OPTIMIZED CONTROLLER DESIGN FOR A BLOWDOWN SUPERSONIC WIND TUNNEL BY USING GENETIC ALGORITHM

Document Type : Research Paper

Authors

Abstract

Blowdown Supersonic Wind Tunnels (SWTs) are generally operated with a constant stagnation pressure in the settling chamber, with control usually provided by one or more pressure regulators or control valves. In this paper, first, a Simulink block diagram code was developed to solve the nonlinear mathematical model of the special BSWT consisting of a set of ordinary differential and algebraic equation that derived from mass and energy conservation equations. At the second step, a Proportional-Integral (PI) controller system was developed in Matlab/Simulink software environment to control the stagnation pressure in the settling chamber and Mach number in the test section. Next, the PI controller parameter is optimized by an intelligent approach such as using Genetic Algorithm (GA). At last, experimental verification has been carried out for supersonic conditions to show the system robustness. Also the system simulation results with the optimized controller for a Mach 3 blowdown run is presented. The results illustrate a great performance of the BSWT model and its controller.

Keywords

References

[1] Pope, A., Goin, K. L. (1965), “High-speed wind tunnel testing”. John Wiley & Sons INC.
[2] Barlow, J. B.,  Rae, W. H., Pope, A. (1999), “Low-Speed Wind Tunnel Testing”. John Wiley and Sons, 3rd Edition.
[3] Silva, M. G., Gamarra, O. R., Koldaev, V. (2009). “Control of Reynolds Number in High Speed Wind Tunnel”. The International Journal of Aerospace of Technology and Management, Vol. 1, pp. 69–77.
[4] Braun, E. M., Lu, F. K., Panicker, P. K., Mitchell, R. R., Wilson, D. R. (2008). “Supersonic Blowdown Wind Tunnel Control Using LabVIEW”. 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, USA, January 7–10.
[5] Cameron, R. N., Ölçmen, S. M., Lewis, D. R., Williams, K. (2008). “Supersonic, variable-throat, blow-down wind tunnel control using genetic algorithms, neural networks, and gain scheduled PID”. Springer Science, Vol. 29, pp. 79–89.
[6] Jacob, V., Binu, L.S. (2009). “Adaptive Fuzzy PI controller for Hypersonic Wind Tunnel Pressure Regulation”. 10th National Conference on Technological Trends, Trivandrum, Kerala, India, November 6–7.
[7] Lu, F.K., Wilson, D.R., Matsumoto, J. (2009). “Rapid valve opening technique for supersonic blow-down tunnel”. Experimental Thermal and Fluid Science Journal, Vol. 33, pp. 551–554.
[8] Zhang, H., Cai, Y., Chen, Y. (2010). “Parameter optimization of PID controllers based on genetic algorithm”. 2010 International Conference on E-Health Networking, Digital Ecosystems and Technologies (EDT), Shenzhen, China, April 17–18.
[9] Jalilvand, A., Kimiyaghalam, A., Ashouri,  A., Mahdavi,  M. (2008). “Advanced particle swarm optimization-based PID controller parameters tuning”. The IEEE International Multitopic Conferenc, Pakistan, December 23–24.
[10] Zhou, K., Qin, J. (2011). “PID controller parameters tuning of main steam temperature based on chaotic particle swarm optimization”. 2011 IEEE International Conference on Computer Science and Automation Engineering (CSAE), Shanghai, China, June 10–12.
[11] El-Gammal, A. A., El-Samahy, A. (2009). “A modified design of PID controller for DC motor drives using Particle Swarm Optimization PSO”. International Conference on Power Engineering, Energy and Electrical Drives, Lisbon, Portugal, March 18–20.
[12] Thomas, Ph. J., (1990), “Simulation of Industrial Process for Control Engineers”. Elsevier Science & Technology Book.
[13] Driskell, L., (1983), “Control-Valve Selection and Sizing”. Instrument Society of America, Research Triangle, 1st Edition.
[14] Bray International, Inc., Document on the www.bray.com.
[15] Wang, X., Yang, H. (2006). “Hierarchical multiple models adaptive feedforward decoupling controller applied to wind tunnel system”. Lecture Notes in Control and Information Sciences, Vol. 344, pp. 287–298.
[16] Franklin, G., Powell, J., Workman, M. (1998), “digital control of dynamic systems”. Addison Wesley Longman, 3rd Edition.
[17] Ogata, K. (2010), “Modern Control Engineering”. Prentice Hall.
 
Journal of Modeling in Engineering
Volume 14, Issue 47
December 2016
Pages 155-169

Files

History

  • Receive Date: 05 December 2013
  • Revise Date: 08 July 2014
  • Accept Date: 15 November 2014

Share

How to cite

Statistics