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The LHC operation requires the modelling of the integral transfer function for all superconducting magnets. The electromagnetic behavior of the superconducting magnet can be studied by means of numerical simulations or magnetic measurements. In simulations, the main challenge is to have correct assumptions on the magnet geometry, material properties, and parametrization of the critical current density. This is why it is crucial to benchmark computations with magnetic measurements. However, measurements are complex and costly, hence the number of excitation cycles that can be performed on a real device is typically limited.
In this talk, we will show that it is important to analyze numerical and experimental data in parallel, to build a reliable magnetic model. Among the benefits of this approach is the possibility to enhance the quality and efficiency of the magnetic measurements by validating results and designing relevant experimental cycles, as well as the opportunity to perform data-driven updates of the numerical models.
In order to perform simultaneous analysis of the numerical and experimental data, new software has been developed (PyMagAnalysis - Python based Analysis of the Superconducting Magnets). The architecture of the program will be presented, highlighting the main functionalities. In addition, the main steps to create a model for the operation will be reviewed. Finally, an example of MCBC/MCBY hysteresis analysis will be discussed, revealing the precision of the magnetic model that has been achieved.