APMonitor Modeling Language Documentation
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(:title APMonitor Modeling Language Documentation:)
(:keywords nonlinear, model, DASPK, DASSL, ode15s:)
(:description Sequential simulation mode for large-scale DAE systems.:)
!! Sequential Simulation
The DBS file parameter ''imode'' is used to control the simulation mode. This option is set to ''7'' for dynamic simulation.
''NLC.imode = 7''
Like steady-state simulation, sequential simulation requires a square problem with no degrees of freedom (n'_eqn_'=n'_var_'). It is also different than the simultaneous approach in that only one time step ahead is solved successively. Sequential simulation has many useful purposes including
* Run through a large amount of historical data
* Investigate step response characteristics of a nonlinear model
* Simulate process changes for design, trouble-shooting, or planning
* Perform what-if scenarios
* Simulate a virtual process
Sequential simulation is one of the easiest dynamic modes to configure and run. The requirement for a square problem facilitates model convergence as the solver has only to achieve feasibility with the equality constraints.
(:keywords nonlinear, model, DASPK, DASSL, ode15s:)
(:description Sequential simulation mode for large-scale DAE systems.:)
!! Sequential Simulation
The DBS file parameter ''imode'' is used to control the simulation mode. This option is set to ''7'' for dynamic simulation.
''NLC.imode = 7''
Like steady-state simulation, sequential simulation requires a square problem with no degrees of freedom (n'_eqn_'=n'_var_'). It is also different than the simultaneous approach in that only one time step ahead is solved successively. Sequential simulation has many useful purposes including
* Run through a large amount of historical data
* Investigate step response characteristics of a nonlinear model
* Simulate process changes for design, trouble-shooting, or planning
* Perform what-if scenarios
* Simulate a virtual process
Sequential simulation is one of the easiest dynamic modes to configure and run. The requirement for a square problem facilitates model convergence as the solver has only to achieve feasibility with the equality constraints.