Course Overview
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 optiSLang. 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.
Prerequisites
- Basic knowledge about statistics and optiSLang is required which can be obtained by attending "Ansys optiSLang Getting Started".
Target Audience:
Engineers and Designers that want to calibrate they simulation to a measurement or a desired system behavior.
Teaching Method:
Lectures and computer practical sessions to validate acquired knowledge.
Self-paced Learning:
Complete a class on your own schedule at your own pace. Scope is equivalent to Instructor led classes. Includes video lecture, workshops and input files. All our Self-Paced video courses are only available with an Ansys Learning Hub subscription. Register for a subscription using the tab below or contact training@ansys.com for more information.
