of the best possible response extraction by comparing model and measured values
- CoP verifies the uniqueness of the best possible correlation model between parameter and response variation
- Identification of non-unique (multiple) parameter sets by coupling of parameters
Methods
- Consideration of scalar response values
- Definition of multi-channel signals, e.g. time-displacement curves
- Extensive library of functions, e.g. local values as maximum and minimum amplitudes, global values as integrals of certain properties and more complex signal calculations
- Definition of individual objective functions
- MOP based sensitivity analysis of different signal properties and pre-evaluation
- Several optimization algorithms (e.g. gradient-based or nature-inspired)
Postprocessing & Visualization
- Illustration of statistical evaluations
- Visualization of signal functions and the corresponding reference value for each design
- Sensitivities and approximation of signal function values/parameter sensitivities
- Interactive evaluation of curve fitting and corresponding design images
- Parallel coordinates plot and cluster analyses for the evaluation of uniqueness
Distributors
optiSLang is sold worldwide. A list of our distributors can be found here.
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