Results

LS-OPT Viewer

Start the LS-OPT Viewer by selecting the View tab and

1. Select under Metamodel the item Accuracy
2. From Entity select Responses → HIC.
3. We can find RMS Error and R² (R-sq) above the plot.

→ The coefficient of determination (R²) is high (=0.922), but the RMS Error may need further improvement, e.g. more iterations or selection of a more suitable metamodel (e.g. Feedforward Neural Network or Radial Basis Function Network). Nevertheless for a raw estimate the result may be considered as acceptable.

lsopt_output File

1. Open the lsopt_output file with a text editor.
2. Search in the file for "Approximating".
3. Find the appropriate values for root mean square error (RMS Error) and R².

Mean Response Value

RMS Error

R²

MASS

0.7742

0

1

Disp1

-159.3846

2.0286 (1.27%)

0.4790

Disp2

-694.5753

5.5459 (0.80%)

0.9896

HIC

275.72

76.5620 (27.77%)

0.9224

LS-OPT Viewer

    ANOVA

1. Restart the LS-OPT Viewer and select under Metamodel the item Sensitivity
2. Select  Linear ANOVA in the new window.
3. From Response select HIC.
4. Sort the variables according to their significance for HIC

→ For the response HIC the variable thood is more important, while the variable tbumper is rather insignificant.

    GSA/Sobol

1. Select GSA/Sobol.
2. From Response select HIC.
3. Sort the variables according to their significance for HIC.

→ We come to the same conclusion by using GSA. The influence of thood on HIC is 84.9%, larger than that of tbumper (15.1%).

lsopt_output File

1. Open the lsopt_output file with a text editor.
2. Search in the file for "Ranking".
3. Find out the importance of each of the variables for the response.

LS-OPT Viewer

1. Restart the LS-OPT Viewer and select under Optimization the item History.
2. From the left side of the window select Responses → HIC.
3. For Value to Plot select Value.
4. If you want to see the accurate values, simply click nearby the red points. There appears a table, which states all the computed and predicted data, e.g. computed HIC vs. predicted HIC.

→ You can find out the appropriate values for the predicted (black) and the computed (red points) results. More iterations may lead to a better predictive capability.

(Note the range of the values when assessing the accuracy)

lsopt_output File

1. Open the lsopt_output file with a text editor.
2. Search in the file for "Evaluating Starting Design" and scroll a bit more forward.
3. You may find both the initial values of HIC, computed vs. predicted.
4. Search now in the file for "F I N A L".
5. The final values of HIC (computed and predicted) after one iteration are available.