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Real-Time Optimization of Jet Mixing via Evolution Strategies

  • Paul Wickersham
  • David Parekh
Part of the International Centre for Mechanical Sciences book series (CISM, volume 439)

Abstract

This paper presents initial findings in the application of evolution strategies for real-time optimization of jet mixing enhancement. The experimental apparatus consists of a small-scale jet turbine and pulsed fluidic actuators operated under computer control. An optimization algorithm determines in real time the actuator frequency selected at each iteration based on minimizing the plume centerline temperature. This approach successfully determined an optimal actuator frequency that corresponds to the global minimum for the system without any a priori information about the flow or actuator characteristics. Additional results are presented that address robustness to noise and changes in plant conditions and convergence characteristics.

Keywords

Mach Number Global Minimum Strouhal Number Centrifugal Compressor Actuator Frequency 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Rechenberg, I. (1971). Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution, Fromman-Holzboog.Google Scholar
  2. Schwefel, H. P. (1974). Numerische Optimierung von Computer-Modellen, Birkhauser, Basel.Google Scholar
  3. Hoffmeister, F., and Back, T. (1991). “Genetic algorithms and evolution strategies: Similarities and differences,” Proceedings, 1t International Conference on Parallel Problem Solving from Nature, Berlin, Springer.Google Scholar
  4. Coley, D. A. (1999). An introduction to Genetic Algorithms for Scientists and Engineers, World Scientific Publishing Co. Pte. Ltd., Singapore.Google Scholar
  5. Haupt R. L., and Haupt S. E. (1998). Practical Genetic Algorithms, John Wiley & Sons Inc., New York.MATHGoogle Scholar
  6. Koumoutsakos, P., Freund, J., and Parekh, D. E. (1998). “Evolution Strategies for Jet Flow Control,” the 7th Proceedings of the Center for Turbulence Research.Google Scholar
  7. Parekh, D. E., Kibens, V., Glezer, A., Wiltse, J. M., and Smith, D. M. (1996). “Innovative Jet Flow Control: Mixing Enhancement Experiments,” 34th Aerospace Sciences Meeting and Exhibit, American Institute of Aeronautics and Astronautics Paper 96–0308.Google Scholar

Copyright information

© Springer-Verlag Wien 2003

Authors and Affiliations

  • Paul Wickersham
    • 1
  • David Parekh
    • 2
    • 3
  1. 1.Woodruff School of Mechanical EngineeringGeorgia Institute of TechnologyAtlantaUSA
  2. 2.Georgia Tech Research InstituteGeorgia Institute of TechnologyAtlantaUSA
  3. 3.Aerospace, Transportation, and Advanced Systems LaboratoryUSA

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