MV-3000: DOE using MotionView - HyperStudy

1. Start a new MotionView session.
2. Click the Open Model icon, , on the Model-Main toolbar.

Or, from the menu bar, select File > Open > Model.

3. Select the file model hs.mdl, located in your <working directory>, and click Open.
4. Review the model and the toe-curve output request under Static Ride Analysis.
5. From the Applications menu, select HyperStudy.
   HyperStudy is launched. The message "

   Establishing connection between MotionView and HyperStudy

   " is displayed.
6. Start new study using one of the following ways:
   • From the Welcome page, click the New Study icon, .
   • From the toolbar, click the New Study icon, .
   • From main menu, select File > New. The HyperStudy - Add Study dialog is displayed.

   
   
   Figure 3.

7. Accept the default label and variable names.
   1. Under Location, click the file browser and select <working directory>\.
   2. Click OK.
8. Model definition.
   1. From the study Setup tree, select Define models.
   2. Click to open the Add Model dialog.
   3. Under Type, select MotionView to add a MotionView model to the study.
   4. Accept the default variable name.
   5. Click OK.
      The following table with model data is created.

      
      
      Figure 4.

9. Model data.
   Please note that following details are automatically filled when you define the model (previous step).
   1. Under Active, check the box to activate or deactivate the model from study.
   2. The label of model entered in previous step.
   3. The variable name of model entered in the previous step.
   4. The model type selected in previous step.
   5. Point to the source file (here model file is sourced from MotionView through the MotionView – HyperStudy interfacing).
   6. Enter a name for the solver input file with the proper extension (for MotionSolve .xml) and select the solver execution script MotionSolve - standalone ( ms).
10. Create design variables.
    1. Click Import Variables to specify the design variables for the study.
    2. The Model Browser window opens in MotionView, allowing you to select the variables interactively.
    3. Select the following from the Browser using the Model Parameter Tree dialog:

       System	Point	Coordinate	Function
       Front SLA susp.	Otr tie-rod ball-jt -left	Y	Double-click or Click Add
       Front SLA susp.	Otr tie-rod ball-jt –left	Z	Double-click or Click Add
       Parallel Steering	Inr tie-rod ball - left	Y	Double-click or Click Add
       Parallel Steering	Inr tie-rod ball - left	Z	Double-click or Click Add

       
       
       Figure 5. Model Parameter Tree dialog

    4. Click Done.
    5. Click Next to go to Define Design Variables.
11. Define design variables.
    1. From the Define design variables tab, edit the upper and lower bounds of the design variables according to the following table.

       Point	Coordinate	Lower	Upper
       Outer tie-rod ball-jt -left	Y	-571.15	-559.15
       Outer tie-rod ball-jt - left	Z	246.92	250.92
       Inner tie-rod ball - left	Y	-221.9	-209.9
       Inner tie-rod ball - left	Z	274.86	278.86

       
       
       Figure 6.

    2. This step also includes definition of other properties to the design variables. The options Details and Distributions specify variations of design variables in the range specified. The option Link Variables is used to link different design variables through a mathematical expression.
    3. Click on each tab to observe these options.
    4. Right click on the column header row to view more options that you may want to add.

       
       
       Figure 7.

    5. Click Next to go to Specifications.
12. Specifications.
    This section allows you to specify the initial run for DOE.
    1. Select the Nominal Run radio button for this study and click the Apply button.
    2. Click Next to go to Evaluate.
13. Evaluate
    1. Click Evaluate Tasks to perform the nominal run.
    2. Make sure that all settings for the run (Write, Execute and Extract) are activated.
    3. MotionSolve runs in the background and the analysis is carried out for the base configuration. Please note the messages in status bar of the HyperStudy interface and the MotionView interface. If message log is not visible, click the Messages button or go to View > Messages to display the log.
    4. Once the nominal run is complete, click Next to go to Define responses.
14. Define Response.
    1. Click Add Response to add a new response.
    2. Label the response Sum of Squared Error.
    3. Accept the variable name and click OK.

       
       
       Figure 8. Response table data

    4. Click the ellipses, …, in the Expression cell of Response table to launch the Expression Builder.

       
       
       Figure 9. Expression builder

       Note: You can move the cursor over the function to display the function help. For this exercise, the response function requires two Data Sources:

       • The elements of Data Source 1 contain actual data points of the toe curve from the solver run for the nominal configuration.
       • The elements of Data Source 2 contain data points from the target curve.
    5. Click the Data Sources tab to source the data from the files.
15. Data Source 1
    1. Click to add a new data source using the solver output file.

       
       
       Figure 10. Response data table

    2. Click the ellipses, …, in the File cell of Data Source table data to launch the Data Source Builder: Data Source 1 (ds_1) dialog box.
    3. Click the file browser button, , and select the file m_1.mrf from <working directory>\approaches\nom_1\run__00001\m_1\.
    4. This enables the Type, Request and Component fields.
    5. From the Type drop-down menu, select Expressions.
    6. From the Request drop-down menu, select REQ/70000033 toe-curve.
    7. From the Component drop-down menu, select F2.

       
       
       Figure 11. Data Source 1 dialog box

       You have now selected the toe curve data from the solver run as the data elements for Data Source 1.

    8. Click OK.
16. Data Source 2
    1. Create a Data Source to hold the data elements from the target toe curve.
    2. Click Add Data Source to add a new data source using a reference file.

       
       
       Figure 12. Response data table

    3. Click the … in the File cell of the Data Source 2 table data to launch the Data Source Builder: Data Source 2 (ds_2) dialog box.
    4. Click the file browser button, , and select the file target_toe.csv, located in your <working directory>\.
       • Set Type to Unknown and Request to Block 1.
       • From the Component drop-down menu, select Column 1.
       • Click OK.
17. In the Expression field, create the following expression:

    sum((ds_1-ds_2))^2)

    This expression evaluates the sum of the square of the difference between the “actual toe change” values (from solver run) and the “targeted toe curve” (from imported file). In the next tutorial, MV-3010, we will use HyperStudy to minimize the value of this expression to get the required suspension configuration.

    
    
    Figure 13.

18. Click Evaluate expression to verify that the expression is evaluated correctly. You should get a value of 16.289.
19. Click OK to close the Expression builder and hit the Evaluate button.
    1. If you do not encounter any error messages and were able to successfully extract the response for the nominal run, click Next to go to Post Processing.
    2. Observe the table with the design variable values used for the nominal run and other tabs with the post-processing options.
    3. Click Next to go to Report.
    4. Observe various reporting formats available. The images and data captured during the post-processing can be exported in any of the formats provided on Report page.
20. From the File menu, select Save As….
21. Save this study set-up as Setup.hstudy to your <working directory>\.

1. Adding new DOE study.
   1. Right-click in the Explorer browser area and from the context menu, click Add to display the Add Approach dialog.

      
      
      Figure 14.

      Or

   2. From the Toolbar, click the Add Approach button to display the HyperStudy - Add dialog.

      
      
      Figure 15.

   3. Under Select Type, select Doe.
   4. Accept the default label and variable name and click OK.
      The DOE study tree is displayed in the Browser with name Doe 1.
   5. Click Next to go to Select design variables.
2. Select design variables for the DOE study.
   1. All variables are used in the present DOE study, so make sure that all design variables are active.
   2. All the design variables in this study are controlled. Therefore, for Category, leave all variables set to Controlled.

      
      
      Figure 16.

   3. Click Next to go to Select Output responses.
3. Select responses for the DOE study:
   1. There is only one response in the present study - make sure to select the response.
   2. Click Next to go to Specifications.
4. Specifications for the DOE study:
   The design space for the DOE study is created in this step. The present study has four design variables with two levels each. A full factorial will give 24 = 16 experiments, as the number of experiments are less. We will do a full factorial run.
   1. Click Show more... and select Full Factorial mode, .

      
      
      Figure 17.

      Note: Selecting any mode from the list shows all possible options in the Parameters panel area on the left side of GUI.
   2. Click the Levels tab to see the design variables and number of levels.

      
      
      Figure 18.

   3. Click the Interaction tab to observe that it is not applicable since all interactions are selected by default for a full factorial run.

      
      
      Figure 19.

      Note: Options which are not applicable will be grayed out or a message will be shown.
5. Click Apply to generate the design space.
6. Click Next to go to Evaluate.
7. DOE run:

   The Tasks tab of Evaluate shows a table of 16 rows and four columns. Column 1 shows the experiment number while other columns corresponding to each experiment get updated with the experiment status of failure or success in the three stages of model execution: Write, Execute and Extract.

   Design variable values used under each experiment can be seen under the Evaluation Data tab.

   The last column corresponds to the response value from each run. The values gets populated once the run is completed.

   1. Click Evaluate Tasks to start the DOE study.

      
      
      Figure 20.

      Once all the runs are finished, the tasks table gets filled up with the status for each run (Success/Fail).

   2. In the present DOE study, all runs are successfully completed. Click Next to go to Post Processing.
8. Viewing Main Effect and Interaction plots:
   The post-processing section has variety of utilities to helps user to effectively post process results. Summary tab of Post processing page will provide a summary of design along with responses.

   The New Generation HyperStudy allows you to sort data by right-clicking on the column heading and selecting the options from context menu.

   The options to post-process are available in other tabs. The main effects can be plotted by selecting the Linear Effects tab.

9. Main Effects
   1. Click the Linear Effects tab to open the main effects plot window. From the Channel page, select Variables and Responses for which main effects need to be plotted. Press the left mouse button and move over the variable or responses list for multiple selection.
   2. Select all controlled variables and responses to plot the main effect plot. This plot shows the effect of each parameter on the response.

      
      
      Figure 21. DOE – Main effects plot

      Note: Click on window icon, , (highlighted above) to toggle it to multiple windows, . Each curve is displayed in a different plot window.
10. Interactions
    An interaction is the failure of one variable to produce the same effect on the output response at different levels of another input variable. In other words, the strength or the sign (direction) of an effect is different depending on the value (level) of some other variable(s). An interaction can be either positive or negative.

    Interactions can be plotted from the Interactions tab following the above procedure.

    1. Select the design variables Otr_tierod_ball_jt-left-z and Inr_tierod_ball_jt-left-z and response Sum of Squared Error.
    2. Click the Interaction tab to observe that it is not applicable since all interactions are selected by default for a full factorial run.
       This displays the interaction plot for these two variables only.

       
       
       Figure 22. Controlled design variable plot for “Otr_tierod_ball_jt-left-z” & “Inr_tierod_ball_jt-left-z” interaction

1. Adding an approximation.
   1. Right-click in the Explorer browser area and from the context menu, click Add to display the Add Approach dialog.

      
      
      Figure 23.

   2. Under Select Type, select Fit.
   3. Accept the default label and variable names and click OK.
      A new tree with the name Fit 1 is created in the Browser.
   4. Click Next.
2. Input matrix.
   1. Click .
      A matrix table is created. Check if the following options to specify the DOE results as the input matrix have been selected by default.
   2. Under Type, use the drop-down menu to select Input.
   3. For Matrix Source, select Doe 1 from the drop-down menu.
      In the present study, we are not using any validation matrix. So, no matrix will be added for validation matrix.

      
      
      Figure 24.

      Observe that the status shows “Import pending”.

   4. Click Import Matrix to import the DOE results for the input matrix.
   5. Click Next to go to Select Input Variables.
   6. Select all design variables and click Next to go to Select Output Responses.
   7. Select the response and click Next to go to Specifications.
      In this section, the approximation type and it’s properties are defined.
   8. Click the Show more… button and select the Moving Least Squares (MLSM):

      
      
      Figure 25.

   9. Click Apply to apply the approximation method.
   10. Click Next to go to Evaluate.
   11. Observe an empty Tasks table which corresponds to the DOE experiments.
   12. Click Evaluate Tasks to evaluate the approximation for the DOE experiments.
       Upon completion, the table is populated with the status value (Success or Fail).
   13. Click the other tabs available to observe the fit.
   14. Click the Evaluation Data tab to observe the experiment table with responses from the MotionSolve run and responses predicted using approximation. The same can be viewed in graph format by selecting the Evaluation plot tab.
   15. Select Sum of Squared of Squared Error and Squared of Squared Error_MLSM to plot against the experiment numbers.

       
       
       Figure 26.

       This fit shows a good approximation to the response.

   16. Click Next to go to Post Processing.
       Post-processing provides you with statistical parameters and graphical tools useful in validating the correctness of approximation.

       The Residuals tab shows the difference between the response value from the solver and the response value from the regression equation.

       The residual values can be used to determine which runs are generating more errors in the regression model.

       The Trade-off tab shows the plots of the main effects vs. response from the approximation.

       
       
       Figure 27. Trade-off: 3-D plots

   17. Select Otr_tierod_ball_jt-left-y as the X axis and Otr_tierod_ball_jt-left-z as the Y axis to plot against response from the approximation.

       
       
       Figure 28.

   18. From the toolbar, click the Save icon, , to save the study.
       Note: All study files will be saved in the study directory with the folder names that are the same as the tree varnames. For example, nom_1, doe_1, and fit_1.