# Create the lake netCDF dataset and get the Profile object
lake = lake_bub.get_lake_data()
# Create the stratified plume model object
spm = stratified_plume_model.Model(lake)
# Create the dispersed phase particles
sphere = dbm.InsolubleParticle(False, False, rho_p=15.)
z0 = 46.
particles = []
# Small particle
Q_N = 100. / 60. / 60.
de = 0.01
lambda_1 = 0.7
particles.append(stratified_plume_model.particle_from_Q(lake, z0, sphere,
1., Q_N, de, lambda_1, 273.15 + 20.))
# Initialize a simulation
R = 6.5 / 2.
spm.simulate(particles, z0, R, maxit=5, delta_z = 0.2)
# Save the model results
spm.save_sim('../../test/output/spm_sphere.nc',
'../../test/output/lake.nc',
'Lake data from McGinnis et al. (2006) in ./test/output/lake.nc')
# Demonstrate how to read the data back in from the hard drive
spm.load_sim('../../test/output/spm_sphere.nc')
spm.plot_state_space(1)
# Plot the full suite of model variables
def get_sim_data():
"""
Create the data needed to initialize a simulation
Performs the steps necessary to set up a stratified plume model simulation
and passes the input variables to the `Model` object and
`Model.simulate()` method.
Returns
-------
profile : `ambient.Profile` object
Return a profile object from the BM54 CTD data
particles : list of `PlumeParticle` objects
List of `PlumeParticle` objects containing the dispersed phase initial
conditions
z : float
Depth of the release port (m)
R : float
Radius of the release port (m)
maxit : float
Maximum number of iterations to converge between inner and outer
plumes
toler : float
Relative error tolerance to accept for convergence (--)
delta_z : float
Maximum step size to use in the simulation (m). The ODE solver
in `calculate` is set up with adaptive step size integration, so
in theory this value determines the largest step size in the
output data, but not the numerical stability of the calculation.
"""
# Get the ambient CTD data
profile = get_profile()
# Specify the release location and geometry and initialize a particle
# list
z0 = 300.
R = 0.15
particles = []
# Add a dissolving particle to the list
composition = ['oxygen', 'nitrogen', 'argon']
yk = np.array([1.0, 0., 0.])
o2 = dbm.FluidParticle(composition)
Q_N = 150. / 60. / 60.
de = 0.005
lambda_1 = 0.85
particles.append(
stratified_plume_model.particle_from_Q(profile, z0, o2, yk, Q_N, de,
lambda_1))
# Add an insoluble particle to the list
composition = ['inert']
yk = np.array([1.])
oil = dbm.InsolubleParticle(True, True)
mb0 = 50.
de = 0.01
lambda_1 = 0.8
particles.append(
stratified_plume_model.particle_from_mb0(profile, z0, oil, [1.], mb0,
de, lambda_1))
# Set the other simulation parameters
maxit = 2
toler = 0.2
delta_z = 1.0
# Return the results
return (profile, particles, z0, R, maxit, toler, delta_z)
# Create the lake netCDF dataset and get the Profile object
lake = lake_bub.get_lake_data()
# Create the stratified plume model object
spm = stratified_plume_model.Model(lake)
# Create the dispersed phase particles
sphere = dbm.InsolubleParticle(False, False, rho_p=15.)
z0 = 46.
particles = []
# Small particle
Q_N = 100. / 60. / 60.
de = 0.01
lambda_1 = 0.7
particles.append(stratified_plume_model.particle_from_Q(lake, z0, sphere,
1., Q_N, de, lambda_1, 273.15 + 20.))
# Initialize a simulation
R = 6.5 / 2.
spm.simulate(particles, z0, R, maxit=5, delta_z = 0.2)
# Save the model results
spm.save_sim('../../test/output/spm_sphere.nc',
'../../test/output/lake.nc',
'Lake data from McGinnis et al. (2006) in ./test/output/lake.nc')
# Demonstrate how to read the data back in from the hard drive
spm.load_sim('../../test/output/spm_sphere.nc')
spm.plot_state_space(1)
# Plot the full suite of model variables
# Create the stratified plume model object
spm = stratified_plume_model.Model(lake)
# Create the dispersed phase particles
composition = ['oxygen', 'nitrogen', 'argon']
yk = np.array([1.0, 0., 0.])
o2 = dbm.FluidParticle(composition, isair=True)
z0 = 46.
bubbles = []
# Small bubble
Q_N = 30. / 60. / 60.
de = 0.001
lambda_1 = 0.9
bubbles.append(stratified_plume_model.particle_from_Q(lake, z0, o2, yk,
Q_N, de, lambda_1, t_hyd=0.))
# Medium bubble
Q_N = 70. / 60./ 60.
de = 0.002
lambda_1 = 0.8
bubbles.append(stratified_plume_model.particle_from_Q(lake, z0, o2, yk,
Q_N, de, lambda_1, t_hyd=0.))
# Initialize a simulation
R = 6.5 / 2.
spm.simulate(bubbles, z0, R, maxit=50, delta_z = 0.2)
# Save the model results
spm.save_sim('../../test/output/spm_gas.nc', '../../test/output/lake.nc',
'Lake data from McGinnis et al. (2006) in ./test/output/lake.nc')
def get_sim_data():
"""
Create the data needed to initialize a simulation
Performs the steps necessary to set up a stratified plume model simulation
and passes the input variables to the `Model` object and
`Model.simulate()` method.
Returns
-------
profile : `ambient.Profile` object
Return a profile object from the BM54 CTD data
particles : list of `PlumeParticle` objects
List of `PlumeParticle` objects containing the dispersed phase initial
conditions
z : float
Depth of the release port (m)
R : float
Radius of the release port (m)
maxit : float
Maximum number of iterations to converge between inner and outer
plumes
toler : float
Relative error tolerance to accept for convergence (--)
delta_z : float
Maximum step size to use in the simulation (m). The ODE solver
in `calculate` is set up with adaptive step size integration, so
in theory this value determines the largest step size in the
output data, but not the numerical stability of the calculation.
"""
# Get the ambient CTD data
profile = get_profile()
# Specify the release location and geometry and initialize a particle
# list
z0 = 300.
R = 0.15
particles = []
# Add a dissolving particle to the list
composition = ['oxygen', 'nitrogen', 'argon']
yk = np.array([1.0, 0., 0.])
o2 = dbm.FluidParticle(composition)
Q_N = 150. / 60. / 60.
de = 0.005
lambda_1 = 0.85
particles.append(stratified_plume_model.particle_from_Q(profile, z0, o2,
yk, Q_N, de, lambda_1))
# Add an insoluble particle to the list
composition = ['inert']
yk = np.array([1.])
oil = dbm.InsolubleParticle(True, True)
mb0 = 50.
de = 0.01
lambda_1 = 0.8
particles.append(stratified_plume_model.particle_from_mb0(profile, z0,
oil, [1.], mb0, de, lambda_1))
# Set the other simulation parameters
maxit = 2
toler = 0.2
delta_z = 1.0
# Return the results
return (profile, particles, z0, R, maxit, toler, delta_z)
# Create the dispersed phase particles
composition = ['oxygen', 'nitrogen', 'argon']
yk = np.array([1.0, 0., 0.])
o2 = dbm.FluidParticle(composition, isair=True)
z0 = 46.
bubbles = []
# Small bubble
Q_N = 30. / 60. / 60.
de = 0.001
lambda_1 = 0.9
bubbles.append(
stratified_plume_model.particle_from_Q(lake,
z0,
o2,
yk,
Q_N,
de,
lambda_1,
t_hyd=0.))
# Medium bubble
Q_N = 70. / 60. / 60.
de = 0.002
lambda_1 = 0.8
bubbles.append(
stratified_plume_model.particle_from_Q(lake,
z0,
o2,
yk,
Q_N,
de,