HS-1030: Parameterize a MotionView Model

Learn how to use HyperStudy to perform an optimization with MotionSolve.

Before you begin, copy the model files used in this tutorial from <hst.zip>/HS-1030/ to your working directory.
Input Variable
The input variable is the angle q (swing angle) of the pendulum.
Output Response
The output response target is to achieve Y-velocity of 6m/s at the tip of the pendulum.
Objective
At the end of this tutorial, you will know how to:
  • Use MotionView to start HyperStudy and create the input variables.
  • Set up a study.
  • Run a system identification optimization study.
Figure 1.


Perform the Study Setup

  1. Start HyperStudy.
  2. Start a new study in the following ways:
    • From the menu bar, click File > New.
    • On the ribbon, click .
  3. In the Add Study dialog, enter a study name, select a location for the study, and click OK.
  4. Go to the Define Models step.
  5. Add an MotionView model.
    1. Click Add Model.
    2. In the Add dialog, select MotionView and click OK.
  6. In the Resource column, browse and select the Pendulum.mdl file.
  7. In the Solver Input File column, enter m1.xml.
    This is the name of the solver input file HyperStudy writes for any evaluation.
  8. In the Solver Execution Script column, select MotionSolve.
    Figure 2.


  9. Click Import Variables.
    MotionView opens.
  10. In the Model Parameters dialog in MotionView, select parameters to import into HyperStudy.
    1. Expand SolverVariables > theta > value, and select lin.
      lin is the scalar value for the swing angle.
    2. Click Add.
    3. Click OK.
    Figure 3.


  11. Go to the Define Input Variables step.
  12. In the work area, modify the input variable's bounds.
    1. Change the Lower Bound to 0.
    2. Change the Upper Bound to 2.
    Figure 4.


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 directory contains the input file, which is the result of the nominal run.

Create and Evaluate Output Responses

In this step you will create one output response.

  1. Go to the Define Output Responses step.
  2. From the Directory, drag-and-drop the m1.mrf file, located in approaches/setup_1-def/run__00001/m_1, into the work area.
  3. In the File Assistant dialog, set the Reading technology to Altair® HyperWorks® (hgmrf.exe) and click Next.
  4. Select Single Item in a Time Series, then click Next.
  5. Define the following options, and then click Next.
    1. Set Type to Marker Velocity.
    2. Set Request to REQ/70000002 Pendulum body from Ground Body(tip velocity).
    3. Set Component to VY.
    Figure 5.


  6. Optional: Enter labels for the data source and output response.
  7. Set Expression to Maximum.
  8. Click Finish.
    Figure 6.


    The output response is displayed in the work area.
  9. Click Evaluate to extract the response values.

Run System Identification Optimization

  1. Add an Optimization.
    1. In the Explorer, right-click and select Add from the context menu.
    2. In the Add dialog, select Optimization.
    3. For Definition from, select Setup and click OK.
  2. Assign an objective to the output response.
    1. Go to the Optimization > Definition > Define Output Responses step.
    2. Click the Objectives/Constraints - Goals tab.
    3. Click Add Goal.
    4. In column Type, select More.
    5. In column 1, select System Identification.
    6. In column 2, change the target to 6.0.
    Figure 7.


  3. Go to the Specifications step.
  4. 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.
  5. Click Apply.
  6. Go to the Evaluate step.
  7. Click Evaluate Tasks to start the Optimization.
  8. View the iteration history of the Optimization in the following ways:
    Use the Channel selector to select input variables, output responses, goals, and so on to display.
    • Click the Iteration History tab to view a table with the Optimization's iteration results.
      Note: The optimal design is highlighted in green.
    • Click the Evaluation Plot tab to compare all of the entities of the Optimization (input variables, output responses, and objectives) against the iteration.
    Figure 8. Evaluation Plot


  9. Post-process the Optimization.
    The Post-Processing step in an optimization approach offers additional tools to review the results. Statistics, histograms, and scatter plots can be used to help compare and analyze designs.
    1. Go to the Post-Processing step.
    2. Click the Integrity tab to view a series of statistical measures on input variables and output responses.
      Figure 9.