The Journal of the Astronautical Sciences

, Volume 66, Issue 4, pp 404–418 | Cite as

Modeling for Concise Space Mission Utility Simulation with Apollo as Exemplar

  • Ja’Mar A. WatsonEmail author


Presented is a stochastic modeling method enabling rapid yet comprehensive space mission utility simulation. The method facilitates multivariate analysis with concurrent tradespace exploration, risk assessment, and holistic design while simultaneously exploring, assessing, and developing statistically validated concepts of prospective space missions. Modeling is achieved through the synergistic integration of statistical mechanics, blackbox, Bayesian, ansatz, and analytics techniques. The method is verified for its ability to accurately depict a human spaceflight mission and validated for its ability to perform mission utility analysis by backtesting the Apollo 11–17 missions to the Moon through Monte Carlo simulation.


Mission utility simulation Space mission engineering Surrogate modeling Apollo missions Progspexion 



This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

The author would like to thank all reviewers for their recommendations to this article for publication.


  1. 1.
    Conway, E.: Exploration and Engineering: The Jet Propulsion Laboratory and the Quest for Mars. John Hopkins University Press, Baltimore (2015)Google Scholar
  2. 2.
    Dunabr, B., & Loff, S.: The Apollo Missions. (National Aeronautics and Space Administration) Retrieved from Apollo Overviewe (2015)Google Scholar
  3. 3.
    Eickhoff, J.: Simulating Spacecraft Systems. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Elliott, L. R., Wesley, R. J., & Schiflett, S. G.: Scaling Scenarios for Synthetic Task Environments: Issues Related to Fidelity and Validity. Human Factors and Ergonomics Society, 1, pp. 377–381. Santa Monica, CA (2001)Google Scholar
  5. 5.
    Kennedy, J.F.: Address to Joint Session of Congress May 25, 1961. Presidential Library and Museum. Columbia Broadcasting System, Washington, D.C. (1961)Google Scholar
  6. 6.
    Moeller, R. C., et al.: Assessment Using the JPL Rapid Mission Architecture (RMA) Team Approach, IEEE Aerospace Conference, (pp. 1–11). Big Sky, MT (2011)Google Scholar
  7. 7.
    National Aeronautics and Space Administration: NASA’s Exploration Systems Architecture Study. NASA-TM-2005-214062 (2005)Google Scholar
  8. 8.
    Nikolaidis, E., Haftka, R., & Rosca, R.: Comparison of Probabilistic and Possibility-Based Methods for Design Against Catastrophic Failure Under Uncertainty. Virginia Polytechnic Institute and State University and the University of Florida (1998)Google Scholar
  9. 9.
    Prassinos, P., et al.: Constellation Probabilistic Risk Assessment (PRA): Design Consideration for CEV. OSMA-PRA-07-01, National Aeronautics and Space Administration, Safety Assurance and Requirements Division, Washington, D.C. (2006)Google Scholar
  10. 10.
    Sforza, P. M.: Safety, Reliability, and Risk Assessment. In Manned Spacecraft Design Principles. Waltham, M.A.: Elsevier (2016)CrossRefGoogle Scholar
  11. 11.
    Watson, J.A.: Holistic Methodology for Stochastic Mission Utility Analysis. Int. J. System of Systems Engineering. 8(2), 174–188 (2017)CrossRefGoogle Scholar
  12. 12.
    Wertz, J. R.: ORS Mission Utility and Measures of Effectiveness. 6th Responsive Space Conference. Los Angeles, C.A. (2008)Google Scholar
  13. 13.
    Williams, M. A., & Alleyne, A. G.: Switched-Fidelity Modeling and Optimization for Multi-Physics Dynamical Systems. IEEE American Control Conference, (pp. 3104–3109). Portland, OR (2014)Google Scholar
  14. 14.
    Young, D. A., & Wilhite, A.: A Methodology for Achieving Optimal Reliability and Cost in a Lunar Architecture. American Institute of Aeronautics and Astronautics (2009)Google Scholar

Copyright information

© American Astronautical Society 2019

Authors and Affiliations

  1. 1.Watson Institute for Scientific Engineering ResearchArlingtonUSA

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