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Design Optimization of Flow Path with ANSYS-Workbench and optiSLang
Design Optimization of Flow Path with ANSYS-Workbench and optiSLang
Johannes EinzingerANSYS Continental Europe
Johannes EinzingerANSYS Continental Europe
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Outline
• Motivation
• Preliminary Consideration
• Practical Example: Flow through Air Condition
• Practical Example: Gas Turbine Stage
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Motivation
120 000
Inhabitants
=Electricity for
+20 MWIncrease of 1%
50 %Efficiency
1000 MWPower Plant
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Preliminary Consideration
ParametricGeometry
AutomaticMeshing
AutomaticCFD-Solution
ANSYS Workbench
optiSLang
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Preliminary Consideration
CFD Requirements
• Complex geometries
• High demand on meshing (Boundary Layer…)
• Relatively long computation time
Best Practice
• Error estimation for CFD Model
• Model reduction (simple to complex model)
• Optimization Strategy
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Practical Example:Flow through Air Condition
Practical Example:Flow through Air Condition
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Flow through Air Condition
• Geometry Parameterization
• Parametric Meshing
• CFD-Simulation Set-Up
• Optimization
– Design of Experiments (DoE)
– Adaptive Response Surface Method (ARSM)
– Evolutionary Algorithm (EA)
– Pareto Optimization (Pareto)
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Geometry Parameterization
Radius untenR2
Radius obenR1
Dicke hintenL3
Dicke vornL4
Anstellwinkelα
Heat Source
Deflector device made of five identical blades
α
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Parametric Meshing
• Hex-mesh for (1), (2), (3), static volumes
• Tet-Prism-mesh for flexible volume
• Tets in the volume (4)
• Prism for boundary layer resolution (5)
(4)
(5)
(2)
(1)
(3)
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CFD-Simulation Set-Up
P1 Heat source (50 W)
Inlet: 10 [m/s], 300 [K]
Medium: Air LamellenkraftFL
Druckverlustpv
Geschw. P1c1
Temperatur P1T1
Output
=minpv
= TTargetT1
Objective
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Design of Experiments
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Design of Experiments
Lammelenkraft Druckverlust
Anstellwinkel
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Design of Experiments
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Optimization
27.7306.66.049.4-13.0EA
45.6304.08.042.0-16.1Pareto
6.8
6.9
8.0
DH
[mm]
304.8
304.0
326.0
T1
[K]
pV
[Pa]RO
[mm]α [°]
63.063.2-25.0ARSM
49.759.8-23.7Best of DoE
14.060.00.0Initial Design
Input Parameter:Winkel α
Radius oben RO
Dicke hinten DH
Target:Min(T1-TTarget)
+Min(pV)
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Optimization
DoE
ARSMEA
Pareto
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Summary
• Design of Experiment
– Shows Pareto Front (100 Designs)
• Adaptive Response Surface Method
– Finds Point on Pareto Front (71 Designs)
• Evolutionary Algorithm
– Finds Point on Pareto Front (105 Designs)
• Pareto Optimization
– Finds best point (108 Designs)
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Practical Example:Gas Turbine StagePractical Example:Gas Turbine Stage
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Gas Turbine Stage
• Set-Up and Parameterization
• Optimization
• Strategy 1:
– Design of Experiments (DoE)
– Adaptive Response Surface Method (ARSM),
based on good DoE Design(s)
• Strategy 2:
– Evolutionary Algorithm (EA), global search
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Set-Up and Parameterization
5000 [rev/min]Rotational Velocity
0.06 [kg/s]Mass Flow Rate@Outlet
340 [K]Total Temperature@Inlet
0.25 [atm]Total Pressure@Inlet
Per SegmentBoundary Conditions
Isentropic Efficiency
Total Temperature Ratio
Total Pressure Ratio
Output Parameter
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Design of Experiments
Mass Flow Rate
Rotational Velocity
Isentropic Efficiency
Total Pressure Ratio
Total Temperature Ratio
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Design of Experiments
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Mass Flow Rate
Isentropic Efficiency
Total Pressure Ratio
Design of Experiments
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Optimization
Isotropic Efficiency [%]
Rotational Velocity [U/min]
Mass Flow Rate [g/s]
88.1455060.4EA
88.0475060.7ARSM
87.9480061.2Best of DoE
87.5500060.0Initial Design
Input Parameter:
Mass Flow RateRotational Velocity
Target:Isentropic Efficiency = MAX
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Pressure Distribution
Initial Design Design of Experiments
Adaptive Response Surface Evolutionary Algorithm
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Mach Number, Blade to Blade Plot
Initial Design Design of Experiments
Adaptive Response Surface Evolutionary Algorithm
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Summary
• Improvement of Isentropic Efficiency +0.5%
• Axial Turbine has a global maximum of Efficiency
• Strategy 1 (DoE+ARSM):
– Number of Designs 53 (DoE=22, ARSM=31)
– Efficient to find “the maximum”
• Strategy 2 (EA):
– Number of Designs 105
– Finds “the maximum” in parameter space
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Thank You!
