A Cognitive-Heuristic Framework for Optimization of Spaceplane-System Configurations

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 215)

Abstract

A cognitive-heuristic framework for interconnecting analytical aerothermodynamics and mass-modeling parameters to heuristic optimizer is proposed. It evaluates a complex highly-integrated forebody-inlet configuration and representative hypersonic spaceplane based on minimal input data of flight altitude and Mach number only. SHWAMIDOF-FI design tool is used which incorporates salient features of multi-stage cognitive work approach integrated to heuristic optimization. Results show substantial improvement in geometric, performance, and flow parameters as compared to baseline configuration.

Keywords

Cognitive-heuristic framework Heuristic optimization Analytical aerothermodynamics Mass-modeling Forebody-inlet configuration 

References

  1. 1.
    Marsh B, Todd PM, Gigerenzer G (2004) Cognitive heuristics: reasoning the fast and frugal way. In: Leighton JP, Sternberg RJ (eds) The nature of reasoning, Cambridge University Press, New York, p 273–287Google Scholar
  2. 2.
    Lintern G (2009) The framework of cognitive work analysis. Found Pragmatics Cogn Work Anal 20–26 Google Scholar
  3. 3.
    Naikar N, Sanderson P (2001) Evaluating design proposals for complex systems with work domain analysis. Hum Factors 43(4):529–542CrossRefGoogle Scholar
  4. 4.
    Rasmussen J, Pejtersen AM, Goodstein LP (1994) Cognitive systems engineering. Wiley, New YorkGoogle Scholar
  5. 5.
    Vicente K (1999) Cognitive work analysis: towards safe, productive, and healthy computer-based work. Lawrence Erlbaum Associates, MahwahGoogle Scholar
  6. 6.
    Heiser WH, Pratt DT (1994) Hypersonic airbreathing engine performance analysis. Hypersonic airbreathing propulsion, AIAA education series, American Institute of Aeronautics and Astronautics, Washington, pp 150–193Google Scholar
  7. 7.
    Ortwerth PJ (2000) Scramjet flowpath integration. Scramjet Propulsion, AIAA, Washington, pp 1105–1293Google Scholar
  8. 8.
    Heiser WH (2010) Single-stage-to-orbit versus two-Stage-to-orbit airbreathing systems. J Spacecraft Rockets 47(1):222–223. (doi: 10.2514/1.46631)Google Scholar
  9. 9.
    Sarosh A, Feng DY Adnan MA (2011) Configurational aerothermodynamics methodology for derivation of baseline configuration on elliptical cone-wedge integrated waverider. In: 2011 international conference on aerospace engineering and information technology (Aeit 2011), pp 102–107Google Scholar
  10. 10.
    Sarosh A, Yun-Feng D Kamarinchev D (2012) STS forebody material selection method using integrated aerothermodynamic optimization approach. Adv Mater Res J TransTech Publications Inc. (ISSN 1022-6680 doi:10.4028/www.scientific.net/AMR 488-489.1103), 2012, 488–489, 1103–1108
  11. 11.
    Tversky A, Kahneman D (1974) Judgement under uncertainity: heuristics and biases. Science, J Am Assoc Adv Sci 185: 1124–1131Google Scholar
  12. 12.
    Sarosh A, Yun-Feng D, Shi-Ming C (2011) A difference-fractional FOM decision method for down-selection of hypersonic compression system configurations. Aerospace science and technology, Elsevier Inc, USA. doi:http://dx.doi.org/10.1016/j.ast.2012.08.001
  13. 13.
    Hu D, Sarosh A, Dong YF (2011) An improved particle swarm optimizer for parametric optimization of flexible satellite controller. Appl Math Comput 217(21): 8512–8521(ISSN 0096-3003, doi: 10.1016/j.amc.2011.03.055)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Aerospace Engineering, College of Aeronautical EngineeringNational University of Sciences and Technology (NUST)KPKChina
  2. 2.Department of Flight Vehicle Design, School of AstronauticsBeihang University (BUAA)BeijingChina

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