Prediction of DC magnetic fields for magnetic cleanliness on spacecraft

Junge, A.; Marliani, F.

doi:10.1109/isemc.2011.6038424

Cited by 45 publications

(28 citation statements)

References 4 publications

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“…Evidently, in the present work, rms needs to be minimized. 4. pbest: The position (D i ) with the lowest fitness value encountered by each particle is known as the personal best or pbest.…”

Section: Pso: a Short Overviewmentioning

confidence: 99%

“…The problem of searching magnetic sources from locally measured magnetic fields belongs to the category of inverse electromagnetic problems and covers many applications [1][2][3][4][5]. Magnetic cleanliness is crucial in space missions.…”

Section: Introductionmentioning

confidence: 99%

“…The parameters of the models (position, magnetic torque) are estimated taking into account of measurements in coil facilities [1]. Based on these measurements, algorithms of renowned capability are employed to solve the inverse problem [4,6].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Multiple Magnetic Dipole Model Parameters From Near Field Measurements Employing Stochastic Algorithms

Kapsalis¹,

Kakarakis²,

Capsalis³

2012

PIER Letters

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Abstract-In this paper, the problem of predicting far field magnitude from near field measurements of an equipment under test (EUT) is studied. Firstly, a multiple magnetic dipole model is developed to simulate the magnetic behavior of the EUT. The parameters of the model (dipoles positions and magnetic moments) are calculated using the values of the near field applying the Particle Swarm Optimization (PSO) algorithm. For the evaluation of the method, extended simulations were conducted, producing theoretical values and distorting them with noise, and then the developed algorithm was used to create the proper model. Finally, the theoretical results are compared to the field assessments the proper models produced.

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“…Evidently, in the present work, rms needs to be minimized. 4. pbest: The position (D i ) with the lowest fitness value encountered by each particle is known as the personal best or pbest.…”

Section: Pso: a Short Overviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Multiple Magnetic Dipole Model Parameters From Near Field Measurements Employing Stochastic Algorithms

Kapsalis¹,

Kakarakis²,

Capsalis³

2012

PIER Letters

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“…The problem of finding the magnetic signature of any Equipment Under Test (EUT) is crucial in many applications and has been thoroughly discussed [1][2][3][4][5]. Magnetic cleanliness is one of the most important applications employing the magnetic behavior of EUTs.…”

Section: Introductionmentioning

confidence: 99%

“…The predominant approach employs the development of multiple magnetic dipole models (MDMs), which emulates the magnetic field of EUTs. Based on near-field measurements in coil facilities and employing deterministic [1][2][3][4][5] or stochastic methods, the parameters of the equivalent MDM (position and magnetic torque) may be estimated. The MDM technique has been employed in many applications such as near field analysis in the antennas field [10], electrocardiography simulation [11] and the representation of electromagnetic emissions of an Integrated Circuit [12].…”

Section: Introductionmentioning

confidence: 99%

A Method of Predicting Composite Magnetic Sources Employing Particle Swarm Optimization

Spantideas¹,

Kapsalis²,

Kakarakis³

et al. 2014

PIER M

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Abstract-In this paper, the problem of predicting the parameters (positions and magnetic moments) of an Equipment Under Test (EUT) composed of a magnetic dipole and quadrupole is studied. Firstly, a multiple magnetic dipole and quadrupole model (MDQM) is developed to simulate the magnetic behavior of the EUT. The parameters of the model are calculated using the values of the near field measurements applying the Particle Swarm Optimization (PSO) algorithm. For the evaluation of the method, extended simulations were conducted, producing theoretical values and distorting them with noise, and then the developed algorithm was used to create the proper MDQM. As an evaluation criterion, the relative difference between the theoretical and the MDQM's magnetic field is considered.

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