基于遗传算法的磁自抑制加热器的设计与优化

Optimal Design of Heater with Magnetic Field Self-suppression Based on Genetic Algorithm

超低磁噪声(0~10 nT)的碱金属加热技术是无自旋交换弛豫原子磁力仪实现超高灵敏度的关键,文章提出一种基于遗传算法的磁自抑制加热器多目标优化设计方法。该方法基于毕奥-萨伐尔定律推导目标函数新模型,优化的目标包括加热丝的长度、宽度、厚度和电流方向4类(18个)参数,以此获取加热器最佳磁自抑制性能。利用有限元分析方法,仿真分析了目标区域的磁场分布和温度分布,结果表明:加热器在目标区域的中心区域产生0.02 nT/mA的平均磁场和180.34℃的平均温度。实验测试结果显示:目标区域的磁通密度取值在0.13 nT/mA和0.14 nT/mA范围内,这表明加热器的磁场自抑制性能较好。该研究有助于进一步提高原子磁力仪的性能。

The ultra-low magnetic noise(<10 nT)of alkali metal heating technique is critical for achieving ultra-high sensitivity in spin-exchange relaxation-free atomic magnetometers.In this study,a multi-objective optimization and design method for a magnetic-field self-suppression heater based on a genetic algorithm was proposed.A novel objective function model based on the Biot-Savart law was derived,and four types of parameters were used for the optimization objectives(a total of 18),including the length,width,thickness,and current direction of the heating wire,in order to obtain the best magnetic self-suppression performance from the heater.Using the finite element analysis method,the magnetic field distribution and temperature distribution in the target region were simulated and analyzed,and the results showed that the heater produced an average magnetic field of 0.02 nT/mA and an average temperature of 180.34℃in the center of the target region.Experimental tests confirmed that the magnetic flux density in the target region fell within the range of 0.13~0.14 nT/mA,which indicated that the heater had a better self-suppressing performance for the magnetic field.This work contributes to further enhancing the performance of atomic magnetometers.