Monte Carlo and statistical study

Monte Carlo

Now, imagine there is an uncertainty uncertainty in the value of the tire spring rate. What is the effect of such an uncertainty on the tire compression? In this section we try to answer this question.

We consider that the uncertainty can be modeled by a Gaussian distribution with a mean value of 100000N/m (the nominal value) and a standard deviation of 1000 (1% of the nominal value).

Step 1: Define the Monte Carlo study

Procedure

1. Click the New button.

2. Change the study name to AS_MonteCarlo.

3. In the Type drop-down list, select Monte Carlo.

4. In the Study settings pane, set Optimized LH as the sampling method.

5. Under Parameters, set the Number of runs to 100.

6. In the Study parameter definition pane, set up the controls as shown in the following figure.

Figure 39: Set up the controls

7. Set up the responses as shown in the following figure.

Figure 40: Set up the responses

This setup means that during the execution:

AS_SkyHookDamperRate and AS_MainDamperRate will remain constant (at the value obtained by the optimization process).

AS_TireSpringRate will vary so that it will have a mean value very near to 100000 and a standard deviation very near to 1000.

The only output saved as a results will be AS_min_TireCompression.

Step 2: Run and analyze the Monte Carlo study

Procedure

1. Click the Start Run button and wait for the 100 runs to complete.

2. Click the Add plot button.

The Design Exploration Plot dialog box appears

3. Select Histogram in the plot type drop-down list.

4. Select MinTireComp in the item to plot drop down list.

5. Click OK.

You get the plot shown on the following figure. Figure 41: Histogram of the frequency distribution

Results

As you can see, for some cases, the tire compression is negative. The wheel would lose contact with the road!