Title: Optimization of Blowing and Suction Control on NACA 0012 Airfoil Using Genetic Algorithm
1Optimization of Blowing and Suction Control on
NACA 0012 Airfoil Using Genetic Algorithm
- L.Huang, R.P. LeBeau, P.G. Huang
- University of Kentucky
- Th. Hauser
- Utah State University
Supported by Kentucky Science and Engineering
Foundation Kentucky NASA EPSCoR
University of Kentucky, 1/5/2004
2Road Map
- Background
- Genetic Algorithm
- Case Setup
- Two-Jet Control System Optimization
University of Kentucky, 1/5/2004
3Background
- Flow Control Design Process
Design Quality? Time Limit?
Trial Error
Knowledge
Jet Control
Solution CFD Genetic Algorithm
University of Kentucky, 1/5/2004
4Background
Solution CFD Genetic Algorithm
- Encode flow control jet parameters (amplitude,
angle, location) into genetic algorithm
individuals - Evaluate each individual (set of jet parameters)
in CFD simulation to determine lift and drag - Use genetic algorithm to guide evolution to
- configuration of jets that yield maximum lift,
minimum drag. - Test case NACA0012 airfoil with two flow-control
jets
University of Kentucky, 1/5/2004
5Background
- Challenges in Genetic Algorithm
- Traditional binary-coded GA limitation
- Convergence speed
- Preliminary convergence to local optima
- Real-Coded EARND Genetic Algorithm
Explicit Adaptive Range Normal Distribution
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6Genetic Algorithm
Genetic Algorithm
Crossover
Mutation
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7Genetic Algorithm
- General Process and Major Improvement
NND
EARND
Real-Coded
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8Genetic Algorithm
- Binary-Coded Real-Coded (Erased)
Example Encode a five-variable solution in to a
binary string
for
Binary String Chromosome
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9Genetic Algorithm
- Binary-Coded Real-Coded (eraseContinue)
C Notation
struct MyChromosome double x5 //
five-variable double fitRaw // raw fitness
double fitScale // scale fitness MyChromosome
X X.x00.714285 X.x10.571429 X.x20.42
8571 X.x30.285714 X.x40.142857
Real-Coded Using double
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10Genetic Algorithm
- Roulette Wheel Selection Operator
4 individuals per generation
Example Maximum function Optimization
Space
Basic
and if
Space
Improve
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11Genetic Algorithm
Binary-Coded One Cut Point Crossover
Real-Coded Crossover
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12Genetic Algorithm
Binary-Coded One Point Mutation
Improved Real-Coded Mutation
is scale up factor
if
is even generation
or
if
is odd generation
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13Genetic Algorithm
- Normal Distribution Major Improvement 1
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14Genetic Algorithm
- Explicit Adaptive Range Major Improvement 2
During the last 50 evolution
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15Genetic Algorithm
- Test Function 1 Ackleys Function
Genetic Coefficients
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16Genetic Algorithm
- Test Function 1 Ackleys Function (Cont.)
NND-Basic Genetic Algorithm
EARND-Improved Genetic Algorithm
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17Genetic Algorithm
- Test Function 2 Rastringins function
Genetic Coefficients
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18Genetic Algorithm
- Test Function 2 Rastringins function (Cont.)
NND-Basic Genetic Algorithm
EARND-Improved Genetic Algorithm
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19Case Setup
210,000 grid points
Re500,000
Incompressible CFD Code Ghost Finite Volume
Structure 2-D Convective Terms Quick Diffusive
Terms Central Difference
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20Case Setup
- Computation and Experiment Comparison
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21Case Setup
- Control Parameter Selection
Jet Width 0.025c
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22Case Setup
- Control Parameter Selection (Cont.)
Two-Jet Control System Suction Jet Blowing Jet
5 Control Variable
fixed Suction Amplitude 0.03
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23Two-Jet Control System Optimization
- Understand the GA Optimization Process
- 2. Understand the Flow Control Physics
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24Two-Jet Control System Optimization
- Understand the GA Optimization Process
Genetic Coefficient
Aggregate Fitness Function
Suction Location Suction Angle Blowing
Location Blowing Angle Blowing Amplitude
Computation Time
Computation Time/Case (Individual) 1.5 Hour on
16 Intel Xeon 2.66G Processor
Total Time (100 Generation) 45 days on 64 Intel
Xeon 2.66G Processor
around 69,000 hours (Wall Time)
University of Kentucky, 1/5/2004
25Two-Jet Control System Optimization
- Understand the GA Optimization Process
University of Kentucky, 1/5/2004
26Two-Jet Control System Optimization
- Understand the GA Optimization Process
- Convergence History Movie (1)
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27Two-Jet Control System Optimization
- Understand the GA Optimization Process
- Convergence History Movie (2)
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28Two-Jet Control System Optimization
- Understand the GA Optimization Process
University of Kentucky, 1/5/2004
29Two-Jet Control System Optimization
- Understand the GA Optimization Process
Suction Location
Suction Angle
Blowing Amplitude
Blowing Angle
Blowing Locations
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30Two-Jet Control System Optimization
- Understand the Flow Control Physics
- Most fit Individual and variations
Split Single Suction Jet Single Blowing Jet
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31Two-Jet Control System Optimization
- Understand the Flow Control Physics
University of Kentucky, 1/5/2004
32Two-Jet Control System Optimization
- Understand the Flow Control Physics
Suction Angle -90
Suction Amplitude 0.173
Suction Location 0.1, 0.333, 0.567
University of Kentucky, 1/5/2004
33Two-Jet Control System Optimization
- Understand the Flow Control Physics
University of Kentucky, 1/5/2004
34Two-Jet Control System Optimization
- Understand the Flow Control Physics
Split Single Suction Jet Single Blowing Jet
University of Kentucky, 1/5/2004
35Two-Jet Control System Optimization
- Algorithm
- EARND is an efficient algorithm, which is proved
to have the ability to - identify and optimize the control factors in
sequence. - Suction/Blowing Jet Control
- Suction jet is dominant, the blowing jet is
secondary to the overall - fitness improvement.
- Control Parameters
- The most important and fastest converging
parameters are the - suction location and angle.
- The blowing location is of secondary importance,
while the blowing angle - and blowing amplitude are the parameters least
well-constrained and - least critical to the overall performance of the
two-jet system.