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基于扩展分布式多极模型的磁感应定向测量。

Orientation measurement based on magnetic inductance by the extended distributed multi-pole model.

作者信息

Wu Fang, Moon Seung Ki, Son Hungsun

机构信息

School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore.

School of Mechanical and Nuclear Engineering, Ulsan National Institute of Science and Technology (UNIST), UNIST-gil 50, Eonyang-eup, Ulju-gun, Ulsan 689-798, Korea.

出版信息

Sensors (Basel). 2014 Jun 27;14(7):11504-21. doi: 10.3390/s140711504.

DOI:10.3390/s140711504
PMID:24977389
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4168429/
Abstract

This paper presents a novel method to calculate magnetic inductance with a fast-computing magnetic field model referred to as the extended distributed multi-pole (eDMP) model. The concept of mutual inductance has been widely applied for position/orientation tracking systems and applications, yet it is still challenging due to the high demands in robust modeling and efficient computation in real-time applications. Recently, numerical methods have been utilized in design and analysis of magnetic fields, but this often requires heavy computation and its accuracy relies on geometric modeling and meshing that limit its usage. On the other hand, an analytical method provides simple and fast-computing solutions but is also flawed due to its difficulties in handling realistic and complex geometries such as complicated designs and boundary conditions, etc. In this paper, the extended distributed multi-pole model (eDMP) is developed to characterize a time-varying magnetic field based on an existing DMP model analyzing static magnetic fields. The method has been further exploited to compute the mutual inductance between coils at arbitrary locations and orientations. Simulation and experimental results of various configurations of the coils are presented. Comparison with the previously published data shows not only good performance in accuracy, but also effectiveness in computation.

摘要

本文提出了一种新颖的方法,利用一种称为扩展分布式多极(eDMP)模型的快速计算磁场模型来计算磁感应强度。互感概念已广泛应用于位置/方向跟踪系统和应用中,但由于实时应用中对鲁棒建模和高效计算的高要求,它仍然具有挑战性。最近,数值方法已被用于磁场的设计和分析,但这通常需要大量计算,其准确性依赖于几何建模和网格划分,这限制了其应用。另一方面,解析方法提供了简单且计算快速的解决方案,但由于其在处理实际复杂几何形状(如复杂设计和边界条件等)方面存在困难,也存在缺陷。在本文中,基于现有的用于分析静磁场的DMP模型,开发了扩展分布式多极模型(eDMP)来表征时变磁场。该方法已被进一步用于计算任意位置和方向的线圈之间的互感。给出了各种线圈配置的仿真和实验结果。与先前发表的数据比较表明,该方法不仅在精度方面表现良好,而且在计算方面也有效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/cb862a12e005/sensors-14-11504f18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/ad1fad9038e3/sensors-14-11504f1.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/5601a9ecf7ea/sensors-14-11504f4.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/a1c22daf6012/sensors-14-11504f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/f0f54f47cc7b/sensors-14-11504f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/7301b505d730/sensors-14-11504f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/9978700eda66/sensors-14-11504f11.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/3fb6416810f5/sensors-14-11504f16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/68cdda7a17e9/sensors-14-11504f17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/cb862a12e005/sensors-14-11504f18.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/ad1fad9038e3/sensors-14-11504f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/4691e7aff862/sensors-14-11504f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/e219b103f027/sensors-14-11504f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/5601a9ecf7ea/sensors-14-11504f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/d261c9825126/sensors-14-11504f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/26c2f9a8c1a3/sensors-14-11504f6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/dfa0637c6cb1/sensors-14-11504f7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/a1c22daf6012/sensors-14-11504f8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/f0f54f47cc7b/sensors-14-11504f9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/7301b505d730/sensors-14-11504f10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/9978700eda66/sensors-14-11504f11.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/7789488536f4/sensors-14-11504f12.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/07ada992ea88/sensors-14-11504f13.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/488ad26726dc/sensors-14-11504f14.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/745c5f169386/sensors-14-11504f15.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/3fb6416810f5/sensors-14-11504f16.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/68cdda7a17e9/sensors-14-11504f17.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e7e6/4168429/cb862a12e005/sensors-14-11504f18.jpg

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