Parameter Fitting, Model Analysis, and Design of Experiments
ModelFitter supports our customers in the automated fitting of model parameters and the design of experiments (DOE). The optimization algorithm used, in combination with efficient evaluation routines, enables simple fitting even for dynamic, highly non-linear models. Our software loads models created in software supporting the FMI standard or compiled from Dymola code and can be used either as an Excel tool or via a Python interface.
Users of the software can first export components or systems that have been modeled with any software that supports the FMI standard (e.g. TIL Suite) as an FMU and then load them into ModelFitter. ModelFitter then determines optimum parameter values for these components or systems to ensure the best possible adaptation of the models to stationary measurement data, results from field calculation methods, or manufacturer specifications. Various mathematical analyses are used to make the fitting process more efficient.
Figure 1: Workflow
ModelFitter for Excel
Using ModelFitter with Integration in Excel
A ready-to-use Excel interface for ModelFitter is available. This allows users to work comfortably in a familiar program. Numerous displays for results are available which support analysis and interpretation of the fitting. In addition, ModelFitter for Excel allows users to take advantage of all the other benefits offered by Excel (e.g. convenient copying and pasting of data as well as adding their own representations and calculations to the document).
Figure 2: Excel interface of ModelFitter
ModelFitter for Python
Using ModelFitter in Python
As an alternative to the Excel tool, we also provide a code-based interface for Python in ModelFitter. This does not offer all the convenient functions of Excel, but the fitting can be operated and evaluated more flexibly. The Python module can also be integrated into your own software environments and scripts. The concise plots familiar from ModelFitter for Excel are also available in Python. ModelFitter for Python is part of Optimization Suite.
Output of Statistical Variables
Improved Understanding of Models with Sensitivity and Data Analysis
ModelFitter users receive important statistical information about the approximation quality of the fitting and about dependencies between the parameters and between the model and database. This enables the analysis of, for example, sensitivities of the selected model parameters, assumptions in the modeling, or inconsistencies in the database. In this way ModelFitter contributes to a better understanding of the model.
Figure 3: Statistical analysis
Design of Experiments
Efficient Test Planning with ModelFitter Statistics and Consulting
The simulation of a system or individual components requires not only a suitable model, but also a suitable database for parameterization. An efficient test plan can be created based on the statistical information provided by ModelFitter. Contact us if you would like our assistance in obtaining your measurements. We will be happy to advise or support you.
Your contact partner
If you have any questions on this topic, please contact: