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for a random variable that is the result of multiplying two or more random effects (if the effects that get multiplied are also lognormally distributed). It is often used for lifetime distributions; for example, the scatter of the strain amplitude of a cyclic loading that a material can endure until low-cycle-fatigue occurs is very often described by a lognormal distribution.

Uniform Distribution

The uniform distribution is a very fundamental distribution for cases where no other information apart from a lower and an upper limit exists. It is very useful to describe geometric tolerances. It can also be used in cases where there is no evidence that any value of the random variable is more likely than any other within a certain interval. In this sense it can be used for cases where "lack of engineering knowledge" plays a role.

Triangular Distribution

The triangular distribution is most helpful to model a random variable when actual data is not available. It is very often used to cast the results of expert-opinion into a mathematical form, and is often used

to describe the scatter of load parameters. However, regardless of the physical nature of the random variable you want to model, you can always ask some experts questions like "What is the one-in-a- thousand minimum and maximum case for this random variable? and other similar questions. You should also include an estimate for the random variable value derived from a computer program, as described earlier. This is also described in more detail above for load parameters in Choosing a Distribution for a Random Variable (p. 45).

Truncated Gaussian Distribution

The truncated Gaussian distribution typically appears where the physical phenomenon follows a Gaussian distribution, but the extreme ends are cut off or are eliminated from the sample population by quality control measures. As such, it is useful to describe the material properties or geometric tolerances.

Weibull Distribution

In engineering, the Weibull distribution is most often used for strength or strength-related lifetime parameters, and it is the standard distribution for material strength and lifetime parameters for very brittle materials (for these very brittle material the "weakest-link-theory" is applicable). For more details see Choosing a Distribution for a Random Variable (p. 45).

1.4.2. Choosing Random Output Parameters

Output parameters are usually parameters such as length, thickness, diameter, or model coordinates.

The ANSYS PDS does not restrict you with regard the number of random output parameters, provided that the total number of probabilistic design variables (that is random input variables and random output parameters together) does not exceed 5000.

ANSYS, Inc. recommends that you include all output parameters that you can think of and that might be useful to you. The additional computing time required to handle more random output parameters is marginal when compared to the time required to solve the problem. It is better to define random output parameters that you might not consider important before you start the analysis. If you forgot to include a random output parameter that later turns out to be important, you must redo the entire analysis.