model.batch().create(<tag>,jobtype); creates a batch job tagged <tag> of type jobtype, where jobtype is Parametric, Batch, or Cluster.

model.batch().remove(<tag>) removes a batch job.

model.batch().size() returns number of batch jobs.

model.batch().tags() returns the tags of the batch jobs.

model.batch(<tag>).attach(<stag>) attaches a batch job with tag <tag> to a study with tag <stag>, which makes it visible under that study.

model.batch(<tag>).create(<jtag>,<oper>) creates a batch job sequence.

model.batch(<tag>).detach(<stag>) detaches a batch job from a study with tag <stag>.

model.batch(<tag>).remove(<ttag>) removes the task.

model.batch(<tag>).run() runs the batch job.

model.batch(<tag>).set(jprop,<jvalue>) sets the property jprop to the value <jvalue>.

model.batch(<tag>).study(<stag>) assigns a batch job to a study tag <stag>.

model.batch(<tag>).study() returns the study tag of batch job with tag <tag>.

model.batch(<tag>).feature(<ttag>)).getAllowedPropertyValues(property) returns the set of allowed values for a property if the set is a finite set of strings; otherwise, it returns null.

The Parametric job type has the following properties:

The Optimization job type sets its property through the Optimization study node, which has the following properties:

The Batch job type has the following properties:

 The Cluster job type has the following properties:

model.batch(<tag>).create(<ttag>,tasktype); creates a task of type tasktype tagged <ttag>. Find options for tasktype in Table 2-3 below.

 model.batch(<tag>).feature(<ttag>).set(ttprop,<tpvalue>) sets the task type property ttprop to the value <tpvalue>.

Task type properties can have the values listed in Table 2-4.

The Data task type contains child nodes with process information of type Process; see Table 2-5.

  model.batch(<tag>).feature(<ttag>).feature(<ptag>).set(ptype, <pvalue>) sets the property ptype to the value <pvalue>. ptype can have the values listed in Table 2-6

where pname is the name of the parametric sweep feature that ran and fname is the name of the solution feature that stored the solutions. Use

where pname is the name of the parametric sweep feature that ran and fname is the name of the solution feature that stored the solutions. Use

model.sol(), model.study()