Multi-Objective Optimization Design of Self-sensing Rod Position Detector End Compensation Based on MOPSO Algorithm

The self-sensing rod position detector utilizes the variation characteristics of the detecting coil inductance with the rod displacement to achieve continuous rod position measurement. However, the non-uniform magnetic field distribution at the coil ends leads to a nonlinear output signal, reducing the measurement accuracy at both ends. Therefore, this paper proposes a multi-objective optimization design method for winding stepped compensating coils at both ends of the detecting coils: ① Develop a mathematical model for the inductance of the end compensation coils; ② Adopt a multi-objective particle swarm optimization (MOPSO) algorithm for the optimization of the compensation coil structure; ③ Employ the entropy weight method and fuzzy comprehensive evaluation to assign weights to multiple optimization objectives and select a compromise optimal design, thereby efficiently selecting the optimal structural parameters of the compensation coils. By comparing the results before and after compensation through finite element simulation, it was found that the end compensation improved inductance sensitivity by 28.6% and reduced the maximum linear fitting error by 45.8%. Finally, prototype experiments were conducted, which indicated that the inductance sensitivity of the end-compensated detection coil is 0.18 mH/10 mm, with a maximum linear fitting error under 0.18 mH. These results confirm a measurement accuracy of 10 mm and validate the effectiveness of the multi-objective optimization design for the coil. This study provides a theoretical foundation for the optimization of its application in modular small reactor design.