def get_sim_data():
"""
Get the input data to the `params.Scales` object
Create an `ambient.Profile` object and a list of
`stratified_plume_model.Particle` objects as the required input for
the `params.Scales` object.
Returns
-------
profile : `ambient.Profile` object
profile object from the BM54 CTD data
disp_phases: list
list of `stratified_plume_model.Particle` objects describing the
blowout dispersed phases.
z0 : float
depth at the plume model origin (m)
"""
# Get the netCDF file
nc = test_sbm.make_ctd_file()
# Create a profile object with all available chemicals in the CTD data
profile = ambient.Profile(nc, chem_names='all')
# Create the stratified plume model object
spm = stratified_plume_model.Model(profile)
# Set the release conditions
T0 = 273.15 + 35. # Release temperature in K
R = 0.15 # Radius of leak source in m
# Create the gas phase particles
composition = ['methane', 'ethane', 'propane', 'oxygen']
yk = np.array([0.93, 0.05, 0.02, 0.0])
gas = dbm.FluidParticle(composition)
z0 = 1000.
disp_phases = []
# Larger free gas bubbles
mb0 = 8. # total mass flux in kg/s
de = 0.025 # bubble diameter in m
lambda_1 = 0.85
disp_phases.append(
stratified_plume_model.particle_from_mb0(profile, z0, gas, yk, mb0, de,
lambda_1, T0))
# Smaller free gas bubbles (note, it is not necessary to have more than
# one bubble size)
mb0 = 2. # total mass flux in kg/s
de = 0.0075 # bubble diameter in m
lambda_1 = 0.9
disp_phases.append(
stratified_plume_model.particle_from_mb0(profile, z0, gas, yk, mb0, de,
lambda_1, T0))
# Liquid hydrocarbon. This could either be a dissolving phase (mixture
# of liquid phases) or an inert phase. We demonstrate here the simple
# case of an inert oil phase
oil = dbm.InsolubleParticle(True,
True,
rho_p=890.,
gamma=30.,
beta=0.0007,
co=2.90075e-9)
mb0 = 10. # total mass flux in kg/s
de = 0.004 # bubble diameter in m
lambda_1 = 0.9
disp_phases.append(
stratified_plume_model.particle_from_mb0(profile, z0, oil,
np.array([1.]), mb0, de,
lambda_1, T0))
# Return the simulation data
return (profile, disp_phases, z0)
T0 = 273.15 + 35. # Release temperature in K
R = 0.15 # Radius of leak source in m
# Create the gas phase particles
composition = ['methane', 'ethane', 'propane', 'oxygen']
yk = np.array([0.93, 0.05, 0.02, 0.0])
gas = dbm.FluidParticle(composition)
z0 = 1000.
disp_phases = []
# Larger free gas bubbles
mb0 = 8. # total mass flux in kg/s
de = 0.025 # bubble diameter in m
lambda_1 = 0.85
disp_phases.append(
stratified_plume_model.particle_from_mb0(ctd, z0, gas, yk, mb0, de,
lambda_1, T0))
# Smaller free gas bubbles (note, it is not necessary to have more than
# one bubble size)
mb0 = 2. # total mass flux in kg/s
de = 0.0075 # bubble diameter in m
lambda_1 = 0.9
disp_phases.append(
stratified_plume_model.particle_from_mb0(ctd, z0, gas, yk, mb0, de,
lambda_1, T0))
# Liquid hydrocarbon. This could either be a dissolving phase (mixture
# of liquid phases) or an inert phase. We demonstrate here the simple
# case of an inert oil phase
oil = dbm.InsolubleParticle(True,
True,
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)
# Set the release conditions
T0 = 273.15 + 35. # Release temperature in K
R = 0.15 # Radius of leak source in m
# Create the gas phase particles
composition = ['methane', 'ethane', 'propane', 'oxygen']
yk = np.array([0.93, 0.05, 0.02, 0.0])
gas = dbm.FluidParticle(composition)
z0 = 1000.
disp_phases = []
# Larger free gas bubbles
mb0 = 8. # total mass flux in kg/s
de = 0.025 # bubble diameter in m
lambda_1 = 0.85
disp_phases.append(stratified_plume_model.particle_from_mb0(ctd, z0, gas,
yk, mb0, de, lambda_1, T0))
# Smaller free gas bubbles (note, it is not necessary to have more than
# one bubble size)
mb0 = 2. # total mass flux in kg/s
de = 0.0075 # bubble diameter in m
lambda_1 = 0.9
disp_phases.append(stratified_plume_model.particle_from_mb0(ctd, z0, gas,
yk, mb0, de, lambda_1, T0))
# Liquid hydrocarbon. This could either be a dissolving phase (mixture
# of liquid phases) or an inert phase. We demonstrate here the simple
# case of an inert oil phase
oil = dbm.InsolubleParticle(True, True, rho_p=890., gamma=30.,
beta=0.0007, co=2.90075e-9)
mb0 = 10. # total mass flux in kg/s
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)
def get_sim_data():
"""
Get the input data to the `params.Scales` object
Create an `ambient.Profile` object and a list of
`stratified_plume_model.Particle` objects as the required input for
the `params.Scales` object.
Returns
-------
profile : `ambient.Profile` object
profile object from the BM54 CTD data
disp_phases: list
list of `stratified_plume_model.Particle` objects describing the
blowout dispersed phases.
z0 : float
depth at the plume model origin (m)
"""
# Get the netCDF file
nc = test_sbm.make_ctd_file()
# Create a profile object with all available chemicals in the CTD data
profile = ambient.Profile(nc, chem_names='all')
# Create the stratified plume model object
spm = stratified_plume_model.Model(profile)
# Set the release conditions
T0 = 273.15 + 35. # Release temperature in K
R = 0.15 # Radius of leak source in m
# Create the gas phase particles
composition = ['methane', 'ethane', 'propane', 'oxygen']
yk = np.array([0.93, 0.05, 0.02, 0.0])
gas = dbm.FluidParticle(composition)
z0 = 1000.
disp_phases = []
# Larger free gas bubbles
mb0 = 8. # total mass flux in kg/s
de = 0.025 # bubble diameter in m
lambda_1 = 0.85
disp_phases.append(stratified_plume_model.particle_from_mb0(profile, z0,
gas, yk, mb0, de, lambda_1, T0))
# Smaller free gas bubbles (note, it is not necessary to have more than
# one bubble size)
mb0 = 2. # total mass flux in kg/s
de = 0.0075 # bubble diameter in m
lambda_1 = 0.9
disp_phases.append(stratified_plume_model.particle_from_mb0(profile, z0,
gas, yk, mb0, de, lambda_1, T0))
# Liquid hydrocarbon. This could either be a dissolving phase (mixture
# of liquid phases) or an inert phase. We demonstrate here the simple
# case of an inert oil phase
oil = dbm.InsolubleParticle(True, True, rho_p=890., gamma=30.,
beta=0.0007, co=2.90075e-9)
mb0 = 10. # total mass flux in kg/s
de = 0.004 # bubble diameter in m
lambda_1 = 0.9
disp_phases.append(stratified_plume_model.particle_from_mb0(profile, z0,
oil, np.array([1.]), mb0, de, lambda_1, T0))
# Return the simulation data
return (profile, disp_phases, z0)