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匀场线圈研究与设计工程实践平台搭建

Construction of an engineering practice platform for uniform-field coil research and design
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摘要 匀场线圈是量子传感器中的关键部件,在原子磁强计实验中,线圈产生的磁场用于抵消剩余磁场,使磁强计在接近零磁的均匀磁场下工作,实现超高灵敏磁场测量。实验室搭建了支持匀场线圈理论研究、工程设计、测试验证的工程实践平台。平台以线圈磁场分布物理模型为基础,支持利用程序算法优化线圈结构参数,设计加工并测试线圈常数和均匀度指标。学生通过平台完成理论建模和工程实验,加强了对线圈磁场原理的理解,在优化设计过程中锻炼了数学思维,提升了工程实践能力。 [Objective]Quantum sensing is an emerging technology that uses quantum behavior to achieve ultrahigh sensitivity.Atomic magnetometers are being commercialized,and the talents represented by engineering graduate students have become leaders and innovators in the quantum sensing industry.However,there are not enough graduates with knowledge of the quantum industry to meet the needs of companies,and it will be advantageous to adopt joint industry–education cultivation.Uniform-field coils are key components in quantum precision measurements.In atomic magnetometry,the uniform magnetic field generated by the coil compensates for the residual magnetic field,and the magnetometer can operate in a uniform zero magnetic field to achieve ultra-high-sensitivity magnetic field measurements.Based on uniform-field coils,this study proposes an engineering practice platform that includes theoretical analysis,parameter optimization,simulation,and experimental testing of the coil,forming a coherent process in which graduate students can have the opportunity to understand quantum engineering.[Methods]The proposed practice platform consisted of coil design and experimental testing.First,the magnetic field distribution inside the uniform field coil is analyzed using the magnetic vector potential.Owing to the coupling effect of the magnetic shield and coil,the expression of the magnetic field distribution is complex.Therefore,an optimization algorithm was used to explore the structural parameters of the coil.Population individuals are constructed with coil structural parameters,which benefit from the advantages of the swarm intelligence optimization algorithm and quickly converge to the optimal solution.The objective function considers the combined effect of the maximum magnetic field deviation and length of the uniform zone,allowing a high degree of uniformity and a large uniform zone.The coil skeleton was designed according to the optimal parameters and fabricated by three-dimensional printing.Next,a high-precision fluxgate magnetometer was used to measure the magnetic field at the center,and the fluxgate was connected to a motion motor to measure the axial magnetic field distribution.A sinusoidal current was applied to the coil using a current controller,and the output signal of the fluxgate was fed to a lock-in amplifier,which was used to demodulate the sinusoidal magnetic field and obtain the amplitude.[Results]A practice platform was built,and a physical coil was designed using the whale optimization algorithm and then fabricated.The coil constant and magnetic field distribution were used to assess the design and fabrication accuracy,and the coil constant was measured to be 64.27±0.01 nT/mA,with a relative error of 0.08%;the magnetic field distribution had a relative error of less than 0.2%,and the maximum magnetic field deviation was 4.65×10–5 in the center range[-10 mm,10 mm],suggesting good uniformity of the magnetic field and meeting the requirement of a uniform magnetic field for quantum sensors.[Conclusions]The engineering practice platform based on a uniform-field coil design fully combines theoretical analysis and engineering experiments,which helps deepen the theoretical understanding of the coil magnetic field,mathematically and physically understand quantum sensors,improve mathematical thinking in the process of optimization design,and improve engineering practice ability.
作者 翟跃阳 黄彬越 刘颖 陆吉玺 全伟 ZHAI Yueyang;HUANG Binyue;LIU Ying;LU Jixi;QUAN Wei(School of Instrumentation and Optoelectronics Engineering,Beihang University,Beijing 100191,China)
出处 《实验技术与管理》 CAS 北大核心 2024年第5期161-165,共5页 Experimental Technology and Management
基金 北京航空航天大学卓越工程师产教联合培养专项研究项目。
关键词 匀场线圈 量子传感器 群体智能优化算法 电磁场 uniform field coil quantum sensor swarm intelligence optimization algorithm electromagnetic field
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