exprs['C'] = m.P['k1'] * m.Z[t, 'A'] * m.Z[t, 'B']
return exprs
builder.set_odes_rule(rule_odes)
# create an instance of a pyomo model template
# the template includes
# - Z variables indexed over time and components names e.g. m.Z[t,'A']
# - P parameters indexed over the parameter names e.g. m.P['k']
pyomo_model = builder.create_pyomo_model(0.0, 1000.0)
# create instance of simulator
simulator = PyomoSimulator(pyomo_model)
# defines the discrete points wanted in the concentration profile
simulator.apply_discretization('dae.collocation',
nfe=200,
ncp=3,
scheme='LAGRANGE-RADAU')
# simulate
results_pyomo = simulator.run_sim('ipopt', tee=True)
# display concentration results
if with_plots:
results_pyomo.Z.plot.line(legend=True)
plt.xlabel("time (s)")
plt.ylabel("Concentration (mol/L)")
plt.title("Concentration Profile")
plt.show()
initialization = pd.read_csv(filename_initY,index_col=0)
sim.initialize_from_trajectory('Y',initialization)
sim.fix_from_trajectory('Y','Csat',fixed_traj)
sim.fix_from_trajectory('Y','f',fixed_traj)
with open("f0.txt", "w") as f:
for t in sim.model.alltime:
val = value(sim.model.Y[t, 'f'])
f.write('\t' + str(t) + '\t' + str(val) + '\n')
f.close()
options = {'halt_on_ampl_error' :'yes'}
results = sim.run_sim('ipopt',
tee=True,
solver_opts=options)
if with_plots:
# display concentration results
results.Z.plot.line(legend=True)
plt.xlabel("time (s)")
plt.ylabel("Concentration (mol/L)")
plt.title("Concentration Profile")
C.plot()
plt.figure()
results.Y['Csat'].plot.line()
plt.plot(fixed_traj['Csat'],'*')
plt.xlabel("time (s)")
# create an instance of a pyomo model template
# the template includes
# - Z variables indexed over time and components names e.g. m.Z[t,'A']
# - P parameters indexed over the parameter names e.g. m.P['k']
pyomo_model = builder.create_pyomo_model(0.0, 200.0)
# create instance of simulator
simulator = PyomoSimulator(pyomo_model)
# defines the discrete points wanted in the concentration profile
simulator.apply_discretization('dae.collocation',
nfe=30,
ncp=1,
scheme='LAGRANGE-RADAU')
# simulate
results_pyomo = simulator.run_sim('ipopt', tee=True)
# second model to scale and initialize
scaled_model = builder.create_pyomo_model(0.0, 200.0)
scaled_sim = PyomoSimulator(scaled_model)
scaled_sim.apply_discretization('dae.collocation',
nfe=30,
ncp=1,
scheme='LAGRANGE-RADAU')
scaled_sim.initialize_from_trajectory('Z', results_pyomo.Z)
scaled_sim.initialize_from_trajectory('dZdt', results_pyomo.dZdt)
scaled_sim.scale_variables_from_trajectory('Z', results_pyomo.Z)
scaled_sim.scale_variables_from_trajectory('dZdt', results_pyomo.dZdt)
return exprs
builder.set_odes_rule(rule_odes)
sim_model = builder.create_pyomo_model(0.0, 40.0)
# create instance of simulator
simulator = PyomoSimulator(sim_model)
simulator.apply_discretization('dae.collocation',
nfe=4,
ncp=1,
scheme='LAGRANGE-RADAU')
# simulate
sigmas = {'device': 1e-10, 'A': 1e-5, 'B': 1e-7}
results_sim = simulator.run_sim('ipopt',
tee=True,
variances=sigmas,
seed=123453256)
if with_plots:
results_sim.C.plot.line()
results_sim.S.plot.line()
plt.show()
#################################################################################
builder = TemplateBuilder()
builder.add_mixture_component(concentrations)
builder.add_parameter('k', bounds=(0.0, 1.0))
builder.add_spectral_data(results_sim.D)
builder.set_odes_rule(rule_odes)
# Once the model is described we run the simulator
# call FESimulator
# FESimulator re-constructs the current TemplateBuilder into fe_factory syntax
# there is no need to call PyomoSimulator any more as FESimulator is a child class
sim = PyomoSimulator(opt_model)
# defines the discrete points wanted in the concentration profile
sim.apply_discretization('dae.collocation', nfe=50, ncp=3, scheme='LAGRANGE-RADAU')
#this will allow for the fe_factory to run the element by element march forward along
#the elements and also automatically initialize the PyomoSimulator model, allowing
#for the use of the run_sim() function as before. We only need to provide the inputs
#to this function as an argument dictionary
init = sim.run_sim(solver = 'ipopt', tee = True)
if with_plots:
init.Z.plot.line(legend=True)
plt.xlabel("time (min)")
plt.ylabel("Concentration ((mol /cm3))")
plt.title("Concentration Profile")
plt.show()
print("Simulation is done")
data = add_noise_to_signal(init.Z,0.02)
if with_plots: