Friction Factor Analysis Of Forced Flow Supercritical Helium In 40-T Hybrid Superconducting Outsert

High magnetic field has a wide range of applications,such as material science,bioengineering,medical care,industrial applications and agricultural applications.Hybrid magnet is a tool which can provide a necessary experimental platform for the fields above.A 40-T hybrid magnet project is funded by the National Development and Reform Commission and built by the High Magnetic Field Laboratory of the Chinese Academy of Sciences(CHMFL).The device which includes a superconducting outsert and a water-cooled insert magnet produced a field of 40-T in a 32-mm room temperature bore at the end of 2016.The superconducting outsert which is wounded by Nb3Sn Cable-In-Conduit Conductors(CICCs)is forced-flow cooled by 4.5 K supercritical helium.to cool down the 26 coil channels of the superconducting outsert magnet.The coils of the supconducting outsert is divided into 26 cooling channels,and the supercritical helium flows inside the CICCs in parallel while taking away the heat generated by CICCs during operation.It is subjected to frictional resistance to produce pressure drop and heat dissipation.The measurement and analysis of CICCs friction factor is an effective way to optimize cooling strategy and improve the performance of the superconducting magnet.The purpose of this paper is to analyze the friction factor of supercritical helium in CICCs.Then we can optimize the cooling circuit and thermodynamic conditions improve the cryo-stability of the magnet.The Katheder empirical formula is widely used for the calculation of CICC friction factor.As the structure of CICCs in this device is unique,we can't get the friction factor exactly when using this empirical formula.Therefore,it is necessary to correct the Katheder empirical formula to assist in the analysis and research of the flow state and pressure drop of supercritical helium in CICCs.In this paper,we use fluid mechanics model to deal with the experimental data.And then,the Local Weighted Linear Regression(LWLR)of the machine learning algorithms is used to eliminate a large number of anomalies.This algorithm also fixes the deviation of the results caused by the uneven distribution of Reynolds number.The formula we get is very consistent with the experimental data.It will help us go further in the thermo-hydraulic analysis and cooling circuit optimization of the superconducting outsert.