Results

LS-OPT Viewer

1. Select the Viewer tab. The New plot window appear. Or restart the LS-OPT Viewer by clicking on the Restart viewer button.
2. Choose Scatter Plots as Type of Plot.

Variable "rail_l" vs. Response "max_intrusion_rail_l":

3. For the Z-axis select No entity to switch to the 2d mode.
4. For the X-axis select the Variable rail_l.
5. For the Y-axis select the Response max_intrusion_rail_l.

Feasible points are shown in green, infeasible points in red.

The Figure shows the correlation between a variable and a response.
We could conclude: the bigger the thickness of the variable rail_l (see highlighted in red in the figure below), the smaller is the maximal intrusion of the response max_intrusion_rail_l. 

The remaining dimensions, that could also have influence on the response, are projected into the plot.

Variable "hreinf3" vs. Response "IIHS_max_accel_engine_top":
 

For the X-axis select the Variable hreinf3 and for Y-axis select IIHS_max_accel_engine_top.

 Variable "w_wellO" vs. Response "max_torsion_angle":

For the X-axis select the Variable w_wellO and for Y-axis select max_torsion_angle.

Obviously we can say: the bigger the thickness of the variable w_wellO (see highlighted in red in the figure below), the smaller is the maximal torsion angle.

 Variable "w_wellO" vs. Response "torsional_stiffness":

For the X-axis select the Variable w_wellO and for Y-axis select tors_stiffness.

We defined torsional stiffness as x-moment / torsion angle. As expected the scatter plot shows: the thicker the w_wellO, the smaller the maximal torsion angle (scatter plot above), the greater is the torsional stiffness.

1. Restart the LS-OPT Viewer either in the LS-OPTui by clicking on the Restart viewer button or click on  new plot icon on the menu bar .
2. Select under Simulations the item Histories.

3. As x-coordinate select time.
4. Choose intrusion_rail_l as y-coordinate.
5. Set the variables rail_l  as c-coordinate.
   (C-coordinate affects the color of each curve dependend on the value of the c-coordinate.)

→ History curves of 60 simulations for intrusion_rail_l. The value of the variable rail_l determines the color of each curve.
There you can see, that the variable rail_l has a considerable influence on the response max_intrusion_rail_l.

Choose now accel_seat_average as y-coordinate and leave the variable rail_l as c-coordinate.

→The chaotic behaviour does not mean inevitable, that the variable does not have an influence on the response. It could be, that the influence of other variables leads to this behaviour.

Metamodel

1. Restart the LS-OPT Viewer either in the LS-OPTui by clicking on the Restart viewer button or click on  new plot icon on the menu bar .
2. Select under Metamodel the item Surface.

Set up the panels

3. Select the Setup panel.
4. Choose max_intrusion_rail_l as z-coordinate.
5. Set the variables rail_l and arm_top as x- and y-coordinate, respectively.
   (The sliders allow changing the variable values for unselected variables (variables not plotted). The slider for the active variables can be activated by selecting the “Predicted Value” option, a purple cross is displayed on the metamodel showing the values for the selection.)
6. Select Constraints.
   (Constraints are displayed on the surface.
   Feasible regions are in green, the shade of red shows the degree of infeasibility (number of violated constraints), the colored
   line show the location where the constraints are exactly met.)
7. Go to the Points panel.
8. Turn Iterations to All.
9. Show all the feasible and infeasible points.
10. Select Show Residuals.
    (Shows the deviation between the metamodel and predicted values as black vertical line.)

→The residuals are mainly small, thus you can say this is a well approximated metamodel.
Note the legend describing violated constraints on the top right.

Select now for response max_intrusion_steering and for variables rail_l and A_pil_l.
→In comparison with the figure above the metamodel is not so well approximated. Almost all residuals are large.

1. Restart the LS-OPT Viewer either in the LS-OPTui by clicking on the Restart viewer button or click on  new plot icon on the menu bar .
2. Select under Metamodel the item Sensitivity.

3. Select  Linear ANOVA in the new window.
4. From Response, e.g. for the load case US_NCAP, select mass.
   (The influence of different variables on response mass is shown in the Figure below.)
5. Sort the variables according to their significance for mass.

→ For the response mass the variables rail_l and t_floor are the most important variables. (They are highlighted in red and blue in the figure below .)

In contrast the variables mat_rl and mat_rO are insignificant.

The influence of different variables on response max_intrusion_rail_l:

From Response select max_intrusion_rail_l  for the load case US_NCAP, to see therefor the belonging diagramm.

 → rail_l and mat_rl are the most important variables.

GSA/Sobol

1. Select the GSA/Sobol.
2. Select Multi. (This option allows to select multiple responses.)
3. From Response select US_NCAP.
   (The influence of different variables on case US_NCAP is shown in the Figure.)
4. Sort the variables according to their significance for US_NCAP.

Select now from Response IIHS to see the influence of variables on this load case.

Sobol - Diagram for load case Torsion: 

Sobol - Diagram for load case EVA: