def TwoGaussianImmuneResponse2D(strength, x, y, Ra):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz))/numerix.sqrt(2*numerix.pi*2*dz))
return ((strength) * numerix.exp(-((x + 0.6)**2 + (y - 0.3)**2) /
(2 * Ra)) +
(strength) * numerix.exp(-((x - 0.6)**2 + (y - 0.2)**2) /
(2 * Ra))) / (numerix.sqrt(
2 * numerix.pi * Ra))
def calc_dep_vars(self, params):
Fbar = params.faradaysConstant / params.gasConstant / params.temperature
self.coeff_forward0 = params.alpha0 * Fbar
self.coeff_backward0 = (params.alpha_ - params.alpha0) * Fbar
self.coeff_forward1 = params.alpha1 * Fbar
self.coeff_backward1 = (params.alpha_ - params.alpha1) * Fbar
exp_forward0 = numerix.exp(self.coeff_forward0 * self.potential)
exp_backward0 = numerix.exp(-self.coeff_backward0 * self.potential)
exp_forward1 = numerix.exp(self.coeff_forward1 * self.potential)
exp_backward1 = numerix.exp(-self.coeff_backward1 * self.potential)
cbar = self.cupric / params.bulkCupric
I0 = params.i0 * (1 - self.interfaceTheta)
I1 = params.i1 * self.interfaceTheta
self.beta_forward0 = cbar * I0 * exp_forward0
self.beta_backward0 = cbar * I0 * exp_backward0
self.beta_forward1 = cbar * I1 * exp_forward1
self.beta_backward1 = cbar * I1 * exp_backward1
self.baseCurrent = I0 * (exp_forward0 - exp_backward0) + I1 * (exp_forward1 - exp_backward1)
self.currentDensity = cbar * self.baseCurrent
self.currentDerivative = cbar * (I0 * (self.coeff_forward0 * exp_forward0 + self.coeff_backward0 * exp_backward0) \
+ I1 * (self.coeff_forward1 * exp_forward1 + self.coeff_backward1 * exp_backward1))
self.depositionRate = self.currentDensity * params.omega / params.charge / params.faradaysConstant
def calc_BV(self, j0,
overpotential): # Coded here is the RHS of the BV equation
# print(overpotential) # Code to debug the exponential overflow issue when the number of nodes in each domain is changed
# print(numerix.exp(((1-self.alpha)*F*(overpotential))/(R*self.T))) # Overpotential values are high, causing the exponents to overflow, it seems..
# print(numerix.exp((-self.alpha*F*(overpotential))/(R*self.T)))
return (j0 * (
numerix.exp(
((1 - self.alpha) * F * (overpotential)) / (R * self.T)
) # Returns a value for "j", flux density on the LHS of the equation
- numerix.exp((-self.alpha * F * (overpotential)) / (R * self.T))))
def __call__(self, x):
"""Evaluate the solver."""
self.phi.value = 0.
x_center = x[:2]
tau = x[2]
h = x[3]
s = x[4]
x, y = self.mesh.cellCenters
self.source.value = 0.5 * s / (math.pi * h**2) * numerix.exp(-0.5 * (
(x_center[0] - x)**2 + (x_center[1] - y)**2) / (2. * h**2))
y_all = []
t = 0.
self._times = []
next_time = 0
while t < self.max_time:
t += self.dt
add = False
if next_time < len(self.T) and t >= self.T[next_time]:
t = self.T[next_time]
next_time += 1
add = True
if t >= tau:
self.source.value = 0.
self.eq.solve(var=self.phi, dt=self.dt)
if add:
self._times.append(t)
y_all.append([self.phi.value[i] for i in self.idx])
y = np.hstack(y_all)
y = y.reshape((self.T.shape[0], len(self.idx))).flatten(order='F')
return y
def __call__(self, x):
"""Evaluate the solver."""
self.phi.value = 0.
x_center = x[:2]
tau = x[2]
h = x[3]
s = x[4]
x, y = self.mesh.cellCenters
self.source.value = 0.5 * s / (math.pi * h ** 2) * numerix.exp(-0.5 *
((x_center[0] - x) ** 2 + (x_center[1] - y) ** 2)
/ (2. * h ** 2))
y_all = []
t = 0.
self._times = []
next_time = 0
while t < self.max_time:
t += self.dt
add = False
if next_time < len(self.T) and t >= self.T[next_time]:
t = self.T[next_time]
next_time += 1
add = True
if t >= tau:
self.source.value = 0.
self.eq.solve(var=self.phi, dt=self.dt)
if add:
self._times.append(t)
y_all.append([self.phi.value[i] for i in self.idx])
y = np.hstack(y_all)
y = y.reshape((self.T.shape[0], len(self.idx))).flatten(order='F')
return y
def define_non_uniform_comp_domain_rad(
self, shells_per_particle, normalised_particle_radius
): # Non-uniform radial domain, electrode particles
self.nr = int(shells_per_particle)
self.Rs_normalised = normalised_particle_radius
self.dr = numerix.diff(
numerix.log(1 - numerix.linspace(
0, 1.0 - numerix.exp(self.Rs_normalised), self.nr + 1)))
self.p2d_mesh = Grid2D(dx=self.dx,
dy=self.dr,
Lx=self.L_normalised,
Ly=self.Rs_normalised)
self.dummy, self.radial_faces = self.p2d_mesh.faceCenters # Create a shorthand name for accessing y-axis faces in the particle mesh
def fluxes(self, ts):
# wind-independent roughness as in doi:10.3189/2014JoG13J045
Tsurf = ts.variables['T_right'][self.indexCell(ts)]
# Tsurf = ts.variables['T'][self.indexCell]
# sat; T in Celsius; in Pa
VPsurf = 100 * 6.1094 * numerix.exp(
(17.625 * Tsurf) / (Tsurf + 243.04))
# T in Celsius
flux_radiation = (self.Sin * (1 - self.bc_parameters['albedo']) +
self.bc_parameters['emissivity'] * self.Lin -
self.bc_parameters['emissivity'] * Stefan_Boltzmann *
(Tsurf + 273.15)**4)
C = von_Karman**2 / (numerix.log(
(self.meas_height - self.bc_parameters['d0']) /
self.bc_parameters['z0m']) * numerix.log(
(self.meas_height - self.bc_parameters['d0']) /
self.bc_parameters['z0a']))
flux_latent = (self.bc_parameters['beta'] * C * rho_air * Le * 0.622 *
self.wind * (self.VPair - VPsurf) / self.pressure)
flux_sensible = C * rho_air * cp_air * self.wind * (self.Tair - Tsurf)
return Fluxes(radiation=flux_radiation,
latent=flux_latent,
sensible=flux_sensible,
total=flux_radiation + flux_latent + flux_sensible)
def loopGaussianKernel(z, dz):
n = z.shape[0]
m = z.shape[0]
kern = sp.lil_matrix((n, m), dtype=np.float64)
# kern = numerix.zeros((n,m))
# eps = 1e-10
eps = dz**2
digits = len(str(n - 1))
delete = "\b" * (digits + 1)
for i in range(n):
#if i>0:
# for j in range(m):
# if j>0:
# kern[i, j] = numerix.exp((-(2*z[i] - z[j])**2)/(2*eps))
kern[i] = numerix.exp((-(2 * z[i] - z)**2) / (2 * eps))
# kern = kern + sp.coo_matrix((np.array([numerix.exp((-(2*z[i] - z[j])**2)/(2*eps))]), (np.array([i]), np.array([j]))), shape=(n, m))
# kern.tocsc()
#/numerix.sqrt(2*numerix.pi*eps)
# print "{0}{1:{2}}".format(delete, i, digits),
# level = int((np.float(i)/np.float(n))*100)
# print "{0}%\r".format((np.float(i)/np.float(n)*100))
# print "#"*(level+1),
return kern / numerix.sqrt(2 * numerix.pi * eps)
def SigmaF2D(amp, x,y, Rs):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz**2))/(numerix.sqrt(dz**2)*numerix.sqrt(2*numerix.pi)))
return amp*numerix.exp(-(x**2 + y**2)/(2*Rs))/(numerix.sqrt(2*numerix.pi*Rs))
def gaussian(x, mu, sig):
return numerix.exp(-numerix.power(x - mu, 2.) /
(2 * numerix.power(sig, 2.)))
def gaussianDivisionRate(z, mean, sigma):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz))/numerix.sqrt(2*numerix.pi*2*dz))
return numerix.exp(-(z - mean)**2 /
(2 * sigma**2)) / (sigma * numerix.sqrt(2 * numerix.pi))
def AxiSymetricGaussianImmuneResponse(r, Ra):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz))/numerix.sqrt(2*numerix.pi*2*dz))
return 22 * numerix.exp(-r**2 /
(2 * Ra)) / (numerix.sqrt(2 * numerix.pi * Ra))
def GaussianKernel2D(strength, x, y, Ra):
return strength * numerix.exp(-(
(x**2) + (y**2)) / (2 * Ra)) / (numerix.sqrt(2 * numerix.pi * Ra))
def run(self,
dt=.5e-7,
dtMax=1e+20,
dtMin=.5e-7,
totalSteps=400,
view=False,
PRINT=False,
sweeps=5,
tol=1e-10,
delta=150e-6,
deltaRef=0.03,
featureDepth=56e-6,
i1=-40.,
i0=40.,
diffusionCupric=2.65e-10,
appliedPotential=-0.25,
faradaysConstant=9.6485e4,
gasConstant=8.314,
temperature=298.,
alpha=0.4,
charge=2,
bulkCupric=1000.,
bulkSuppressor=.02,
diffusionSuppressor=9.2e-11,
kappa=15.26,
kPlus=150.,
kMinus=2.45e7,
omega=7.1e-6,
gamma=2.5e-7,
perimeterRatio=1. / 2.8e-6 * 0.093,
areaRatio=0.093,
capacitance=0.3,
dt_mult=1e+10):
r"""
Run an individual simulation.
:Parameters:
- `dt`: time step size
- `dtMax`: maximum time step size
- `dtMin`: minimum time step size
- `totalSteps`: total time steps
- `view`: whether to view the simulation while running
- `PRINT`: print convergence data
- `sweeps`: number of sweeps at each time step
- `tol`: tolerance to exit sweep loop
- `delta`: boundary layer depth
- `deltaRef`: distance to reference electrode
- `featureDepth`: depth of the feature
- `i1`: current density constant
- `i0`: current density constant
- `diffusionCupric`: cupric diffusion
- `appliedPotential`: applied potential
- `faradaysConstant`: Faraday's constant
- `gasConstant`: gas constant
- `temperature`: temperature
- `alpha`: kinetic factor
- `charge`: charge
- `bulkCupric`: bulk cupric concentration
- `bulkSuppressor`: bulk suppressor concentration
- `diffusionSuppressor`: suppressor diffusion
- `kappa`: conductivity
- `kPlus`: suppressor adsorption factor
- `kMinus`: suppressor incorporation factor
- `omega`: copper molar volume
- `gamma`: saturation suppressor coverage,
- `perimeterRatio`: feature perimeter ratio
- `areaRatio`: feature area ratio
- `capacitance`: capacitance
"""
Fbar = faradaysConstant / gasConstant / temperature
self.parameters = locals().copy()
del self.parameters['self']
self.parameters['trenchWidth'] = 2 * 0.093 / perimeterRatio
self.parameters['fieldWidth'] = 2 / perimeterRatio
epsilon = 1e-30
L = delta + featureDepth
N = 1000
dx = L / N
mesh = fipy.Grid1D(nx=N, dx=dx) - [[featureDepth]]
potential = fipy.CellVariable(mesh=mesh, hasOld=True, name=r'$\psi$')
potential[:] = -appliedPotential
cupric = fipy.CellVariable(mesh=mesh, hasOld=True, name=r'$c_{cu}$')
cupric[:] = bulkCupric
cupric.constrain(bulkCupric, mesh.facesRight)
suppressor = fipy.CellVariable(mesh=mesh, hasOld=True, name=r'$c_{\theta}$')
suppressor[:] = bulkSuppressor
suppressor.constrain(bulkSuppressor, mesh.facesRight)
theta = fipy.CellVariable(mesh=mesh, hasOld=True, name=r'$\theta$')
I0 = (i0 + i1 * theta)
baseCurrent = I0 * (numerix.exp(alpha * Fbar * potential) \
- numerix.exp(-(2 - alpha) * Fbar * potential))
cbar = cupric / bulkCupric
current = cbar * baseCurrent
currentDerivative = cbar * I0 * (alpha * Fbar * numerix.exp(alpha * Fbar * potential) \
+ (2 - alpha) * Fbar * numerix.exp(-(2 - alpha) * Fbar * potential))
def dirac(x):
value = numerix.zeros(mesh.numberOfCells, 'd')
ID = numerix.argmin(abs(mesh.x - x))
if mesh.x[ID] < x:
ID = ID + 1
value[ID] = 1. / dx
return value
THETA = (mesh.x < 0) * perimeterRatio + dirac(0) * (1 - areaRatio) + dirac(-featureDepth) * areaRatio
AREA = (mesh.x < 0) * (areaRatio - 1) + 1
THETA_UPPER = fipy.CellVariable(mesh=mesh)
THETA_UPPER[-1] = kappa / dx / (deltaRef - delta)
potentialEq = fipy.TransientTerm(capacitance * THETA) == fipy.DiffusionTerm(kappa * AREA) \
- THETA * (current - potential * currentDerivative) \
- fipy.ImplicitSourceTerm(THETA * currentDerivative) \
- THETA_UPPER * appliedPotential - fipy.ImplicitSourceTerm(THETA_UPPER)
cupricEq = fipy.TransientTerm(AREA) == fipy.DiffusionTerm(diffusionCupric * AREA) \
- fipy.ImplicitSourceTerm(baseCurrent * THETA / (bulkCupric * charge * faradaysConstant))
suppressorEq = fipy.TransientTerm(AREA) == fipy.DiffusionTerm(diffusionSuppressor * AREA) \
- fipy.ImplicitSourceTerm(gamma * kPlus * (1 - theta) * THETA)
thetaEq = fipy.TransientTerm(THETA + epsilon) == kPlus * suppressor * THETA \
- fipy.ImplicitSourceTerm(THETA * (kPlus * suppressor + kMinus * current * (omega / charge / faradaysConstant)))
t = 0.
if view:
potentialBar = -potential / appliedPotential
potentialBar.name = r'$\bar{\eta}$'
cbar.name = r'$\bar{c_{cu}}$'
suppressorBar = suppressor / bulkSuppressor
suppressorBar.name = r'$\bar{c_{\theta}}$'
viewer = fipy.Viewer((theta, suppressorBar, cbar, potentialBar), datamax=1, datamin=0.0, xmin=-featureDepth)
viewer.axes.legend([var.name for var in viewer.vars], loc='lower right', prop={'size':16})
viewer.axes.tick_params(labelsize=14)
viewer.axes.set_xticks((-50e-6, 0e-6, 50e-6, 100e-6, 150e-6))
viewer.axes.set_xticklabels((-50, 0, 50, 100, 150))
viewer.axes.set_xlabel(r'$x$ ($\mu$m)', fontsize=16)
potentials = []
for step in range(totalSteps):
if view:
viewer.axes.set_title(r'$t=%1.2e$ (s)' % t, fontsize=18)
viewer.plot(filename='movie/movie%s.png' % str(step).rjust(6, '0'))
potential.updateOld()
cupric.updateOld()
suppressor.updateOld()
theta.updateOld()
for sweep in range(sweeps):
potentialRes = potentialEq.sweep(potential, dt=dt)
cupricRes = cupricEq.sweep(cupric, dt=dt)
suppressorRes = suppressorEq.sweep(suppressor, dt=dt)
thetaRes = thetaEq.sweep(theta, dt=dt)
res = numpy.array((potentialRes, cupricRes, suppressorRes, thetaRes))
if sweep == 0:
res0 = res
else:
if ((res / res0) < tol).all():
break
if PRINT:
print res / res0
if sweep == sweeps - 1 and PRINT:
print 'Did not reach sufficient tolerance'
print 'kPlus',kPlus
print 'kMinus',kMinus
print 'res',res
if PRINT:
print 'theta',theta[0]
print 'cBar_supp',suppressor[0] / bulkSuppressor
print 'cBar_cu',cupric[0] / bulkCupric
print 'potentialBar',-potential[0] / appliedPotential
print 'dt',dt
print 'step',step
t += dt
dt = dt * dt_mult
dt = min((dt, dtMax))
dt = max((dt, dtMin))
potentials.append(-float(potential[0]))
if view:
viewer.plot()
self.parameters['potential'] = numpy.array(potential)
self.parameters['cupric'] = numpy.array(cupric)
self.parameters['suppressor'] = numpy.array(suppressor)
self.parameters['theta'] = numpy.array(theta)
self.parameters['potentials'] = numpy.array(potentials)
def SigmaF1D(x, Rs):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz**2))/(numerix.sqrt(dz**2)*numerix.sqrt(2*numerix.pi)))
# return 0.00001*numerix.exp(-((x)**2)/(2*Rs))/(numerix.sqrt(2*numerix.pi*Rs))
return 10.*numerix.exp(-((x)**2)/(2*Rs))/(numerix.sqrt(2*numerix.pi*Rs))
def GaussianKernel(strength, x, Ra):
return strength * numerix.exp(-(x**2) / (2 * Ra)) / (numerix.sqrt(
2 * numerix.pi * Ra))
def TwoSigmaF2D(x,y, Rs):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz**2))/(numerix.sqrt(dz**2)*numerix.sqrt(2*numerix.pi)))
return ((200e-3+0.)*numerix.exp(-((x+0.6)**2 + (y-0.3)**2)/(2*Rs))
+(300e-3+0.)*numerix.exp(-((x-0.6)**2 + (y-0.2)**2)/(2*Rs)))/(numerix.sqrt(2*numerix.pi*Rs))
def AxiSymmetricSigmaF(r, Rs):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz**2))/(numerix.sqrt(dz**2)*numerix.sqrt(2*numerix.pi)))
return 10*numerix.exp(-((r)**2)/(2*Rs))/(numerix.sqrt(2*numerix.pi*Rs))
def GaussianImmuneResponse2D(strength, x, y, Ra):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz))/numerix.sqrt(2*numerix.pi*2*dz))
return strength * numerix.exp(-(
(x**2) + (y**2)) / (2 * Ra)) / (numerix.sqrt(2 * numerix.pi * Ra))
def AxiSymmetricGaussianGammaF(r, Rs):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz**2))/(numerix.sqrt(dz**2)*numerix.sqrt(2*numerix.pi)))
return 0.03 * numerix.exp(-(r**2) /
(2 * Rs)) / (numerix.sqrt(2 * numerix.pi * Rs))
def gaussianGammaF(x, Rs):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz**2))/(numerix.sqrt(dz**2)*numerix.sqrt(2*numerix.pi)))
return 0.3 * numerix.exp(-(x**2) /
(2 * Rs)) / (numerix.sqrt(2 * numerix.pi * Rs))
def GaussianConversionRate(r, mean, Rp):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz**2))/(numerix.sqrt(dz**2)*numerix.sqrt(2*numerix.pi)))
return 0.3*numerix.exp(-(r-mean)**2/(2*Rp))/(numerix.sqrt(2*numerix.pi*Rp))
def GaussianConversionRate2D(amp, x,y, mean, Rp):
#Test : plt.plot(r,10*numerix.exp(-r**2/(2*dz**2))/(numerix.sqrt(dz**2)*numerix.sqrt(2*numerix.pi)))
return amp*numerix.exp(-((x-mean)**2 + (y-mean)**2)/(2*Rp))/(numerix.sqrt(2*numerix.pi*Rp))
