Page 114 - Using ANSYS for Finite Element Analysis Dynamic, Probabilistic, Design and Heat Transfer Analysis

probabilistic Design analysis • 101
• As a lognormal plot. If the probabilistic design variable follows a
lognormal distribution then the cdf is displayed as a straight line in
this type of plot.
• As a Weibull plot. If the probabilistic design variable follows a
Weibull distribution then the cdf is displayed as a straight line in
this type of plot.

3.5.1.4 Print Probabilities

The PDS offers a function where you can determine the cdf at any point
along the axis of the probabilistic design variable, including an interpola-
tion function so you can evaluate the probabilities between sampling points.
This feature is most helpful if you want to evaluate the failure probability
or reliability of your component for a very specific and given limit value.

3.5.1.5 Print inverse Probabilities

The PDS offers a function where you can probe the cdf by specifying a cer-
tain probability level; the PDS tells you at which value of the probabilistic
design variable this probability will occur. This is helpful if you want to
evaluate what limit you should specify to not exceed a certain failure prob-
ability, or to specifically achieve certain reliability for your component.

3.5.2 TRenD PoSTPRoCeSSing

Trend postprocessing allows you several options for reviewing your results.

3.5.2.1 Sensitivities

Probabilistic sensitivities are important in allowing you to improve your
design toward a more reliable and better quality product, or to save money
while maintaining the reliability or quality of your product. You can
request a sensitivity plot for any random output parameter in your model.
There is a difference between probabilistic sensitivities and determin-
istic sensitivities. Deterministic sensitivities are mostly only local gradient
information. For example, to evaluate deterministic sensitivities you can
vary each input parameters by ±10 percent (one at a time) while keeping all
other input parameters constant, then see how the output parameters react to
these variations. As illustrated in the following figure, an output parameter