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A minimal ODE model

This example will cover the basic setup to setup a new model in SUND. It will:

  1. Install and import the model
  2. Build the model and simulation objects
  3. Simulate using the simulation object

Python Code

The code uses this model file: small dae model.

The code requires that the following packages are installed

uv add numpy matplotlib

pip install numpy matplotlib

Following the python code is attached, and it can also be downloaded from here: here. 

#%% Import packages
import matplotlib.pyplot
import numpy

import sund

#%% Print the model to a file called 'minimal_dae.txt'
MODEL_NAME = "minimal_dae"

#%% Install the model by using the SUND function 'install_model'
sund.install_model(f"modelfiles/{MODEL_NAME}.txt")

#%% Import the model by using the SUND function 'import_model'
model = sund.load_model(f"{MODEL_NAME}")

#%% A simulation object can be constructed from the model template 
sim = sund.Simulation(models = model, time_unit = model.time_unit)

#%% DAE settings
# We set the option for the solver to calculate consistent initial conditions for the  algebraic states 
# and differential derivatives by using the given initial conditions for the differential states.
# The option is called 'calc_ic', and the default value is false.
options = sim.get_options()
print(f'Option calc_ic before change: {options['calc_ic']}')
options['calc_ic'] = True
print(f'Option calc_ic after change: {options['calc_ic']}')

sim.set_options(options)

#%% Now we can simulate the model using the simulation object
sim.simulate(time_vector = numpy.linspace(0, 10, 100))

matplotlib.pyplot.figure()
feature_index = sim.feature_names.index('yA')
matplotlib.pyplot.plot(sim.time_vector, sim.feature_data[:, feature_index])
matplotlib.pyplot.xlabel(sim.time_unit)
matplotlib.pyplot.ylabel('A.U.')
matplotlib.pyplot.title("Simulation result")

matplotlib.pyplot.show()