No abstract

In this work, a Preisach-recurrent neural network model is proposed to predict the dynamic hysteresis in ARMCO pure iron, an important soft magnetic material in particle accelerator magnets. A recurrent neural network coupled with Preisach play operators is proposed, along with a novel validation method for the identification of the model’s parameters. The proposed model is found to predict the magnetic flux density of ARMCO pure iron with a Normalised Root Mean Square Error (NRMSE) better than 0.7%, when trained with just six different hysteresis loops. The model is evaluated using ramp-rates not used in the training procedure, which shows the ability of the model to predict data which has not been measured. The results demonstrate that the Preisach model based on a recurrent neural network can accurately describe ferromagnetic dynamic hysteresis when trained with a limited amount of data, showing the model’s potential in the field of materials science.

In this paper, we present a measurement model for estimating the magnetic field of a synchrotron-type particle accelerator, based on sensors installed in a reference magnet. The model combines the calibration of the individual sensors with the experimental characterization of the magnets to infer, in absolute terms, the value of the average field in the ring, as needed for the real-time feedback control of the accelerator. Implementation of this model at the extra low energy antiproton (ELENA) ring at the European Organization for Nuclear Research (CERN) is used as a case study. We describe first the measurement setup and method, followed by the detailed definition of the model, along with its parameters and an evaluation of their value and uncertainty. Next, we assess the combined uncertainty of the whole measurement chain. Finally, we discuss the results obtained so far during the machine commissioning phase and outline our plans for future improvement.