Assessment of Two Advanced Aluminium-Based Metal Matrix Composites for Application to High Energy Physics Detectors.

The Outer Tracker of the Compact Muon Solenoid (CMS), one of the large experiments at the CERN Large Hadron Collider, will consist of about 13,200 modules, each built up of two silicon sensors. The modules and support structures include thousands of parts that contribute to positioning and cooling the sensors during operation at -30 °C. These parts should be low mass while featuring high thermal conductivity, stiffness and strength.

Rotating-Coil Measurement System for Small-Bore-Diameter Magnet Characterization.

Rotating-coil measurement systems are widely used to measure the multipolar fields of particle accelerator magnets. This paper presents a rotating-coil measurement system that aims at providing a complete data set for the characterization of quadrupole magnets with small bore diameters (26 mm). The PCB magnetometer design represents a challenging goal for this type of transducer.

Drift-Free Integration in Inductive Magnetic Field Measurements Achieved by Kalman Filtering.

Sensing coils are inductive sensors commonly used to measure magnetic fields, such as those generated by electromagnets used in many kinds of industrial and scientific applications. Inductive sensors rely on integrating the output voltage at the coil's terminals in order to obtain flux linkage, which may suffer from the magnification of low-frequency noise resulting in a drifting integrated signal. This article presents a method for the cancellation of integrator drift.

Dynamic Ferromagnetic Hysteresis Modelling Using a Preisach-Recurrent Neural Network Model.

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.