Non-Destructive Measurement of Turn-to-Turn Resistivity Distribution in Full-Scale Racetrack REBCO Coils

The accurate measurement of turn-to-turn resistivity (TTR) distribution in high-temperature superconducting (HTS) coils is important for optimizing performance parameters such as excitation speed and quench risk at different locations. However, this measurement remains challenging due to the non-uniformity of contact pressure. A hybrid methodology integrating the moderate equivalent circuit model with heuristic optimization algorithms (genetic algorithm and particle swarm optimization) is proposed to nondestructively detect the TTR distribution in full-scale racetrack REBCO coils for maglev applications. The systematic comparison demonstrates that Particle Swarm Optimization (PSO) is more suitable for the studied problem under the specific parameter settings, as it shows better voltage fitting accuracy and operational stability compared to Genetic Algorithm (GA). The method first estimates the global TTR range through a lumped circuit model and time constant analysis, then minimizes the deviation between measured and simulated voltage values to obtain the optimal spatial distribution of TTR. Experimental validation on three full-scale racetrack coils (C1, C2, C3) reveals characteristic TTR distributions, where the resistivity values at inner and outer turns are 2–6 times higher than those at middle turns.