Research on the Design Method of Uniform Magnetic Field Coil Based on the MSR

Pan, Donghua; Lin, Shengxin; Li, Liyi; Li, Ji; Jin, Y.; Sun, Zhiyin; Liu, Tianhao

doi:10.1109/tie.2019.2899544

Cited by 79 publications

(19 citation statements)

References 20 publications

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“…Mimicking the transportation of light in two opposite mirrors, a wire in between two parallel plates that are treated as mirrors will be repetitively reflected, thus generating multiple mirror wires 29,30 . The ith mirror wire is labelled as I i .…”

Section: A Classical Mirror Model For Parallel Platesmentioning

confidence: 99%

“…For a coil in front of an infinitely large and infinitely thick shielding plate, an accepted treatment is substituting the plate by an imaginary coil on the opposite side of the plate as a reflection of the original coil, known as the method of mirror images 27,28 . Pan et al applied the standard images method to optimize square coils enclosed in a magnetically shielded room (MSR) 29 . For the case where the thickness of the plates is several mm, the current of the imaginary coil is, however, influenced by the geometry of the setup as well as the position of the observation point, which is not considered in the method of mirror images.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Three-step Model for Optimizing Coil Spacings Inside Cuboid-shaped Magnetic Shields

Liu,

Schnabel,

Voigt

et al. 2020

Preprint

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Section: A Classical Mirror Model For Parallel Platesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Three-step Model for Optimizing Coil Spacings Inside Cuboid-shaped Magnetic Shields

Liu,

Schnabel,

Voigt

et al. 2020

Preprint

Self Cite

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“…The stability of the environmental magnetic field has a direct effect on the measurement performance of sensors. Consequently, passive magnetic shielding and active magnetic compensation are widely used in the aforementioned sectors [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

A High-Performance Magnetic Shield with MnZn Ferrite and Mu-Metal Film Combination for Atomic Sensors

Fang

Sun

et al. 2022

Materials

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This study proposes a high-performance magnetic shielding structure composed of MnZn ferrite and mu-metal film. The use of the mu-metal film with a high magnetic permeability restrains the decrease in the magnetic shielding coefficient caused by the magnetic leakage between the gap of magnetic annuli. The 0.1–0.5 mm thickness of mu-metal film prevents the increase of magnetic noise of composite structure. The finite element simulation results show that the magnetic shielding coefficient and magnetic noise are almost unchanged with the increase in the gap width. Compared with conventional ferrite magnetic shields with multiple annuli structures under the gap width of 0.5 mm, the radial shielding coefficient increases by 13.2%, and the magnetic noise decreases by 21%. The axial shielding coefficient increases by 22.3 times. Experiments verify the simulation results of the shielding coefficient of the combined magnetic shield. The shielding coefficient of the combined magnetic shield is 16.5%. It is 91.3% higher than the conventional ferrite magnetic shield. The main difference is observed between the actual and simulated relative permeability of mu-metal films. The combined magnetic shielding proposed in this study is of great significance to further promote the performance of atomic sensors sensitive to magnetic field.

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“…Nevertheless, in the derivation of such coil configurations, only the field homogeneity at the centre point is considered. Therefore, once a large space homogeneous magnetic field is required, the classic coil configurations are insufficient in capacity and efficiency [14], and a more targeted and optimized coil system is required. In addition, the classic coil systems consider an ideal slim coil model.…”

Section: Introductionmentioning

confidence: 99%

Multi‐objective optimization design of the large‐scale high‐intensity homogeneous magnetic field coil system based on non‐dominated sorting genetic algorithm (NSGA‐II)

Zhu

Yang

et al. 2022

IET Electric Power Appl

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With the development of modern tokamak devices, the magnetic interference problem has become increasingly serious. On considering the International Thermonuclear Experimental Reactor (ITER), for example, the stray magnetic field around the tokamak device will be over 200 mT. To ensure the reliable operation of the electrical and electronic equipment installed around the tokamak, the magnetic immunity test becomes a mandatory requirement. Generally, a set of coils is employed to realize the required test field. Since the coil power loss and conductor mass are proportional to the dimension and intensity of the test field, optimization design is of great significance. This paper presents the multi‐objective optimization design of a large‐scale high‐intensity homogeneous magnetic field coil system. The analytical model of the multi‐coil system is proposed, and the constrained multi‐objective optimization model is established based on the non‐dominated sorting genetic algorithm. Taking the ITER test coil as an example, the optimization design is performed and the Pareto‐front is figured out. The designed four‐coil system enables the magnetic field immunity tests for equipment within 1 m side‐length at a highest test level of 275 mT with a magnetic field inhomogeneity of 1.05. The validity of the proposed model is verified by the finite element analysis and experimental tests.

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Research on the Design Method of Uniform Magnetic Field Coil Based on the MSR

Cited by 79 publications

References 20 publications

A Three-step Model for Optimizing Coil Spacings Inside Cuboid-shaped Magnetic Shields

A Three-step Model for Optimizing Coil Spacings Inside Cuboid-shaped Magnetic Shields

A High-Performance Magnetic Shield with MnZn Ferrite and Mu-Metal Film Combination for Atomic Sensors

Multi‐objective optimization design of the large‐scale high‐intensity homogeneous magnetic field coil system based on non‐dominated sorting genetic algorithm (NSGA‐II)

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