The Field data models menu option creates a random field model based on simple numerical autocorrelation models.
In the dialog box, you set these options:
- Ident
A unique identifier for the new quantity.
- Data type
Data type of the new synthetic random field model. Choices are Node and Element.
- Desired explained variation
Accuracy of the random field model. For simulation of random designs, 90% might be sufficient. oSP3D automatically determines the number of scatter shapes required by the random field model to represent the desired variation.
- Maximum number of shapes
Upper bound for the number of scatter shapes to be computed for the random field model. This option is used to limit hardware resource usage.
- Correlation model
Definition in standard normal space of the numerical autocorrelation model type and autocorrelation length distance (
l), which you can specify as either an absolute or relative value. The following autocorrelation model types are supported:Exponential autocorrelation model using the distance between two nodes,
and 
, defined as:
Squared exponential autocorrelation model using the squared distance between two nodes,
and 
, defined as:
- Mean value and standard deviation
Defines the random field model either as homogeneous or a database object. When Define homogeneous is selected, the value that you specify to the right is used for both the mean and standard deviation at each mesh point. When Use data object is selected, user-defined individual data objects are used for the mean value and standard deviation.
- Compute cumulative variations
Select this check box to generate data objects that represent the cumulative explainable variation by the random field model, given a varying number of scatter shapes.
- Compute individual variations
Select this check box to generate data objects that represent the individual explainable variation that is explained by each individual scatter shape.
Output Objects for Each Quantity
The output objects generated for each quantity are the same as those for the empirical random field model.
Usage
The synthetic random field model offers the possibility of building a random field model even if insufficient data is available to estimate the true correlation structure.