Application of genetic algorithms to the optimization of hybrid rockets

34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference and Exhibit

Volumn , Issue , 1998, Pages

  Schoonover, P L ^a   Crossley, W A ^a   Heister, S D ^a  

^a PURDUE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BOOSTERS (ROCKET); OPTIMIZATION; PROPULSION; ROCKETS;

CHAMBER PRESSURE; CONTINUOUS VARIABLES; DISCRETE VARIABLES; GENETIC-ALGORITHM OPTIMIZATIONS; GROSS LIFT-OFF WEIGHTS; OPTIMAL SOLUTIONS; OPTIMIZE DESIGN; PURDUE UNIVERSITY;

GENETIC ALGORITHMS;

EID: 33845388824     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. Goldberg, D. E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, Reading, MA, 1989.
2. Lin, C.-Y., and Hajela, P., “Genetic Algorithms in Optimization Problems with Discrete and Integer Design Variables,” Engineering Optimization, Vol. 19, 1992, pp. 309-327.
3. Crossley, W. A. and Laananen, D. H., “Conceptual Design of Helicopters via Genetic Algorithm,” Journal of Aircraft, Vol. 33, No. 6, Nov.-Dec. 1996, pp. 1062-1070.
4. Heister, S. D., “Propellants for Low Cost Solid Rocket Boosters”, TOR-0086(6453-01)-1, The Aerospace Corporation, El Segundo, CA, February, 1986.
5. Ventura, M. and Heister, S., “Hydrogen Peroxide as an Alternate Oxidizer for a Hybrid Rocket Strap-on Booster”, AIAA 93-2411, 29th Joint Propulsion Conference, 1993.
6. Werthman, W. L., and Schroeder, C. A., “A Preliminary Design Code for Hybrid Propellant Rockets,” AIAA-94-0006, AIAA Aerospace Sciences Meeting, 1994.
7. Vonderwell, D. J., Murray, I. F., and Heister, S. D., “Optimization of Hybrid Rocket Engine Fuel Grain Design”, Journal of Spacecraft and Rockets, V 32, No. 6, pp 964-969, 1995.
8. Kalt, S., and Bendal, D., “Conical Rocket Performance Under Flow Separated Conditions”, J. Spacecraft and Rockets, V2, No.3, pp 447-449, 1965.
9. Holland, J. H., Adaptation in Natural and Artificial Systems, MIT Press, Cambridge, MA, 1992.
10. Williams, E. A., and Crossley, W. A., “Empirically-Derived Population Size and Mutation Rate Guidelines for a Genetic Algorithm with Uniform Crossover,” In: Soft Computing in Engineering Design and Manufacturing, P. K. Chawdhry, R. Roy and R. K. Pant (editors), Springer-Verlag, 1998, pp. 163-172.