Optimising the Bandpass Filter with HyperStudy Using a POSTFEKO Session
Configure the bandpassfilter.pfs file with the S11 data to minimise the reflection coefficient in HyperStudy.
- Open CADFEKO.
- Click Application Macro under the Scripting group.
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In the drop-down list, select Macro
Library and run the EG I3: Bandpass
Filter script.
- Save the model with the name bandpassfilter.cfx and execute the solver.
- Open the bandpassfilter.cfx model in POSTFEKO.
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Create a Cartesian graph and add the
S11 trace from the SParamOpt
request.
- Save the bandpassfilter.pfs file in the same location as the bandpassfilter.cfx.
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Click Application macro in the
Scripting group.
The scripts loaded in the Macro library is displayed.
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Click Utility and select Optimise model in
HyperStudy.
The following Create HyperStudy Session dialog is shown.
- In the Study label field, enter a value for the name of the study.
- In the Study folder field, specify a value for the directory of the HyperStudy session.
- In the Installation directory field, specify the directory where HyperStudy is installed.
- [Optional] Set the FEKO_HST_INSTALLATION_DIR environment variable to use a specific version of HyperStudy.
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Click OK to start to create a HyperStudy session.
The following Create HyperStudy Session dialog is shown.
- Click OK to launch HyperStudy.
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Under Define models, verify the Solver
Execution Script field is set to use the correct version.
Note:
- The script is accessible under Edit > Register Solver Script, which offers the possibility to register another solver or version.
- The argument -np can be typed in Solver Input Arguments to specify the number of cores to use.
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Under Define Input Variables, select which variables to
include in the study. Only S1 – S3 should be activated and the default ranges
used.
An example of the selected variables is displayed.
- Click Next.
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Click Run Definition.
During the Write phase, the bandpassfilter.cfx_extract.lua file was copied to the run directory and executed after the Feko solver was run in the Extract phase. This generated an output file that HyperStudy can process easily.Note: The script bandpassfilter.cfx_extract.lua is different if a .pfs file was present before importing the variables and will automatically extract the visible traces on a Cartesian graph and polar graph.See Figure 5 for the completed definition run for the Write, Execute and Extract phases for the initial test run done by HyperStudy.
- Click Next.
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Select Add Output Response.
An output response is added with the Expression field highlighted.
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Select the Expression field.
The Expression Builder:Response1(r1) dialog is shown.
- In the Expression field, enter max(m1_ds_1) and click OK.
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Under Goals click to add an optimisation goal.
The following dialog is displayed.
- Set the goal Type to Minimize and click OK.
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Click Evaluate to extract the value from the output
file.
HyperStudy is now configured to understand which model to use, which variables are available for modification and how to process the output.
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Right-click on the defined study in the Explorer tab and
click Add.
- Under Select Type, select Optimization and click OK.
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In the Definition from:
drop-down list, select Setup and click OK
The optimisation approach is created in the Explorer tab
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Click Optimization 1 > Specifications, and select the Adaptive Response Surface
Method as the optimiser.
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Click Apply and click Next.
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Click Evaluate Tasks.
Each of the input variables is altered randomly, and its effect on the response analysed.
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Click Iteration History tab and look for the row
highlighted in green.
The optimum values are as follows:
- S1 = 0.4500000
- S2 = 1.9254786
- S3 = 2.2000000