Efficient Global Optimization Applied to Wind Tunnel Evaluation Based Optimization forImprovement 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

Contents

Introduction Overview of Active Flow Control by Means of Plasma

ActuatorObjectivesOptimization Method Efficient Global Optimization (EGO)Experimental Setup

FormulationResultsConclusions

2

Introduction(1/3)Requirements of flow control around aircraftTake-off and landing Pitching, rolling and yawing motion

➔ Large aerodynamic force underthe large scale flow

3

Complex geometry

Noise

Improvement ofaerodynamics atlanding and take-off

Introduction(2/3)Plasma Actuator: PAElectric device for active flow controlInduced flow (Jet) is appeared by ionization of

the air between exposed electrode and insulated electrodeAlternating current (AC) is supplied.

Small and light weight device

4

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 formFlow simulation by CFD*: over 10 hours.Real time scale in wind tunnel: 1~ sec.

→ Optimization during a wind tunnel experiment in real time

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*CFD: Computational Fluid Dynamics

Objectives

Wind tunnel evaluation based optimizationOptimization during a wind tunnel experiment in

real timeEfficient Global Optimization ~ Kriging model based

Genetic Algorithm Improvement of flow control by PA Designing AC wave form

6

7Optimization Method(1/5) Surrogate model：Kriging model

Interpolation based on sampling data Standard error estimation (uncertainty)

)()( iiy xx

global model localized deviationfrom 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.

8Optimization 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.

9Optimization Method(3/5) Heuristic search：Genetic algorithm (GA)

Inspired by evolution of life Selection, crossover, mutation

BLX-0.5EI maximization → Multi-modal problem Island GA which divide the population into

subpopulationsMaintain high diversity

Optimization Method(4/5)

Fully automated optimization based on the wind tunnel evaluation.Wind tunnel testing is incorporated into EGO.

• NI LabVIEWTM is employed.

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Design variable (Power supply)Objective function(Aerodynamic force)

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.

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PA off PA on

12Formulation Modulation frequency：

Duty ratio： [%]

m

p

xf

Tf 1

201

1mod

1

2100TTDcycle

Power supply unit provide frequency fp 9kHzand 20/fp as a one unit wave.

[Hz]

Objective function

Design variablesMinimize CD (Drag coefficient)

2 .0 ≤ xm ≤ 90.010.0 ≤ Dcycle ≤ 70.0

13Result（1/5）

Lower xm = Higher jet energy

10 initial samples

14Result（1/5）

15Result（1/5）

16Result（1/5）

17Result（1/5）

18Result（1/5）

19Result（1/5）

20Result（1/5）

21Result（1/5）

22Result（1/5）

23Result（1/5）

24Result（1/5）

25Result（1/5）

Local minimum

Global minimum

After 12 additional sampling

26Result（2/5）

The minimum point could be obtained about 20 wind tunnel runs.

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

27Result（3/5）

DesA

DesB

DesC

28Result（4/5）

x m [-] D cycle [%] f mod [Hz] C D

DesA 2.0 60.0 400.0 0.2985DesB 15.0 25.0 53.3 0.3272DesC 88.0 55.0 9.1 0.4105

DesA

DesB

DesC

29Result（5/5）

x m [-] D cycle [%] f mod [Hz] C D

DesA 2.0 60.0 400.0 0.2985DesB 15.0 25.0 53.3 0.3272DesC 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

ConclusionsWind Tunnel Evaluation–Based OptimizationThe optimization technique successfully

integrated in the operating system of the wind tunnel experiment Automation of the data-acquisition/optimization

processImprovement of Flow Control by Plasma

ActuatorThe cost of optimization based on wind tunnel

evaluation can be drastically reduced Not only global optimum but also local optimum were

found out.

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Kiitos paljon!

Thank you!