| modeFRONTIER / Star-CD tutorial: optimization of a blunt object |
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3.3.1. Post-processing of the Mono-objective optimization
If we were not able to run the Multi-objective with constraints optimization
we can load the project for the post-processing from:
.../modeFRONTIER30x/doc/html/caeInt/star-CD/
blunt/prj_full/monoObj/StarCD-Mono.prj
Once we move to the Design Space tab we
enter into the post-processing environment.
On the left we find a new tool bar where there
are all post-processing tools for result assessment.
Clich on the Design Table icon
to show the complete result database
(Input and Output Variables, Objective and Constraints),
see Fig. 1.
This table updates while the optimization is running and each design
is written as soon as it is calculated.
When the optimization has finished we can mark the design belonging to
the Pareto Frontier selecting the action Mark > Mark Pareto Design > only Real
from the Edit menu.
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| Fig. 1: Design Table |
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While the project is running we can click on the History chart icon
and Multiple History Chart icon
to monitor the evolution of the optimization process.
To see the evolution of the aerodynamic efficience click on the
in the tool bar on the left or choose the History Chart
in the Assessment > Add Design chart menu, select
the Objective eff and click Ok,
see Fig. 2.
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| Fig. 2: History Chart Creation |
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A new window will appear into the design space and it shows us the value of the objective for each design, see Fig. 3.
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| Fig. 3: History Chart : the objective history |
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and select all the Input Variable (keep pressing the Shift or Ctrl button while
we are selecting to select more than one Variable), see Fig. 4.
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| Fig. 4: Miltiple History Chart Creation |
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A new window will appear into the Design Space and it show us the value of the Input Variables for each design, see Fig. 5.
Note: In order to have a chart like in Fig. 5 RBM on the chart and a menu will appear. Select the Axis Properties item, mark the normalized check box in the Chart Properties window and click on Ok to apply the new properties.
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| Fig. 5: Multiple History Chart : the input variables history |
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As we can see the optimization algorithm has started evaluating the designs
belonging to the D.O.E. (desing from 0 to 15) and values of the Input
Variable are random accordingly with the algorithm we used to create the D.O.E.
These D.O.E. designs define the first population of the genetic algorithm and they will evolve
in order to achieve the goal.
In this case we can see how
the Input Variables are converging towards the place
where the value of the function is maximum
(high value for oc and hs and low value for rcrf and hc)
The only optimization goal is the aerodynamic efficency so
the optimization process does not take into account other interesting quantities
such as lift and drag.
For example create a scatter chart to relate
the aerodynamic efficency with the lift.
To see the scatter plot of aerodynamic efficency versus lift click on the
icon
and select lift among Output Variables
and eff among Objectives, see Fig. 6.
Even if there is not visible any relationship between lift and eff we can see
that to have high eff we have a very low lift.
If we are not satisfied with the lift of our model we have to run a
Multi-objective Optimization with Constraints.
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| Fig. 6: Scatter Chart |
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