No abstract

Contact resistance is key for stable operation of electrical contact equipment, and can also be extensively applied. For Tokomak devices in fusion reactors, contact resistance of the superconductor magnet system strongly relates to the alternating current (AC) loss of the cable; the cable is assembled using a certain number of contacting superconducting tapes coated with copper layers on both sides. The contact resistance of a metal solid surface is affected by many factors. In this work, the contact resistance of copper surface samples was studied experimentally under variable normal cyclic load, temperature and number of contact surfaces. This is consistent with real-world working conditions, as the structure of superconducting cables can be changed, and such cables are used under cyclic electromagnetic forces in temperatures which range from room to working temperature. Experimental results showed that contact resistance decreased rapidly with an increase of load. Further, when temperature was varied from 77 to 373 K, the load–unload contact resistance lag decreased. When the number of contact surfaces was increased, contact resistance increased. Finally, a fitted formula describing the relationship between contact resistance and cyclic times, temperature and number of contact interfaces was determined. This formula can be used to predict variation trends of contact resistance in complex environments and provide more accurate contact resistance parameters for calculating the AC loss of superconducting cables.

No abstract

This paper establishes a data-driven Neural Network (NN) framework. The contact resistance of T2 copper blocks with different roughnesses is predicted by deep learning at room temperature and cyclic loading. The contact resistance problem can be regarded as a regression problem of mapping the high-dimensional array space of multiple variables to the contact resistance. This paper measures the contact resistance of copper blocks with different surface roughnesses under loading and unloading states and obtains the original dataset required by the algorithm. The data characteristics include three surface topography parameters, number of cyclic loads, loading and unloading conditions, and load magnitude, with the data labeled contact resistance. This paper compares the results of the NN model and Holm model results to verify the NN model’s effectiveness. The comparison results show that the prediction results of the NN are consistent with the predictions of the Holm model. After training and debugging, the root mean square error of the multiple hidden layers neural network test set is 6.81%, showing a good prediction effect. In conclusion, the deep learning algorithm provides a new way for fast and accurate prediction of the relationship between T2 copper blocks and contact resistance under cyclic loading times and unloading states.

No abstract