Skip to main content

Mission and System Design

  • Chapter
  • First Online:
The International Handbook of Space Technology

Part of the book series: Springer Praxis Books ((ASTROENG))

  • 10k Accesses

Abstract

This chapter presents different approaches to the design of space missions and in particular the overall integration of systems and mission design. The chapter will start with the relationship between mission analysis and system design and the role of mission analysts in the context of the overall design process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 509.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 649.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 649.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. R.H.Battin, An introduction to the mathematics and methods of Astrodynamics, AIAA Education series, AIAA, New York, 1987

    Google Scholar 

  2. S.Kemble Interplanetary Mission Analysis and Design: Springer Praxis 2006

    Google Scholar 

  3. J.R,Wertz, Orbit & Constellation Design & Management, Microcosm, 2001

    Google Scholar 

  4. J. A. Aguilar, A. B. Dawdy, G. W. Law, Aerospace Corporation’s Concept Design Center, Proceedings of the 8th Annual International Symposium of the International Council on Systems Engineering, July 26-30, 1998

    Google Scholar 

  5. G. E. COOK, SATELLITE DRAG COEFFICIENTS, Planet. Space Sci. 1965, Vol. 13, pp. 929 to 946. Pergamon Press Ltd.

    Google Scholar 

  6. ECSS-Q-ST-60-C on Electrical, Electronic and Electromechanical (EEE) components, rev.1, March 2009

    Google Scholar 

  7. International Council of System Engineering, System Engineering Terms Glossary, www.incose.org, date: Oct-1998

  8. D. K. Sobek, A. C. Ward, Principles from Toyota’s set-based concurrent engineering process, the 1996 ASME Design Engineering Technical Conferences and Computers in Engineering Conference

    Google Scholar 

  9. http://jplteamx.jpl.nasa.gov/

  10. M. Bandecchi, B. Melton, B. Gardini, F. Ongaro, The ESA/ESTEC Concurrent Design Facility, EuSEC 2000

    Google Scholar 

  11. R. Cook, G. Kazz, W. Tai, The Mars Pathfinder End-to-end information system – A Pathfinder for the development of future NASA planetary missions, SpaceOps ‘96, Proceedings of the Fourth International Symposium held 16-20 September 1996 in Munich, Germany

    Google Scholar 

  12. CDF System Description, ESA internal document CDF-SYS-001, 20 January 2008

    Google Scholar 

  13. Taguchi G., Quality Engineering through Design Optimization, Kraus International Publications, New York, 1984.

    Google Scholar 

  14. Du X., Chen W. Towards a better understanding of modeling feasibility robustness in engineering design, ASME J. Mech. De. 122 (2) (2000) 291-311.

    Google Scholar 

  15. Chen W., Wiecek M., Zhang J., Quality utility- a compromise programming approach to robust design, ASME J. Mech. De. 121 (2) (1999) 179-187.

    Google Scholar 

  16. N. Rolander, J. Rambo, Y. Joshi, J. Allen, F. Mistree, An approach to robust design of turbulent convective systems, J. Mech. Des. 128 (4) (2006) 844-855.

    Google Scholar 

  17. Y. Jin, B. Sendhoff, Trade-off between performance and robustness: an evolutionary multiobjective approach, in C. Fonseca, P. Fleming, E. Zitzler, K. Deb (Eds.), Evolutionary Multi-Criterion Optimization: Second International Conference, EMO 2003, Springer-Verlag, Hidelberg, 2003, pp. 237-251.

    Google Scholar 

  18. Sundarsen S. Ishii K. Houser D., A robust optimization procedure with variations on design variables and constraints, in : ASME Design Automation Conference, ASME, 1993, pp. 387-394.

    Google Scholar 

  19. Arakawa M., Yamakawa H., Ishikawa H., Robust design using fuzzy numbers with intermediate variables, in: 3rd World Congress of Structural and Multidisciplinary Optimization, 1999.

    Google Scholar 

  20. Choi L., Amd Du K.K., Youn B., Gorsich D., Possibility-based design optimization method for design problems with both statistical and fuzzy input data, in : 6th World Congress of Structural and Multidisciplinary Optimization, Rio de Janeiro, Brazil, 2005.

    Google Scholar 

  21. Youn B., Choi L., Amd Du K.K., Gorsich D., Integration of possibility-based optimization to robust design for epistemic uncertainty, in : 6th World Congress of Structural and Multidisciplinary Optimization, Rio de Janeiro, Brazil, 2005.

    Google Scholar 

  22. Oberkampf W.L. Helton J.C. Investigation of Evidence Theory for Engineering Applications. AIAA 2002-1569, 4th Non-Deterministic Approaches Forum, 22-25 April 2002, Denver Colorado.

    Google Scholar 

  23. Agarwal H., Renaud J.E., Preston E.L. Trust Region Managed Reliability Based Design Optimization using Evidence Theory. AIAA 2003-1779, 44th AIAA/ASCE/AHA Structures, Structural Dynamics and Materials Conference, 7-10 April 2003, Norfolk, Virginia.

    Google Scholar 

  24. Vasile M., Bonetti D. Evolution of the Concurrent Design Process Under Uncertainties. International Concurrent Engineering Workshop, ESA/ESTEC 30 September-1 October 2004.

    Google Scholar 

  25. Vasile M. Robustness Optimisation of Aerocapture Trajectories Design Using a Hybrid Co-evolutionary Approach. 18th International Symposium on Spaceflight Dynamics. 11-15 October 2004, Munich, Germany.

    Google Scholar 

  26. Vasile M. Robust mission design through evidence theory and multiagent collaborative search. Annals of the New York Academy of Sciences, 1065:152–173, December 2005.

    Google Scholar 

  27. Croisard, N., Ceriotti, M., Vasile, M., Uncertainty Modelling in Reliable Preliminary Space Mission Design (extended abstract), Workshop on Artificial Intelligence for Space Applications (IJCAI-07), Hyderabad, India, January 2007.

    Google Scholar 

  28. Croisard N., Vasile M., Kemble S., Radice G., Preliminary Space Mission Design Under Uncertainty. IAC-08-D1.3, Glasgow 2008.

    Google Scholar 

  29. Croisard N., Vasile M., Kemble S., Radice G., Preliminary Space Mission Design Under Uncertainty, Acta Astronautica, 2009, doi:10.1016/j.actaastro.2009.08.004.

  30. Croisard N., Vasile M., System Engineering Design Optimisation Under Uncertainty for Preliminary Space Mission. IEEE Congress on Evolutionary Computation 2009, 18th-21st May, 2009, Trondheim, Norway.

    Google Scholar 

  31. M. Fuchs and A. Neumaier, Handling uncertainty in higher dimensions with potential clouds towards robust design optimization, pp. 376-382 in: Soft Methods for Handling Variability and Imprecision (D. Dubois et al., eds.), Advances in Soft Computing, Vol. 48, Springer 2008.

    Google Scholar 

  32. Manton, K. G., Woodbury, M. A., and Tolley, H. D., Statistical Applications Using Fuzzy Sets, John Wiley, New York, 1994.

    Google Scholar 

  33. Onisawa, T., and Kacprzyk, J., eds. Reliability and Safety Analyses Under Fuzziness, Physica-Verlag Heidelberg, 1995.

    Google Scholar 

  34. Klir, G. J., St. Clair, U., and Yuan, B., Fuzzy Set Theory: Foundations and Applications, Prentice Hall PTR, Upper Saddle River, NJ, 1997.

    Google Scholar 

  35. Dubois, D., and Prade, H., eds. Fundamentals of Fuzzy Sets, Kluwer Academic Publishers, Boston, MA, 2000.

    Google Scholar 

  36. Moore, R. E., Methods and Applications of Interval Analysis, SAIM, Philadelphia, PA, 1979.

    Google Scholar 

  37. Kearfott, R. B., and Kreinovich, V., eds. Applications of Interval Computations, Kluwer Academic Pub., Boston, MA, 1996.

    Google Scholar 

  38. Guan, J., and Bell, D. A., Evidence Theory and Its Applications, Vol. I, North Holland, Amsterdam, 1991.

    Google Scholar 

  39. Krause, P., and Clark, D., Representing Uncertain Knowledge: An Artificial Intelligence Approach, Kluwer Academic Publishers, Dordrecht, The Netherlands, 1993.

    Google Scholar 

  40. Kohlas, J., and Monney, P.-A., A Mathematical Theory of Hints - An Approach to the Dempster- Shafer Theory of Evidence, Springer, Berlin, 1995.

    Google Scholar 

  41. Klir, G. J., and Wierman, M. J., Uncertainty-Based Information: Elements of Generalized Information Theory, Vol. 15, Physica-Verlag, Heidelberg, 1998.

    Google Scholar 

  42. Kramosil, I., Probabilistic Analysis of Belief Functions, Kluwer, New York, 2001.

    Google Scholar 

  43. Dubois, D., and Prade, H., Possibility Theory: An Approach to Computerized Processing of Uncertainty, Plenum Press, New York, 1988.

    Google Scholar 

  44. De Cooman, G., Ruan, D., and Kerre, E. E., eds. Foundations and Applications of Possibility Theory, World Scientific Publishing Co., Singapore, 1995.

    Google Scholar 

  45. Walley, P., Statistical Reasoning with Imprecise Probabilities, Chapman and Hall, London, 1991.

    Google Scholar 

  46. Klir, G. J., and Smith, R. M., On Measuring Uncertainty and Uncertainty-Based Information: Recent Developments, Annals of Mathematics and Artificial Intelligence, Vol. 32, No. 1-4, 2001, pp. 5-33.

    Google Scholar 

  47. Dempster A.P. (1967): “Upper and Lower Probabilities Induced by a Multivalued Mapping”, The Annals of Mathematical Statistics, 38, pp. 325-338.

    Google Scholar 

  48. Shafer G. (1976): A Mathematical Theory of Evidence, Princeton University Press, Princeton.

    Google Scholar 

  49. Shafer G. (1990): “Perspectives on the Theory and Practice of Belief Functions”, International Journal of Approximate Reasoning, 4, pp. 323-362.

    Google Scholar 

  50. Zadeh, L., “Review of Shafer’s A Mathematical Theory of Evidence,” Artificial Intelligence Magazine, Vol. 5, 1984, pp. 81–83.

    Google Scholar 

  51. Tessem B. Apporximation for efficient computation in the theory of evidence. Artificial Intelligence 61 (1993) 315-329, Elsevier.

    Google Scholar 

  52. Bauer M. Approximation for Decision Making in the Dempster-Shafer Theory of Evidence. In Uncertainty in Artificial Intelligence,1996, 73–80, Morgan Kaufmann Publishers.

    Google Scholar 

  53. Vasile M., Robust Optimization of Trajectory Intercepting Dangerous NEO. AAS/AIAA Astrodynamic Specialist Conference, 5-8 August 2002, Monterey, California, U.S.A.

    Google Scholar 

  54. Zhou, Jun, and Zissimos P. Mourelatos, “A sequential algorithm for possibility-based design optimization,” Journal of Mechanical Design, Volume 130, January 2008.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Massimiliano Vasile .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Vasile, M., Kemble, S., Santovincenzo, A., Taylor, M. (2014). Mission and System Design. In: Macdonald, M., Badescu, V. (eds) The International Handbook of Space Technology. Springer Praxis Books(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41101-4_25

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-41101-4_25

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-41100-7

  • Online ISBN: 978-3-642-41101-4

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics