Page 87 - Using ANSYS for Finite Element Analysis Dynamic, Probabilistic, Design and Heat Transfer Analysis

74 • using ansys for finite eLement anaLysis

(Continued )
PDS term Description
Mathematically, the mean value is the arithmetic aver-
age of the data. The mean value also represents the
center of gravity of the data points. Another name for
the mean value is the expected value.
The statistical point where 50% of the data is below the
median value and the 50% is above.
Median For symmetrical distribution functions (Gaussian, uni-
value form, etc.) the median value and the mean value are
identical, while for nonsymmetrical distributions they
are different.
A measure of variability (dispersion or spread) about
the arithmetic mean value, often used to describe the
width of the scatter of a random output parameter or
Standard of a statistical distribution function.
deviation
The larger the standard deviation, the wider the scatter
and the more likely it is that there are data values
further apart from the mean value.

3.1.4 STePS FoR PRobAbiLiSTiC DeSign AnALySiS USing
ANSyS

The usual process for probabilistic design consists of the following gen-
eral steps:

1. Create an analysis file for use during looping. The file should
represent a complete analysis sequence and must do the following:
• Build the model parametrically (PREP7).
• Obtain the solution(s) (SOLUTION).
• Retrieve and assign to parameters the quantities that will be
used as RVs and RPs (POST1/POST26).
2. Establish parameters in the ANSYS database, which corre-
spond to those used in the analysis file. This step is typical, but not
required (Begin or PDS); however, if you skip this step, then the
parameter names are not available for selection in interactive mode.
3. Enter PDS and specify the analysis file (PDS).
4. Declare random input variables (PDS).
5. Visualize random input variables (PDS). Optional.
6. Specify any correlations between the RVs (PDS).
7. Specify random output parameters (PDS).