Working with the model¶
For the engine to setup the equations,
variables and mappings between these the top-level subsystem is passed to a model object.
numerous.engine.model.Model traverses the system to
collect all information needed to pass to the solver for computation –
the model also back-propagates the numerical results from the solver into the system,
so they can be accessed as variable values there.
Model is created from the
m1 = Model(model_system) sim = Simulation(m1, t_start=0, t_stop=10, num=100, max_step=0.01)
Adding callbacks to model¶
It is possible to add two types of callbacks that will be executed during simulation. - callback functions. These are functions that are called each time a solver step has been completed.
m1 = Model(model_system) m1.add_callback('hitground', hitground_callback_ms1) sim = Simulation(m1, t_start=0, t_stop=10, num=100, max_step=0.01)
- event functions. An event function uses a root-finding algorithm to detect when a certain condition is triggered.
- A callback can be attached to run after any specific event.
m1 = Model(model_system) m1.add_event("hitground_event", hitground_event_fun) m1.add_event_callback("hitground_event", hitground_event_callback_fun) sim = Simulation(m1, t_start=0, t_stop=10, num=100, max_step=0.01)
Creating aliases for variables¶
Having many nested subsytems can make it difficult to follow the changes of important variable. to highlight one of the variables we can add a special alias to it. Later we can only save such variables to history data frame
Saving and restoring state of the model¶
It is possible to periodically save the states of the system to the file. Such that the long running solution would not be lost.
hdf = HistoryDataFrame() m1 = Model(S3('S3'), historian=hdf) c1 = _SimulationCallback("test") m1.save_variables_schedule(0.1, filename) s1 = Simulation(m1, t_start=0, t_stop=2, num=100) hdf2 = HistoryDataFrame.load(filename) m2 = Model(S3('S3'), historian=hdf2) m2.restore_state()