HS-1620: Set Up a Flux Model

Learn how to set up a Flux model in HyperStudy that will investigate the relationship between actuator dimensions and the mechanical force output.

Before you begin:
  • Copy the model files used in this tutorial from <hst.zip>/HS-1620/ to your working directory.

  • Setup Flux to work with HyperStudy. For more information, refer to the registrations steps in Flux Model.

An electromagnetic actuator is a device that converts an electric current into a mechanical output. The actuator is composed of an U magnetic core, mobile magnetic part, and two coils supplied by an amp-turn number. The finite element model is created and analyzed by Flux. The Flux model also contains the input variables and output responses of interest.


Figure 1. Electromagnetic Actuator and its Finite Element Model


Figure 2. Input Variables

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 a Flux model by dragging-and-dropping the Example.F2HST from the Directory into the work area.
    The Resource, Solver input file, and Solver input arguments fields become populated.


    Figure 3.
  6. Click Import Variables.
    Ten input variables are imported from the Example.F2HST file.
  7. Go to the Define Input Variables step.
  8. Review the input variables.


    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

Go to the Define Output Responses step.
Responses are automatically recovered.


Figure 5.

Run a Hammersley Stochastic Study

  1. Add a Stochastic.
    1. In the Explorer, right-click and select Add from the context menu.
    2. In the Add dialog, select Stochastic and click OK.
  2. Go to the Stochastic 1 > Definition > Define Input Variables step.
  3. Modify input variables.
    1. Clear the Active checkbox for GAP.


      Figure 6.
    2. For each input variable, adjust the lower and upper bounds.
      The lower and upper bounds will be used to calculate statistical distribution settings, in this case the variance of normal distribution.
    3. In the Nominal column, click ....
      A pop-up window opens.
    4. In the Percent field, enter 1 and click +/-.


      Figure 7.
    5. Click OK.
  4. Go to the Stochastic > Specifications step.
  5. In the work area, set the Mode to Hammersley.
  6. In the Settings tab, change the Number of runs to 100.
  7. Click Apply.
  8. Go to the Stochastic > Evaluate step.
  9. Click Evaluate Tasks.
    Tip: Increase the value of Multi-Execution to reduce the runtime. The number of concurrent jobs depends on the available computational resource. For instance, on a laptop with a Win64 operating system, 32 GB ram, Intel Core i7 CPU, and Multi-Execution set to 4, it takes about 20 minutes to run 100 simulations.
  10. Go to the Stochastic > Post-Processing step.
  11. Plot correlation values.
    1. Click the Scatter tab.
    2. Click the Correlation tab and select correlation coefficients.

      Correlation measures the strength and direction between associated variables. Correlation coefficients can have a value from -1 to 1; -1 indicates a strong but negative correlation and 1 indicates a strong and positive correlation.

      The correlation values for variables AT, DEPTH, and TCORE with Force are 0.80, 0.43, and 0.39, which indicates that Force is correlated to AT and Force is somewhat correlated to DEPTH and TCORE. These three correlations are positive, meaning that you should expect to see an increase in Force corresponding to an increases in AT, DEPTH, and TCORE. You can also expect to see no changes in Force corresponding to changes in other variables. DEPTH and TCORE are somewhat correlated to FORCE, therefore you may see deviations from these predicted behaviors.


      Figure 8.
    3. Visualize these correlations in the Scatter plot.
      In the plot for FORCE vs AT, you can see the design cloud follows a nice pattern of increasing Force with increasing AT. In the plot for WCOIL vs FORCE, you should not observe any relationship between the two.


      Figure 9.