def crossflow_plume(fig):
"""
Define, run, and plot the simulations for a pure bubble plume in crossflow
for validation to data in Socolofsky and Adams (2002).
"""
# Jet initial conditions
z0 = 0.64
U0 = 0.
phi_0 = -np.pi / 2.
theta_0 = 0.
D = 0.01
Tj = 21. + 273.15
Sj = 0.
cj = 1.
chem_name = 'tracer'
# Ambient conditions
ua = 0.15
T = 0.
F = 0.
H = 1.0
# Create the correct ambient profile data
uj = U0 * np.cos(phi_0) * np.cos(theta_0)
vj = U0 * np.cos(phi_0) * np.sin(theta_0)
wj = U0 * np.sin(phi_0)
profile_fname = './crossflow_plume.nc'
profile = get_profile(profile_fname, z0, D, uj, vj, wj, Tj, Sj, ua, T, F,
1., H)
# Create a bent plume model simulation object
jlm = bpm.Model(profile)
# Define the dispersed phase input to the model
composition = ['nitrogen', 'oxygen', 'argon', 'carbon_dioxide']
mol_frac = np.array([0.78084, 0.20946, 0.009340, 0.00036])
air = dbm.FluidParticle(composition)
particles = []
# Large bubbles
Q_N = 0.5 / 60. / 1000.
de0 = 0.008
T0 = Tj
lambda_1 = 1.
(m0, T0, nb0, P, Sa,
Ta) = dispersed_phases.initial_conditions(profile, z0, air, mol_frac, Q_N,
1, de0, T0)
particles.append(
bpm.Particle(0.,
0.,
z0,
air,
m0,
T0,
nb0,
lambda_1,
P,
Sa,
Ta,
K=1.,
K_T=1.,
fdis=1.e-6))
# Small bubbles
Q_N = 0.5 / 60. / 1000.
de0 = 0.0003
T0 = Tj
lambda_1 = 1.
(m0, T0, nb0, P, Sa,
Ta) = dispersed_phases.initial_conditions(profile, z0, air, mol_frac, Q_N,
1, de0, T0)
particles.append(
bpm.Particle(0.,
0.,
z0,
air,
m0,
T0,
nb0,
lambda_1,
P,
Sa,
Ta,
K=1.,
K_T=1.,
fdis=1.e-6))
# Run the simulation
jlm.simulate(np.array([0., 0., z0]),
D,
U0,
phi_0,
theta_0,
Sj,
Tj,
cj,
chem_name,
particles,
track=True,
dt_max=60.,
sd_max=100.)
# Perpare variables for plotting
xp = jlm.q[:, 7] / jlm.D
yp = jlm.q[:, 9] / jlm.D
plt.figure(fig)
plt.clf()
plt.show()
ax1 = plt.subplot(111)
ax1.plot(xp, yp, 'b-')
ax1.set_xlabel('x / D')
ax1.set_ylabel('z / D')
ax1.invert_yaxis()
ax1.grid(b=True, which='major', color='0.65', linestyle='-')
plt.draw()
return jlm
def get_particles(self, composition, data, md_gas0, md_oil0, profile, d50_gas, d50_oil, nbins,
T0, z0, dispersant, sigma_fac, oil, mass_frac, hydrate, inert_drop):
"""
docstring for get_particles
"""
# Reduce surface tension if dispersant is applied
if dispersant is True:
sigma = np.array([[1.], [1.]]) * sigma_fac
else:
sigma = np.array([[1.], [1.]])
# Create DBM objects for the bubbles and droplets
bubl = dbm.FluidParticle(composition, fp_type=0, sigma_correction=sigma[0], user_data=data)
drop = dbm.FluidParticle(composition, fp_type=1, sigma_correction=sigma[1], user_data=data)
# Get the local ocean conditions
T, S, P = profile.get_values(z0, ['temperature', 'salinity', 'pressure'])
rho = seawater.density(T, S, P)
# Get the mole fractions of the released fluids
molf_gas = bubl.mol_frac(md_gas0)
molf_oil = drop.mol_frac(md_oil0)
print molf_gas
print molf_oil
# Use the Rosin-Rammler distribution to get the mass flux in each
# size class
# de_gas, md_gas = sintef.rosin_rammler(nbins, d50_gas, np.sum(md_gas0),
# bubl.interface_tension(md_gas0, T0, S, P),
# bubl.density(md_gas0, T0, P), rho)
# de_oil, md_oil = sintef.rosin_rammler(nbins, d50_oil, np.sum(md_oil0),
# drop.interface_tension(md_oil0, T0, S, P),
# drop.density(md_oil0, T0, P), rho)
# Get the user defined particle size distibution
de_oil, vf_oil, de_gas, vf_gas = self.userdefined_de()
md_gas = np.sum(md_gas0) * vf_gas
md_oil = np.sum(md_oil0) * vf_oil
# Define a inert particle to be used if inert liquid particles are use
# in the simulations
molf_inert = 1.
isfluid = True
iscompressible = True
rho_o = drop.density(md_oil0, T0, P)
inert = dbm.InsolubleParticle(isfluid, iscompressible, rho_p=rho_o, gamma=40.,
beta=0.0007, co=2.90075e-9)
# Create the particle objects
particles = []
t_hyd = 0.
# Bubbles
for i in range(nbins):
if md_gas[i] > 0.:
(m0, T0, nb0, P, Sa, Ta) = dispersed_phases.initial_conditions(
profile, z0, bubl, molf_gas, md_gas[i], 2, de_gas[i], T0)
# Get the hydrate formation time for bubbles
if hydrate is True and dispersant is False:
t_hyd = dispersed_phases.hydrate_formation_time(bubl, z0, m0, T0, profile)
if np.isinf(t_hyd):
t_hyd = 0.
else:
t_hyd = 0.
particles.append(bpm.Particle(0., 0., z0, bubl, m0, T0, nb0,
1.0, P, Sa, Ta, K=1., K_T=1., fdis=1.e-6, t_hyd=t_hyd))
# Droplets
for i in range(len(de_oil)):
# Add the live droplets to the particle list
if md_oil[i] > 0. and not inert_drop:
(m0, T0, nb0, P, Sa, Ta) = dispersed_phases.initial_conditions(
profile, z0, drop, molf_oil, md_oil[i], 2, de_oil[i], T0)
# Get the hydrate formation time for bubbles
if hydrate is True and dispersant is False:
t_hyd = dispersed_phases.hydrate_formation_time(drop, z0, m0, T0, profile)
if np.isinf(t_hyd):
t_hyd = 0.
else:
t_hyd = 0.
particles.append(bpm.Particle(0., 0., z0, drop, m0, T0, nb0,
1.0, P, Sa, Ta, K=1., K_T=1., fdis=1.e-6, t_hyd=t_hyd))
# Add the inert droplets to the particle list
if md_oil[i] > 0. and inert_drop is True:
(m0, T0, nb0, P, Sa, Ta) = dispersed_phases.initial_conditions(
profile, z0, inert, molf_oil, md_oil[i], 2, de_oil[i], T0)
particles.append(bpm.Particle(0., 0., z0, inert, m0, T0, nb0,
1.0, P, Sa, Ta, K=1., K_T=1., fdis=1.e-6, t_hyd=0.))
# Define the lambda for particles
model = params.Scales(profile, particles)
for j in range(len(particles)):
particles[j].lambda_1 = model.lambda_1(z0, j)
# Return the particle list
return particles
# Larger free gas bubbles
mb0 = 5. # total mass flux in kg/s
de = 0.005 # bubble diameter in m
lambda_1 = 0.85
(m0, T0, nb0, P, Sa,
Ta) = dispersed_phases.initial_conditions(ctd, z0, gas, yk, mb0, 2, de,
Tj)
disp_phases.append(
bent_plume_model.Particle(0.,
0.,
z0,
gas,
m0,
T0,
nb0,
lambda_1,
P,
Sa,
Ta,
K=1.,
K_T=1.,
fdis=1.e-6,
t_hyd=0.,
lag_time=False))
# Smaller free gas bubbles
mb0 = 5. # total mass flux in kg/s
de = 0.0005 # bubble diameter in m
lambda_1 = 0.95
(m0, T0, nb0, P, Sa,
Ta) = dispersed_phases.initial_conditions(ctd, z0, gas, yk, mb0, 2, de,
Tj)
def particles(m_tot, d, vf, profile, oil, yk, x0, y0, z0, Tj, lambda_1,
lag_time):
"""
Create particles to add to a bent plume model simulation
Creates bent_plume_model.Particle objects for the given particle
properties so that they can be added to the total list of particles
in the simulation.
Parameters
----------
m_tot : float
Total mass flux of this fluid phase in the simulation (kg/s)
d : np.array
Array of particle sizes for this fluid phase (m)
vf : np.array
Array of volume fractions for each particle size for this fluid
phase (--). This array should sum to 1.0.
profile : ambient.Profile
An ambient.Profile object with the ambient ocean water column data
oil : dbm.FluidParticle
A dbm.FluidParticle object that contains the desired oil database
composition
yk : np.array
Mole fractions of each compound in the chemical database of the oil
dbm.FluidParticle object (--).
x0, y0, z0 : floats
Initial position of the particles in the simulation domain (m). Note
that x0 and y0 should be zero for particles starting on the plume
centerline.
Tj : float
Initial temperature of the particles in the jet (K)
lambda_1 : float
Value of the dispersed phase spreading parameter of the jet integral
model (--).
lag_time : bool
Flag that indicates whether (True) or not (False) to use the
biodegradation lag times data.
Returns
-------
disp_phases : list of bent_plume_model.Particle objects
List of `bent_plume_model.Particle` objects to be added to the
present bent plume model simulation based on the given input data.
Notes
-----
See the documentation for the `bent_plume_model` for more
information on the `Particle` object.
"""
# Create an empty list of particles
disp_phases = []
# Add each particle in the distribution separately
for i in range(len(d)):
# Get the total mass flux of this fluid phase for the present
# particle size
mb0 = vf[i] * m_tot
# Get the properties of these particles at the source
(m0, T0, nb0, P, Sa,
Ta) = dispersed_phases.initial_conditions(profile, z0, oil, yk, mb0,
2, d[i], Tj)
# Append these particles to the list of particles in the simulation
disp_phases.append(
bent_plume_model.Particle(x0,
y0,
z0,
oil,
m0,
T0,
nb0,
lambda_1,
P,
Sa,
Ta,
K=1.,
K_T=1.,
fdis=1.e-6,
t_hyd=0.,
lag_time=lag_time))
# Return the list of particles
return disp_phases
def get_sim_data():
"""
Create the data needed to initialize a simulation
Performs the steps necessary to set up a bent 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
z0 : float
Depth of the release port (m)
D : float
Diameter of the release port (m)
Vj : float
Initial velocity of the jet (m/s)
phi_0 : float
Vertical angle from the horizontal for the discharge orientation
(rad in range +/- pi/2)
theta_0 : float
Horizontal angle from the x-axis for the discharge orientation.
The x-axis is taken in the direction of the ambient current.
(rad in range 0 to 2 pi)
Sj : float
Salinity of the continuous phase fluid in the discharge (psu)
Tj : float
Temperature of the continuous phase fluid in the discharge (T)
cj : ndarray
Concentration of passive tracers in the discharge (user-defined)
tracers : string list
List of passive tracers in the discharge. These can be chemicals
present in the ambient `profile` data, and if so, entrainment of
these chemicals will change the concentrations computed for these
tracers. However, none of these concentrations are used in the
dissolution of the dispersed phase. Hence, `tracers` should not
contain any chemicals present in the dispersed phase particles.
particles : list of `Particle` objects
List of `Particle` objects describing each dispersed phase in the
simulation
dt_max : float
Maximum step size to take in the storage of the simulation
solution (s)
sd_max : float
Maximum number of orifice diameters to compute the solution along
the plume centerline (m/m)
"""
# Get the ambient CTD data
profile = get_profile()
# Specify the release location and geometry and initialize a particle
# list
z0 = 300.
D = 0.3
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 = 1.5 / 60. / 60.
de = 0.009
lambda_1 = 0.85
(m0, T0, nb0, P, Sa, Ta) = dispersed_phases.initial_conditions(
profile, z0, o2, yk, Q_N, 1, de)
particles.append(bent_plume_model.Particle(0., 0., z0, o2, m0, T0,
nb0, lambda_1, P, Sa, Ta, K=1., K_T=1., fdis=1.e-6, t_hyd=0.))
# Add an insoluble particle to the list
composition = ['inert']
yk = np.array([1.])
oil = dbm.InsolubleParticle(True, True)
mb0 = 1.
de = 0.01
lambda_1 = 0.8
(m0, T0, nb0, P, Sa, Ta) = dispersed_phases.initial_conditions(
profile, z0, oil, yk, mb0, 1, de)
particles.append(bent_plume_model.Particle(0., 0., z0, oil, m0, T0,
nb0, lambda_1, P, Sa, Ta, K=1., K_T=1., fdis=1.e-6, t_hyd=0.))
# Set the other simulation parameters
Vj = 0.
phi_0 = -np.pi/2.
theta_0 = 0.
Sj = 0.
Tj = Ta
cj = np.array([1.])
tracers = ['tracer']
dt_max = 60.
sd_max = 3000.
# Return the results
return (profile, np.array([0., 0., z0]), D, Vj, phi_0, theta_0, Sj, Tj,
cj, tracers, particles, dt_max, sd_max)
def test_particle_obj():
"""
Test the object behavior for the `Particle` object
Test the instantiation and attribute data for the `Particle` object of
the `bent_plume_model` module.
"""
# Set up the base parameters describing a particle object
T = 273.15 + 15.
P = 150e5
Sa = 35.
Ta = 273.15 + 4.
composition = ['methane', 'ethane', 'propane', 'oxygen']
yk = np.array([0.85, 0.07, 0.08, 0.0])
de = 0.005
lambda_1 = 0.85
K = 1.
Kt = 1.
fdis = 1e-6
x = 0.
y = 0.
z = 0.
# Compute a few derived quantities
bub = dbm.FluidParticle(composition)
nb0 = 1.e5
m0 = bub.masses_by_diameter(de, T, P, yk)
# Create a `PlumeParticle` object
bub_obj = bent_plume_model.Particle(x, y, z, bub, m0, T, nb0,
lambda_1, P, Sa, Ta, K, Kt, fdis)
# Check if the initialized attributes are correct
assert bub_obj.integrate == True
assert bub_obj.sim_stored == False
assert bub_obj.farfield == False
assert bub_obj.t == 0.
assert bub_obj.x == x
assert bub_obj.y == y
assert bub_obj.z == z
for i in range(len(composition)):
assert bub_obj.composition[i] == composition[i]
assert_array_almost_equal(bub_obj.m0, m0, decimal=6)
assert bub_obj.T0 == T
assert_array_almost_equal(bub_obj.m, m0, decimal=6)
assert bub_obj.T == T
assert bub_obj.cp == seawater.cp() * 0.5
assert bub_obj.K == K
assert bub_obj.K_T == Kt
assert bub_obj.fdis == fdis
for i in range(len(composition)-1):
assert bub_obj.diss_indices[i] == True
assert bub_obj.diss_indices[-1] == False
assert bub_obj.nb0 == nb0
assert bub_obj.lambda_1 == lambda_1
# Including the values after the first call to the update method
us_ans = bub.slip_velocity(m0, T, P, Sa, Ta)
rho_p_ans = bub.density(m0, T, P)
A_ans = bub.surface_area(m0, T, P, Sa, Ta)
Cs_ans = bub.solubility(m0, T, P, Sa)
beta_ans = bub.mass_transfer(m0, T, P, Sa, Ta)
beta_T_ans = bub.heat_transfer(m0, T, P, Sa, Ta)
assert bub_obj.us == us_ans
assert bub_obj.rho_p == rho_p_ans
assert bub_obj.A == A_ans
assert_array_almost_equal(bub_obj.Cs, Cs_ans, decimal=6)
assert_array_almost_equal(bub_obj.beta, beta_ans, decimal=6)
assert bub_obj.beta_T == beta_T_ans
# Test the bub_obj.outside() method
bub_obj.outside(Ta, Sa, P)
assert bub_obj.us == 0.
assert bub_obj.rho_p == seawater.density(Ta, Sa, P)
assert bub_obj.A == 0.
assert_array_almost_equal(bub_obj.Cs, np.zeros(len(composition)))
assert_array_almost_equal(bub_obj.beta, np.zeros(len(composition)))
assert bub_obj.beta_T == 0.
assert bub_obj.T == Ta
# No need to test the properties or diameter objects since they are
# inherited from the `single_bubble_model` and tested in `test_sbm`.
# No need to test the bub_obj.track(), bub_obj.run_sbm() since they will
# be tested below for the simulation cases.
# Check functionality of insoluble particle
drop = dbm.InsolubleParticle(isfluid=True, iscompressible=True)
m0 = drop.mass_by_diameter(de, T, P, Sa, Ta)
drop_obj = bent_plume_model.Particle(x, y, z, drop, m0, T, nb0,
lambda_1, P, Sa, Ta, K, fdis=fdis, K_T=Kt)
assert len(drop_obj.composition) == 1
assert drop_obj.composition[0] == 'inert'
assert_array_almost_equal(drop_obj.m0, m0, decimal=6)
assert drop_obj.T0 == T
assert_array_almost_equal(drop_obj.m, m0, decimal=6)
assert drop_obj.T == T
assert drop_obj.cp == seawater.cp() * 0.5
assert drop_obj.K == K
assert drop_obj.K_T == Kt
assert drop_obj.fdis == fdis
assert drop_obj.diss_indices[0] == True
assert drop_obj.nb0 == nb0
assert drop_obj.lambda_1 == lambda_1
# Including the values after the first call to the update method
us_ans = drop.slip_velocity(m0, T, P, Sa, Ta)
rho_p_ans = drop.density(T, P, Sa, Ta)
A_ans = drop.surface_area(m0, T, P, Sa, Ta)
beta_T_ans = drop.heat_transfer(m0, T, P, Sa, Ta)
assert drop_obj.us == us_ans
assert drop_obj.rho_p == rho_p_ans
assert drop_obj.A == A_ans
assert drop_obj.beta_T == beta_T_ans