Once the study Setup is complete, an unlimited combination of approaches can be added to a study. A study approach
is a specific set of steps taken to study the mathematical model of a design.

A DOE is a series of tests in which purposeful changes are made to the input variables
to investigate their effect upon the output responses and to get an understanding of the global behavior
of a design problem. By running a DOE, you can determine which factors are most influential on an output response.

Central Composite Design contains an imbedded factorial or fractional factorial design with center points that are
augmented with a group of star points that allow the estimation of curvature.

Primarily intended to be used as the input matrix for a Least Squares RegressionFit. By identifying the type of regression that will be used, samples are selected to maximize the determinant of the
information matrix that is inverted during the Fit’s regression analysis, which in turn improves the numerical efficiency of the DOE.

Hammersley sampling belongs to the category of quasi-Monte Carlo methods. This technique uses a quasi-random
number generator, based on the Hammersley points, to uniformly sample a unit hypercube.

A square grid containing sample positions is a Latin square if, and only if, there is only one sample in each row
and each column. A Latin HyperCubeDOE, categorized as a space filling DOE, is the generalization of this concept to an arbitrary number of dimensions.

A lattice sequence is a quasi-random sequence, or low discrepancy sequence, designed to
equally spread out points in a space by minimizing clumps and empty spaces.

Sobol Sequence is a widely known low-discrepancy sequence of quasi-random sampling family. It is designed to equally
spread-out points in a space by minimizing clumps and empty spaces.

An Inclusion matrix contains existing data that will be appended into the newly created approach as known data points.
This data typically comes from other approaches, such as DOEs or previously run Optimizations.

An Optimization is a mathematical procedure used to determine the best design for a set of given constraints, by changing
the input variables in an automatic manner.

A Stochastic approach is a method of probabilistic analysis where the input variables are defined by a probability
distribution, and consequently the corresponding output responses are not a single
deterministic value, but a distribution.

Once the study Setup is complete, an unlimited combination of approaches can be added to a study. A study approach
is a specific set of steps taken to study the mathematical model of a design.

A DOE is a series of tests in which purposeful changes are made to the input variables
to investigate their effect upon the output responses and to get an understanding of the global behavior
of a design problem. By running a DOE, you can determine which factors are most influential on an output response.

Evaluates all possible combinations of input variable levels. This
will resolve all the effects and interactions.

Table 1 shows a Full Factorial run matrix for a three
variable problem (variables A and B have two levels and variable C has three
levels).

Table 1.

Run Number

A

B

C

1

1

1

1

2

1

1

2

3

1

1

3

4

1

2

1

5

1

2

2

6

1

2

3

7

2

1

1

8

2

1

2

9

2

1

3

10

2

2

1

11

2

2

2

12

2

2

3

Usability Characteristics

For a case with k input variables, each at L levels, a
Full Factorial design has L^k runs. For studies with
a large number of input variables and levels, the total
number of runs is larger. For example, for a study with eight factors and
each with three levels, 6561 runs are needed (3^8 = 6561).

This method may be practical for studies where there is a small number of
variables and each variable has two levels, such as yes or no; -1 or 1. This
method is not practical for most CAE applications where there are many
factors possibly at several levels, and the simulations are costly.

If the number of levels is not equal across variables, then the total number
of runs is calculated by the product of the L^k terms. For example, consider
eight variables: five variables with two levels, two variables with three
levels and one variable with four levels. The number of full factorial runs
is 1152 = 2^5 * 3^2 * 4^1.

Any data in the inclusion matrix is combined with the run data for
post-processing. Any run matrix point which is already part of the inclusion
data will not be rerun.

When the number of levels is less than the defined number of states of a
discrete or categorical variable, the assigned levels are based on an
equally spaced sample of the ordinal indices.

Settings

In the Specifications step, Settings tab, change method
settings.

Parameter

Default

Range

Description

Number of Runs

${2}^{nd{v}_{2}}{3}^{nd{v}_{3}}\mathrm{...}$

2-1,000,000

Number of new designs to
be evaluated.$nd{v}_{i}$ is the number of input variables with i levels. This number is
determined automatically based on the number of input variables and levels.

Use Inclusion
Matrix

Off

Off or On

Concatenation without
duplication between the inclusion and the generated run matrix.