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finland_japan_joint_seminor

Associate Professor at Tokyo Metropolitan University um Tokyo Metropolitan University
27. Jun 2013
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finland_japan_joint_seminor

  1. Efficient Global Optimization Applied to Wind Tunnel Evaluation Based Optimization for Improvement of Flow Control by Plasma Actuator ○Masahiro Kanazaki(Tokyo Metropolitan University) Takashi Matsuno (Tottori University) Kengo Maeda (Tottori University) Hiromitsu Kawazoe (Tottori University) Japan-Finland Joint Seminar 2013
  2. Contents Introduction  Overview of Active Flow Control by Means of Plasma Actuator Objectives Optimization Method Efficient Global Optimization (EGO) Experimental Setup Formulation Results Conclusions 2
  3. Introduction(1/3) Requirements of flow control around aircraft Take-off and landing Pitching, rolling and yawing motion ➔ Large aerodynamic force under the large scale flow 3  Complex geometry  Noise Improvement of aerodynamics at landing and take-off
  4. Introduction(2/3) Plasma Actuator: PA Electric device for active flow control Induced flow (Jet) is appeared by ionization of the air between exposed electrode and insulated electrode Alternating current (AC) is supplied. Small and light weight device 4
  5. Introduction(3/3) Pulse Width Modulation(PWM) PA  Efficient AC supplement for PA  Optimum values of (T1, T2) or (1/T1, 1/T2) are unknown. Requirement to find the optimum AC wave form Flow simulation by CFD*: over 10 hours. Real time scale in wind tunnel: 1~ sec. → Optimization during a wind tunnel experiment in real time 5 *CFD: Computational Fluid Dynamics
  6. Objectives Wind tunnel evaluation based optimization Optimization during a wind tunnel experiment in real time Efficient Global Optimization ~ Kriging model based Genetic Algorithm Improvement of flow control by PA Designing AC wave form 6
  7. 7 Optimization Method(1/5)  Surrogate model:Kriging model  Interpolation based on sampling data  Standard error estimation (uncertainty) )()( ii y xx   global model localized deviation from the global model  EI(Expected Improvement)  The balance between optimality and uncertainty  EI maximum point has possibility to improve the model. Improvement at a point x is I=max(fmin-Y,0) Expected improvement E[I(x))]=E[max(fmin-Y,0)] To calculate EI, Jones, D. R., “Efficient Global Optimization of Expensive Black-Box Functions,” J. Glob. Opt., Vol. 13, pp.455-492 1998.
  8. 8 Optimization Method(2/5) , :standard distribution, normal density :standard errors Surrogate model construction Multi-objective optimization and Selection of additional samples Sampling and Evaluation Evaluation of additional samples Termination? Yes Knowledge discovery Knowledge based design No Kriging model Genetic Algorithms Wind tunnel Exact Initial model Initial designs Additional designs Improved model Image of additional sampling based on EI for minimization problem.
  9. 9 Optimization Method(3/5)  Heuristic search:Genetic algorithm (GA)  Inspired by evolution of life  Selection, crossover, mutation  BLX-0.5 EI maximization → Multi-modal problem Island GA which divide the population into subpopulations Maintain high diversity
  10. Optimization Method(4/5) Fully automated optimization based on the wind tunnel evaluation. Wind tunnel testing is incorporated into EGO. • NI LabVIEWTM is employed. 10 Design variable (Power supply) Objective function(Aerodynamic force)
  11. Optimization method(5/5) Flowfield around semicircular cylinder with two PAs Drag minimization by controlling two design variables related to (T1, T2)  Over 1,000 wind tunnel run will be required if full- factorial design should be carried out. 11 PA off PA on
  12. 12 Formulation  Modulation frequency:  Duty ratio: [%] m p x f T f 1 20 1 1 mod  1 2 100 T T Dcycle  Power supply unit provide frequency fp 9kHz and 20/fp as a one unit wave. [Hz]  Objective function  Design variables Minimize CD (Drag coefficient) 2 .0 ≤ xm ≤ 90.0 10.0 ≤ Dcycle ≤ 70.0
  13. 13 Result(1/5) Lower xm = Higher jet energy 10 initial samples
  14. 14 Result(1/5)
  15. 15 Result(1/5)
  16. 16 Result(1/5)
  17. 17 Result(1/5)
  18. 18 Result(1/5)
  19. 19 Result(1/5)
  20. 20 Result(1/5)
  21. 21 Result(1/5)
  22. 22 Result(1/5)
  23. 23 Result(1/5)
  24. 24 Result(1/5)
  25. 25 Result(1/5) Local minimum Global minimum After 12 additional sampling
  26. 26 Result(2/5) The minimum point could be obtained about 20 wind tunnel runs.
  27.  Higher Dcycle can achieve lower CD  Higher Dcycle as DesA provides a higher AC voltage long time to PAs  Local optimum DesB can also be found  CD can be also reduced with DesB while the total electrical energy is relatively low. → PAs can control the flow with lower electrical energy under proper PWM driving conditions 27 Result(3/5) DesA DesB DesC
  28. 28 Result(4/5) x m [-] D cycle [%] f mod [Hz] C D DesA 2.0 60.0 400.0 0.2985 DesB 15.0 25.0 53.3 0.3272 DesC 88.0 55.0 9.1 0.4105 DesA DesB DesC
  29. 29 Result(5/5) x m [-] D cycle [%] f mod [Hz] C D DesA 2.0 60.0 400.0 0.2985 DesB 15.0 25.0 53.3 0.3272 DesC 88.0 55.0 9.1 0.4105 DesA DesB DesC  DesA: Separated region was reduced, and the streamline was less deformed from the uniform flow  DesB: Separated region was reduced, the streak of smoke far downstream from the model was blurred
  30. Conclusions Wind Tunnel Evaluation–Based Optimization The optimization technique successfully integrated in the operating system of the wind tunnel experiment Automation of the data-acquisition/optimization process Improvement of Flow Control by Plasma Actuator The cost of optimization based on wind tunnel evaluation can be drastically reduced Not only global optimum but also local optimum were found out. 30
  31. 31 Kiitos paljon! Thank you!
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