In this tutorial, you will:

Theory

HyperStudy allows you to perform Design of Experiments (DOE), optimization, and stochastic studies in a CAE environment.  The objective of a DOE, or Design of Experiments, study is to understand how changes to the parameters (design variables) of a model influence its performance (response).

After a DOE study is complete, approximation can be created from the results of the DOE study.  The approximation is in the form of a polynomial equation of an output as a function of all input variables. This is called as the regression equation.

The regression equation can then be used to perform Optimization.

HyperStudy can be used to study different aspects of a design under various conditions, including non-linear behavior.

HyperStudy also does the following:

Tools

In MotionView, HyperStudy can be accessed from

You can then select MDL property data as design variables in a DOE or an optimization exercise. Solver scripts registered in the MotionView Preferences file are available through the HyperStudy interface to conduct sequential solver runs for DOE or optimization.

For any study, the HyperStudy process is shown below:

The HyperStudy process

MotionView MDL files can be directly loaded into HyperStudy. Any solver input file, such as ADAMS, MotionSolve, OptiStruct, Nastran, or Abaqus, can be parameterized and the template file submitted as input for HyperStudy. The parameterized file identifies the design variables to be changed during DOE, optimization, or stochastic studies. The solver runs are carried out accordingly and the results are then post-processed within HyperStudy.

Copy the files hs.mdl and target_toe.csv, located in the mbd_modeling\doe folder, to your <working directory>.

In the following steps, you will create a study to carry out subsequent DOE study on a front SLA suspension model.

While performing a Static Ride Analysis, you will determine the effects of varying the coordinate positions of the origin points of the inner and outer tie-rod joints on the toe-curve.

Step 1: Study Set-up.

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From the menu bar, select File > Open > Model.

HyperStudy is launched.  The message "Establishing connection between MotionView and Hyperstudy" is displayed.

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Accept the default label and variable names.

Under Location, click the file browser and select <working directory>\.

Please note that following details are automatically filled when you define the model (previous step).

Enter a name for the solver input file with the proper extension (for Motionsolve ->.xml) and select the solver execution script MotionSolve - standalone ( ms ).

Model Parameter Tree dialog

- Click Next to go to Specifications.

This section allows you to specify the initial run for DOE.

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 Message log button, , or go to View > Message log to display the log.

Response table data

Expression builder

Note: You can move the cursor over the function to display the function help.

For this exercise, the response function requires two vectors:

Vector 1:

Response data table

Vector 1 source dialog box

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

Expanded dialog box of Vector 1 source

Vector 2:

Create a vector to hold the data elements from the target toe curve.

Response data table

sum((v_1-v_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.

Step 2: DOE Study.

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The DOE study tree is displayed in the Browser with name Doe 1.

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.  Selecting any mode from the list shows all possible options in the Parameters panel area on the left side of GUI.

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.

DOE run:

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

7. Viewing Main Effect and Interaction plots:

The post-processing section has variety of utilities to helps user to effectively post process results. Run 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.

Main Effects:

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.

Interactions:

Interactions can be plotted from the Interactions tab following the above procedure.  Here, we will use the post-processing window to plot the interactions. Click Launch Post Processing to display the Post-processing window.

Display the Regressions in one of the following ways:

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By default, all interactions are displayed.

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

Step 3: Approximation.

System response is approximated by using various curve fitting methods.  An approximation for the response with the design variables variation is calculated using the data from above DOE study.  The accuracy of the approximation can be checked and improved.

1. Adding an approximation.

2. Input matrix.

A matrix table is created.  Select the following options to specify the DOE results as the input matrix.

In the present study, we are not using any validation matrix.  So, no matrix will be added for validation matrix.

Observe that the status shows “Import pending”.

In this section, the approximation type and it’s properties are defined.

Select Moving Least Squares (SMLS):

Upon completion, the table is populated with the status value (Success or Fail).

This fit shows a good approximation to the response.

Trade-off: 3-D plots