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Point MSE

A composite MSE (Mean Square Error) function is used in order to apply parameter identification with simple target values.

Solution with LS-OPTui

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Solution with LS-OPTui

Solvers

Solvers

Open LS-OPTui.

Solvers Panel

  1. Select the Solvers panel.
  2. For Command select your LS-DYNA executable
  3. For Input File browse the file foam1.k in your directory.
  4. Enter the Name of Analysis Case, e.g. Case1.
  5. Push the Add button to create a new analysis case.
solver1.png

 

Variables

Variables

Define the Variables

  1. Select the Variables panel.
  2. Switch the YMod Type from Constant to Variable.
  3. Enter 500000 for the Minimum.
  4. Enter 2000000 for the Maximum.

 

 var_ymod1.png
  1. Switch the Yield Type from Constant to Variable.
  2. Enter 500 for the Minimum.
  3. Enter 2000 for the Maximum.

 

 var_yield1.png

Histories

Histories

Extract History

  1. Select the Histories panel.
  2. From the possible histories select RCFORC.
  3. Enter 1 for the Interface ID.
  4. Select Z slave force.
  5. Enter the label Force as History Name.
  6. Push the Add button to create the new history.

This will extract the Z-slave history Force from the binary output database RCFORC.

history1.png

Responses

Responses

Define the Responses

  1. Select the Responses panel.
  2. From the possible response types select: Response-Expression.
  3. Enter an algebraic expression: Force(0.002).
  4. Type in the label F1_1 for Response Name.
  5. Push the Add button to create a new response.
resp_f11.png
  1. From the possible response types select: Response-Expression.
  2. Enter an algebraic expression: Force(0.004).
  3. Type in the label F2_1 for Response Name.
  4. Push the Add button to create a new response.
resp_f22.png
  1. From the possible response types select: Response-Expression.
  2. Enter an algebraic expression: Force(0.006).
  3. Type in the label F3_1 for Response Name.
  4. Push the Add button to create a new response.
resp_f33.png
  1. From the possible response types select: Response-Expression.
  2. Enter an algebraic expression: Force(0.008).
  3. Type in the label F4_1 for Response Name.
  4. Push the Add button to create a new response.
resp_f44.png
  1. From the possible response types select: Composite.
  2. Select the Response F1_1.
  3. For Target type in: 10000.
  4. Select the Response F2_1.
  5. For Target type in: 13000.
  6. Select the Response F3_1.
  7. For Target type in: 15000.
  8. Select the Response F4_1.
  9. For Target type in: 17000.
  10. For Response Name enter: MSE.
  11. Push the Add button to create a new response.
resp_mse2.png

Objective

Objective

Objective

  1. Select the Objective panel.
  2. Select MSE from the Responses as the objective function.
  3. Leave the default 1.0 for Weight.
objective1.png

Run

Run

Run the Optimization

  1. Select the Run panel.
  2. For Number of iterations enter 5.
  3. Push the Run button to start the optimization. (Save the project as com.mse_point)
run1.png

Com-file

Com-file

The created command file may look like this:

"parameter identification"
Author "jim brown"
$ Created on Thu Jan 10 12:59:20 2008
solvers 1
responses 4
histories 1
$
$ DESIGN VARIABLES
$
variables 2
 Variable 'YMod' 700000
  Lower bound variable 'YMod' 500000
  Upper bound variable 'YMod' 2e+06
 Variable 'Yield' 1500
  Lower bound variable 'Yield' 500
  Upper bound variable 'Yield' 2000

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
$      OPTIMIZATION METHOD   
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
$
Optimization Method SRSM

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
$      SOLVER "1"
$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
$
$ DEFINITION OF SOLVER "1"
$
 solver dyna960 '1'
  solver command "/home/prak1/LS-DYNA/ls971_s_7600_1224_ia32_redhat90"
  solver input file "/home/prak1/LS-OPT/beispiele/parameter_identification/foam1.k"
  solver check output on 
  solver compress d3plot off 
$ ------ Pre-processor --------
$   NO PREPROCESSOR SPECIFIED
$ ------ Metamodeling ---------
  solver order linear
  solver experiment design dopt
$ ------ Job information ------
  solver concurrent jobs 1
$
$ WARNING - NO RESPONSES DEFINED FOR SOLVER "1"
$
$
$ HISTORIES FOR SOLVER "1"
$
 history 'Force' "BinoutHistory -res_type RCForc -cmp z_force  -id 1  -side SLAVE"
$
$ RESPONSE EXPRESSIONS FOR SOLVER "1"
$
 response 'F1' expression {Force(0.002)}
 response 'F2' expression {Force(0.004)}
 response 'F3' expression {Force(0.006)}
 response 'F4' expression {Force(0.008)}

composites 1
$
$ COMPOSITE RESPONSES
$
 composite 'MSE' type standardMSE
  composite 'MSE' response 'F1' 10000
  composite 'MSE' response 'F2' 13000
  composite 'MSE' response 'F3' 15000
  composite 'MSE' response 'F4' 17000
$
$ OBJECTIVE FUNCTIONS
$
 objectives 1
 objective 'MSE' 1
$
$ THERE ARE NO CONSTRAINTS!!!
$
 constraints 0
$
$ JOB INFO
$
 concurrent jobs 1
 iterate param design 0.01
 iterate param objective 0.01
 iterate param stoppingtype or
 iterate 5
STOP

Results

Document Actions

Results

Accuracy

Accuracy

Metamodel accuracy of all points for response F1

Start the LS-OPT Viewer.

  1. Select Accuracy from Type of Plot.
  2. Choose the Response F1.
  3. Switch the button to All to use all the points from every iteration.

 

→ Good approximation: low distance between predicted (black line) and computed (red points) values.

accuracy_f11.png

Computed vs Predicted in Opt History plot:

  1. Select Opt History for the Type of Plot.
  2. Choose the Response F1.

 

 

→ Good approximation of optima.

opthistory_f11.png

Convergence

Convergence

Convergence of the Variable YMod

  1. Select Opt History for Type of Plot.
  2. Select the Variable YMod.

 

→ The value of the variable (red line) changes and with every iteration the range of the variable is getting smaller (blue lines). The variable is rather insignificant (see ANOVA) and therefore isn't converged.

convergence_ymod1.png

Convergence of the Variable Yield

  1. Select the Variable Yield.

 

→ Here you can see the value (red line) and the range (blue lines) of the variable Yield. The variable is important for the optimization (see ANOVA) and appears to be converged.

 

convergence_yield1.png

Confidence Intervals

Confidence Intervals

Confidence Intervals

The Confidence Intervals can be found in the lsopt_report file.

  1. From the main menu bar select View → Summary.

 

The Confidence Intervals are located at the end of the file.

 

view_summary1.png

You can also open the lsopt_report file from the main directory (search for 'Confidence') to find the Confidence Intervals of YMod and Yield. In both cases you will find this output file.

lsopt_report-file1.png

ANOVA

ANOVA

ANOVA Results

The ANOVA (analysis of variance) result can be displayed as follows in the LS-OPT Viewer:

  1. Select ANOVA for Type of Plot.

  2. Choose the response F1.

 

anova_f11.png

To view an ANOVA result for another response:

  1. Stay in the ANOVA Type of Plot.

  2. Choose the response F2.

 

→ As we can see for these two responses the variable YMod is rather insignificant and the variable Yield is important.

anova_f22.png

Download

Download

The complete data set (input and command files) is available for download as tar.gz point_MSE.tar.gz or zip-file point_MSE.zip.