Design Optimization of Flow Path with ANSYS-Workbench and ...
Design Optimization of Flow Path with ANSYS-Workbench and ...
Design Optimization of Flow Path with ANSYS-Workbench and ...
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<strong>Design</strong> <strong>Optimization</strong> <strong>of</strong> <strong>Flow</strong> <strong>Path</strong> <strong>with</strong><br />
<strong>ANSYS</strong>-<strong>Workbench</strong> <strong>and</strong> optiSLang<br />
Johannes Einzinger<br />
<strong>ANSYS</strong> Continental Europe<br />
johannes.einzinger@ansys.com<br />
© 2008 <strong>ANSYS</strong>, Inc. All rights reserved. 1 <strong>ANSYS</strong>, Inc. Proprietary
Outline<br />
• Motivation<br />
• Preliminary Consideration<br />
• Practical Example: <strong>Flow</strong> through Air Condition<br />
• Practical Example: Gas Turbine Stage<br />
© 2008 <strong>ANSYS</strong>, Inc. All rights reserved. 2 <strong>ANSYS</strong>, Inc. Proprietary
Motivation<br />
Power Plant<br />
Efficiency<br />
Increase <strong>of</strong> 1%<br />
=Electricity for<br />
1000 MW<br />
50 %<br />
+20 MW<br />
120 000<br />
Inhabitants<br />
© 2008 <strong>ANSYS</strong>, Inc. All rights reserved. 3 <strong>ANSYS</strong>, Inc. Proprietary
Preliminary Consideration<br />
optiSLang<br />
Parametric<br />
Geometry<br />
<strong>ANSYS</strong> <strong>Workbench</strong><br />
Automatic<br />
Meshing<br />
Automatic<br />
CFD-Solution<br />
© 2008 <strong>ANSYS</strong>, Inc. All rights reserved. 4 <strong>ANSYS</strong>, Inc. Proprietary
Preliminary Consideration<br />
CFD Requirements<br />
• Complex geometries<br />
• High dem<strong>and</strong> on meshing (Boundary Layer…)<br />
• Relatively long computation time<br />
Best Practice<br />
• Error estimation for CFD Model<br />
• Model reduction (simple to complex model)<br />
• <strong>Optimization</strong> Strategy<br />
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Practical Example:<br />
<strong>Flow</strong> through Air Condition<br />
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<strong>Flow</strong> through Air Condition<br />
• Geometry Parameterization<br />
• Parametric Meshing<br />
• CFD-Simulation Set-Up<br />
• <strong>Optimization</strong><br />
– <strong>Design</strong> <strong>of</strong> Experiments (DoE)<br />
– Adaptive Response Surface Method (ARSM)<br />
– Evolutionary Algorithm (EA)<br />
– Pareto <strong>Optimization</strong> (Pareto)<br />
© 2008 <strong>ANSYS</strong>, Inc. All rights reserved. 7 <strong>ANSYS</strong>, Inc. Proprietary
Geometry Parameterization<br />
Deflector device made <strong>of</strong><br />
five identical blades<br />
α<br />
Heat Source<br />
Radius unten<br />
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α<br />
L4<br />
L3<br />
R1<br />
R2<br />
Anstellwinkel<br />
Dicke vorn<br />
Dicke hinten<br />
Radius oben
Parametric Meshing<br />
• Hex-mesh for<br />
(1), (2), (3),<br />
static volumes<br />
• Tet-Prism-mesh<br />
for flexible<br />
volume<br />
• Tets in the<br />
volume (4)<br />
• Prism for<br />
boundary layer<br />
resolution (5)<br />
(1)<br />
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(5)<br />
(4)<br />
(2)<br />
(3)
CFD-Simulation Set-Up<br />
Inlet: 10 [m/s], 300 [K]<br />
P 1 Heat source (50 W)<br />
Medium: Air<br />
Lamellenkraft<br />
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T 1<br />
c 1<br />
p v<br />
F L<br />
T 1<br />
p v<br />
Output<br />
Temperatur P 1<br />
Geschw. P 1<br />
Druckverlust<br />
Objective<br />
= T Target<br />
=min
<strong>Design</strong> <strong>of</strong> Experiments<br />
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<strong>Design</strong> <strong>of</strong> Experiments<br />
Lammelenkraft Druckverlust<br />
Anstellwinkel<br />
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<strong>Design</strong> <strong>of</strong> Experiments<br />
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<strong>Optimization</strong><br />
Initial <strong>Design</strong><br />
Best <strong>of</strong> DoE<br />
ARSM<br />
EA<br />
Pareto<br />
Input Parameter:<br />
Winkel α<br />
Radius oben R O<br />
Dicke hinten D H<br />
-13.0<br />
306.6<br />
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α [°]<br />
0.0<br />
-23.7<br />
-25.0<br />
-16.1<br />
R O<br />
[mm]<br />
60.0<br />
59.8<br />
63.2<br />
49.4<br />
42.0<br />
D H<br />
[mm]<br />
8.0<br />
6.9<br />
6.8<br />
6.0<br />
8.0<br />
Target:<br />
Min(T 1 -T Target )<br />
+Min(p V )<br />
T 1<br />
[K]<br />
326.0<br />
304.0<br />
304.8<br />
304.0<br />
p V<br />
[Pa]<br />
14.0<br />
49.7<br />
63.0<br />
27.7<br />
45.6
<strong>Optimization</strong><br />
DoE<br />
EA<br />
Pareto<br />
ARSM<br />
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Summary<br />
• <strong>Design</strong> <strong>of</strong> Experiment<br />
– Shows Pareto Front (100 <strong>Design</strong>s)<br />
• Adaptive Response Surface Method<br />
– Finds Point on Pareto Front (71 <strong>Design</strong>s)<br />
• Evolutionary Algorithm<br />
– Finds Point on Pareto Front (105 <strong>Design</strong>s)<br />
• Pareto <strong>Optimization</strong><br />
– Finds best point (108 <strong>Design</strong>s)<br />
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Practical Example:<br />
Gas Turbine Stage<br />
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Gas Turbine Stage<br />
• Set-Up <strong>and</strong> Parameterization<br />
• <strong>Optimization</strong><br />
• Strategy 1:<br />
– <strong>Design</strong> <strong>of</strong> Experiments (DoE)<br />
– Adaptive Response Surface Method (ARSM),<br />
based on good DoE <strong>Design</strong>(s)<br />
• Strategy 2:<br />
– Evolutionary Algorithm (EA), global search<br />
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Set-Up <strong>and</strong> Parameterization<br />
Boundary Conditions<br />
Total Pressure@Inlet<br />
Total Temperature@Inlet<br />
Mass <strong>Flow</strong> Rate@Outlet<br />
Rotational Velocity<br />
Per Segment<br />
0.25 [atm]<br />
340 [K]<br />
0.06 [kg/s]<br />
5000 [rev/min]<br />
Output Parameter<br />
Total Pressure Ratio<br />
Total Temperature Ratio<br />
Isentropic Efficiency<br />
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<strong>Design</strong> <strong>of</strong> Experiments<br />
Total Temperature Ratio<br />
Total Pressure Ratio<br />
Isentropic Efficiency<br />
Mass <strong>Flow</strong> Rate<br />
Rotational Velocity<br />
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<strong>Design</strong> <strong>of</strong> Experiments<br />
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<strong>Design</strong> <strong>of</strong> Experiments<br />
Total Pressure Ratio<br />
Mass <strong>Flow</strong> Rate<br />
Isentropic Efficiency<br />
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<strong>Optimization</strong><br />
ARSM<br />
EA<br />
Input Parameter:<br />
Mass <strong>Flow</strong> Rate<br />
Rotational Velocity<br />
Initial <strong>Design</strong><br />
Best <strong>of</strong> DoE<br />
Mass<br />
<strong>Flow</strong> Rate<br />
[g/s]<br />
60.0<br />
61.2<br />
60.7<br />
60.4<br />
Target:<br />
Isentropic Efficiency = MAX<br />
Rotational<br />
Velocity<br />
[U/min]<br />
5000<br />
4800<br />
4750<br />
4550<br />
Isotropic<br />
Efficiency<br />
[%]<br />
87.5<br />
87.9<br />
88.0<br />
88.1<br />
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Pressure Distribution<br />
Initial <strong>Design</strong> <strong>Design</strong> <strong>of</strong> Experiments<br />
Adaptive Response Surface Evolutionary Algorithm<br />
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Mach Number, Blade to Blade Plot<br />
Initial <strong>Design</strong> <strong>Design</strong> <strong>of</strong> Experiments<br />
Adaptive Response Surface<br />
Evolutionary Algorithm<br />
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Summary<br />
• Improvement <strong>of</strong> Isentropic Efficiency +0.5%<br />
• Axial Turbine has a global maximum <strong>of</strong> Efficiency<br />
• Strategy 1 (DoE+ARSM):<br />
– Number <strong>of</strong> <strong>Design</strong>s 53 (DoE=22, ARSM=31)<br />
– Efficient to find “the maximum”<br />
• Strategy 2 (EA):<br />
– Number <strong>of</strong> <strong>Design</strong>s 105<br />
– Finds “the maximum” in parameter space<br />
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Thank You!<br />
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