# HS-4215: Multi-Disciplinary Design Optimization Study

Learn how to perform a multi-disciplinary design Optimization study. The disciplines used in this tutorial are structural performance and cost.

Before you begin, copy the model files used in this tutorial from <hst.zip>/HS-4215/ to your working directory.
Structural performance is simulated using OptiStruct, and Cost is simulated using Compose or Python. Optimization parameters for both the simulations are identified in template files corresponding to each input deck:
tail.fem
OptiStruct
tail.oml
Compose
tail.py
Python
It is assumed that the tail is cantilevered about its inboard section. Three loading scenarios are considered; one where the tail experiences pressure loads of 0.25 psi on the bottom skin, a second where the tail experiences a tip load of 400 lbs, and a third where the tail experiences both the pressure load and tip load simultaneously. The applied loading is represented in Figure 2.
Problem Formulation for this study is as follows:
Input variables
Glass fabric thickness at inboards; initial value = 0.1; lower bound = 0.01, upper bound = 2.0
Glass fabric thickness at midspan; initial value = 0.1; lower bound = 0.01, upper bound = 2.0
Glass fabric thickness at outboards; initial value = 0.1; lower bound = 0.01, upper bound = 2.0
Core thickness at inboards; initial value = 0.1; lower bound = 0.01, upper bound = 2.0
Core fabric thickness at midspan; initial value = 0.1; lower bound = 0.01, upper bound = 2.0
Core fabric thickness at outboards; initial value = 0.1; lower bound = 0.01, upper bound = 2.0
Aluminum rib thickness; initial value = 0.1; lower bound = 0.01, upper bound = 2.0
Note: Both models have seven input variables; however values of the input variables need to be consistent between the two models. In order to obtain this, we will be linking the two sets of input variables to each other.
Objective
Minimize the cost
Design constraints
Maximum displacement must be less than its baseline value of 31

## Perform the Study Setup

1. Start a new study in the following ways:
• From the menu bar, click File > New.
• On the ribbon, click .
2. In the Add Study dialog, enter a study name, select a location for the study, and click OK.
3. Go to the Define Models step.
4. Add a Parameterized File model.
1. From the Directory, drag-and-drop the tail_structure_optistruct.tpl file into the work area.
2. In the Solver Input File column, enter tail.fem.
This is the name of the solver input file HyperStudy writes during the evaluation.
3. In the Solver Execution Script column, select OptiStruct (os).
5. Add a second Parameterized File model.
1. From the Directory, drag-and-drop the appropriate .tpl file into the work area.
• If you are using Python, use tail_cost_python.tpl.
• If you are using Compose, use tail_cost_compose.tpl.
2. In the Solver Input File column, enter a name for the solver input file HyperStudy writes during any evaluation.
• If you are using Python, enter tail.py.
• If you are using Compose, enter tail.oml.
3. In the Solver Execution Script column, select either:
• Python (py)
• Compose (oml)
4. If you are using Compose as the solver execution script, in the Solver Input Arguments column, enter -f before \$file.
Note: If you are using Compose as part the HyperWorks suite, than HyperStudy should automatically point to the correct .bat file. If you have Compose as a separate installation, than during the Register Solver Script step you must point to Compose_batch.bat.
6. Click Import Variables.
Fourteen input variables are imported from the two .tpl resource files.
7. Go to the Define Input Variables step.
8. Review the input variable's lower and upper bound ranges.
2. In the Varname column, copy all of the independent variables (all variables from Model_1).
3. In the Expression column of all of the dependent input variables (all variables from Model_2), paste the independent variables.

## Perform Nominal Run

1. Go to the Test Models step.
2. Click Run Definition.
An approaches/setup_1-def/ directory is created inside the study directory. The approaches/setup_1-def/run__00001/m_1 and approaches/setup_1-def/run__00001/m_2 sub-directories contain the tail.h3d (for maximum displacement) and cost.res (for cost) files, which are the result of the nominal run, and will be used in the optimization.

## Create and Evaluate Output Responses

In this step you will create two output responses: MaxDisp and Cost.

1. Go to the Define Output Responses step.
2. Create the MaxDisp output response.
1. From the Directory, drag-and-drop the tail.h3d file, located in approaches/setup_1-def/run__00001/m_1, into the work area.
2. In the File Assistant dialog, set the Reading technology to Altair® HyperWorks® and click Next.
3. Select Multiple Items at Multiple Time Steps, then click Next.
4. Define the following options and click Next.
• Subcase: Subcase 5 (Combo)
• Type: Displacement (Grids)
• Request - Start: Select First Request and enter N4660
• Request - End: Select Last Request and enter N7528
• Component: Mag.
5. Select the Create individual Responses (1) checkbox, and then select Maximum.
6. Click Finish.
The output response is added to the work area.
7. In the work area, Label column, change the label to MaxDisp.
3. Create the Cost output response.
1. From the Directory, drag-and-drop the cost.res file, located in approaches/setup_1-def/run__00001/m_1, into the work area.
2. In the File Assistant dialog, set the Reading technology to Altair® HyperWorks® and click Next.
3. Select Single item in a time series, then click Next.
4. Define the following options and click Next.
• Type: Unknown.
• Request: Block 1.
• Component: Column 1.
5. Label the output response Cost.
6. Set Expression to First Element.
7. Click Finish.
The Cost output response is added to the work area.
4. Click Evaluate to extract the response values.

## Run Optimization

1. In the Explorer, right-click and select Add from the context menu.
2. In the Add dialog, select Optimization and click OK.
2. Go to the Optimization > Definition > Define Output Responses step.
3. Click the Objectives/Constraints - Goals tab.
4. Apply an objective on the Cost output response.
2. In the Apply On column, select Cost.
3. In the Type column, select Minimize.
5. Apply a constraint to the MaxDisp output response.
2. In the Apply On column, select MaxDisp.
3. In the Type column, select Constraint.
4. deterministic
5. In column 1, select <= (less than or equal to).
6. In column 2, enter 31.
6. Go to the Optimization > Specifications step.
7. In the work area, set the Mode to Adaptive Response Surface Method (ARSM).
Note: Only the methods that are valid for the problem formulation are enabled.
8. Click Apply.
9. Go to the Optimization > Evaluate step.