Optimal Magnetic Cleanliness Modeling of Spacecraft

Chapter
Part of the Springer Optimization and Its Applications book series (SOIA, volume 73)

Abstract

The magnetometers used by spacecraft for scientific research in the near-Earth and interplanetary space are highly sensitive. Since spacecraft contain in general some more or less magnetic parts which can impair scientific measurements, stringent magnetic cleanliness requirements have to be imposed on the spacecraft. In the domain of constant magnetics (magnetostatics), which is part of EMC (electromagnetic compatibility), modeling is a key issue for the verification of the magnetic cleanliness requirements. The paper describes the concept, improvements, and extensions of the multiple magnetic dipole modeling (MDM) method which had been introduced by the author in 1977 and which then has been used by numerous international scientific spacecraft projects during more than three decades. Specific issues, like the NLP method chosen and like the problem of the ambiguity of solutions, are presented in detail. Special techniques for the handling of model parameter constraints, for optimal MDM sizing, for avoidance of relative minima, and for multiple-point far-field compensation are presented as well. The extension of the MDM method to field gradient measurements is formulated and demonstrated by a significant example. Some challenging applications of MDM to spacecraft provide insight in practical modeling problems. Finally, a short description of the MDM software used is given.

Keywords

Magnetic cleanliness Multiple dipole model Magnetic field and field gradient modeling Magnetic testing Magnetic compensation Inversion problems 

Acronyms

ASTOS

Astos Solutions GmbH, Germany

CNES

Centre National d’Etudes Spatiales, France

CSG

Centre Spatial Guyanais, French Guiana

CSP

Magnetic Cleanliness Specification Point

ECG

Electrocardiography

EEG

Electroencephalography

EMC

Electromagnetic Compatibility

ESA

European Space Agency, Paris

ESTEC

European Space Technology Centre, Netherlands

F2, F6, F7

RTG flight models

FGM

Fluxgate Magnetometer

FGMI

Inboard Magnetometer

FGMO

Outboard Magnetometer

GAMAG

MDM Software

GRB

Solar X-ray and Cosmic Gamma-Ray Burst Instrument

GSFC

Goddard Space Flight Center, USA

IABG

Industrieanlagen Betriebsgesellschaft, Germany

ISEE-B

International Sun-Earth Explorer

KSC

Kennedy Space Center, USA

MAG-1

Magnetometer

MCF

Mobile Coil Facility

MDM

Multiple Dipole Model

MEG

Magneto Encephalography

MFSA

Magnet-Field simulations-Anlage, IABG, Germany

NLP

Non-Linear Programming

RTG

Radioisotope Thermoelectric Power Generator

S/C

Spacecraft

SCS

Spacecraft Coordinate System

SNR

Signal-to-Noise Ratio

TCS

Test Coordinate System

TSS

Tethered Satellite System

TWT

Travelling Wave Tube

UCS

Unit Coordinate System

URAP

Unified Radio and Plasma Wave Instrument

VHM

Vector Helium Magnetometer

References

  1. 1.
    Musmann, G., Neubauer, F.M., Lammers, E.: Observations by the Helios-1 spacecraft. J. Geophys. 42, 591–598 (1977)Google Scholar
  2. 2.
    Musmann, G.: Design guide for magnetic cleanliness control, internal paper GIOTTO (1982)Google Scholar
  3. 3.
    Kuegler, H.: Performance improvement of the magnetic field simulation facility MFSA. In: Proceedings of the 5th International Symposium on Environmental Testing for Space Programmes, Noordwijk (ESA SP-558, June 2004). Compiled by: K. Fletcher, pp. 407–414, Bibliographic Code: 2004ESASP.558.407K, p. 409, § 3 (2004)Google Scholar
  4. 4.
    Mehlem, K.: Multiple magnetic dipole modeling and field prediction of satellites. IEEE Trans. Magn. 14(5), 1064–1071 (1978). ISSN: 0018-9464CrossRefGoogle Scholar
  5. 5.
    Mehlem, K., Narvaez, P.: Magnetostatic cleanliness of the radioisotope thermoelectric power generators (RTGs) of Cassini. IEEE Int. Symp. Electromagn. Compat. 2, 899–904 (1999). ISBN: 0-7803-5057-XGoogle Scholar
  6. 6.
    Campbell, S.L., Meyer Jr., C.D.: Generalized Inverses of Linear Transformations. Dover, New York (1991)MATHGoogle Scholar
  7. 7.
    Nocedal, J., Wright, S.: Numerical Optimization. Springer, New York (1999). ISBN 0387987932MATHCrossRefGoogle Scholar
  8. 8.
    Rosenbrock, H.H.: An automatic method for finding the greatest or least value of a function. Comput. J. 3, 175–184 (1960). doi: 10.1093/comjnl/3.3.175. doi:dx.doi.org, ISSN: 0010-4620MathSciNetCrossRefGoogle Scholar
  9. 9.
    Zisserman, A.: Robotics Research Group in the Department of Engineering Science, University of Oxford, Andrew Zisserman's Home Page, Lecture Courses, B1 Optimization (Michaelmas Term 2011), Lecture 2 “Newton and Newton like methods, and the amoeba algorithm”, www.robots.ox.ac.uk/~az/lectures/b1/lect2.pdf
  10. 10.
    Duffin, W.J.: Electricity and Magnetism, 4th edn. McGraw-Hill, London (1990)Google Scholar
  11. 11.
    Mehlem, K.: Cassini RTG F5, F2, F7, F6 magnetic analysis report, ESA/ESTEC-JPL internal working paper (1996/1997)Google Scholar
  12. 12.
    Mehlem, K.: Ulysses RTG F3 magnetic compensation, ESA/ESTEC internal working paper 1537 (March 1989)Google Scholar
  13. 13.
    Balogh, A., Beek, T.J., Forsyth, R.J., Hedgecock, P.C., Marquedant, R.J., Smith, E.J., Southwood, D.J., Tsurutani, B.T.: The magnetic field investigation on the Ulysses mission: instrumentation and preliminary scientific results. Astron. Astrophys. Suppl. Ser. 92, 221–236 (1992)Google Scholar
  14. 14.
    Mehlem, K.: Magnetic properties of the Cluster I and II spacecraft, 11 ESA/ESTEC working papers (2000)Google Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  1. 1.European Space AgencyHoehr-GrenzhausenGermany

Personalised recommendations