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A small DAE 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

The model file

The code uses this model file, saved as minimal_dae.txt.

The model file 
########## NAME
minimal_dae
########## METADATA
time_unit = s
########## MACROS
########## STATES
d/dt(A) = 1 + k1 - r1
0 = r1 - B

A(0) = 1.0
B(0) = 0.0

########## PARAMETERS
k1 = 0.5
k2 = 1.0

########## VARIABLES
r1 = k2*A

########## FUNCTIONS
########## EVENTS
########## OUTPUTS
########## INPUTS
########## FEATURES
yA = A
yB = B

It can also be downloaded from from here.

Full python code to run the example

The full code to run the example is shown below, but it can also be downloaded from here.

Requirements

The code requires that the following packages are installed

uv add numpy matplotlib

pip install numpy matplotlib

Example code

#%% Import packages
import matplotlib.pyplot as plt
import numpy as np

import sund


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

#%% Import the model by using the SUND function 'load_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 = np.linspace(0, 10, 100))

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

plt.show()