HS-3010: Fuselage Sizing Trade-Off using Categorical Variables
Learn how to create a Fit in order to investigate the relative effect of the variable on the identified output responses, and identify a combinations of variables that were not explicitly simulated.
- Continuous variables
- Thickness of floor
- Category variables
- Cross sections of the frames
- Load cases
- Free-free normal modes case
Perform the Study Setup
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Start a new study in the following ways:
- From the menu bar, click .
- On the ribbon, click .
- In the Add Study dialog, enter a study name, select a location for the study, and click OK.
- Go to the Define Models step.
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Add a Parameterized File model.
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Click Import Variables.
Six input variables are imported from the fuselage.tpl resource file.
- Go to the Define Input Variables step.
- Review the input variable's lower and upper bound ranges.
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Modify input variable mode.
Perform Nominal Run
- Go to the Test Models step.
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Click Run Definition.
An approaches/setup_1-def/ directory is created inside the study directory. The approaches/setup_1-def/run__00001/m_1 directory contains the input file, which is the result of the nominal run.
Create and Evaluate Output Responses
- Go to the Define Output Responses step.
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Create the Mass output response.
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Using step 2
create two more output responses substituting the Type, Request, and Component
for those shown in Table 1.
Because this is a free-free analysis, Freq1 will be the seventh frequency in the list due to the six rigid body modes (all near zero). Freq2 will be the eighth frequency in the list.
Table 1. Output Response Type Request Component Freq1 Frequency Mode 7 Value Freq2 Frequency Mode 8 Value -
Create the Bending displacement output response, which will have a magnitude of
node 8196 (loading point).
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Create the Torsional rotation output response, which will have a z-direction of
node 8196 (loading point).
- Click Evaluate to extract the response values.
- Click OK.
Run DOE
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Add a DOE.
- Go to the step.
- In the work area, set the Mode to None.
- Click Apply.
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Edit run matrix.
- Go to the step.
- Click Evaluate Tasks.
- Go to the step.
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Review Pareto plot.
The relative effect of a input variable can vary from output response to output response. The most influential input variables when analyzing frequency output responses are Frame Section and Stringer Section Upper. In contrast, the most influential input variables when analyzing the two stiffness conditions are Skin thickness and Stringer Section Upper.
Some input variables can have no effect on output responses. Floor beam thickness has minimal effect on any of the output responses, which indicates that you may want to consider removing this input variable from the analysis.
In a Pareto plot, the effect of input variables on output responses does not measure sensitivity but rather absolute change. Floor thickness has a major effect on Volume. This effect is not a derivative, but a measure of the possible increase over the range of the input variables (the range is the difference between the upper and lower bounds). The floor has a large area and the thickness has very large bounds (+/-0.1 inches), therefore it can make a dramatic impact on Volume as the input variables move through the available space.
Run Least Squares Regression Fit
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Add a Fit.
- In the Explorer, right-click and select Add from the context menu.
- In the Add dialog, select Fit Existing Data and Setup, and click OK.
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Import matrix.
- Go to the step.
- Click Add Matrix.
- In the work area, set Matrix Source to Doe 1 (doe_1).
- Click Apply.
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Define specifications.
- In the work area, Fit Type column, select Least Squares Regression for all output responses.
- Click Apply.
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Evaluate tasks.
- Go to the step.
- Click Evaluate Tasks.
- Go to the step.
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Assess the accuracy of the Fit.
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Click the ANOVA tab and review the Mean
Squares Percent column to see the relative importance of input
variables.
The results should be similar to the results noted in the Pareto Chart tab of the Doe.
- Click the Trade-Off tab to perform "what if" scenarios. In the Inputs pane, modify the values of input variables to see their effect on the output response approximations in the Output pane.