def T_1(self, DEBUG = None): # Water entrainment. IN: Q_a,Q_th,Q_cj,Q_v. OUT: Q_vj,Q_th
'''
This function calculates the water entrainment.\
Entrainment is the transport of fluid across an interface\
between two bodies of fluid by a shear induced turbulent flux.
I.e. the 'mixing' of two fluids across their interface. \
'''
# ipdb.set_trace()
g_prime = ma.calc_g_prime(self.Nj, self.Q_cj, self.rho_j, self.rho_a, g=self.g)
if DEBUG is True:
g_prime[:,:] = 0.5
Ri = T1.calc_RichardsonNo(g_prime, self.Q_th, self.Q_v)
# Ri[np.isnan(Ri)] = 0
# Ri[Ri == 0] = np.inf
E_wStar = T1.calc_dimlessIncorporationRate(Ri) # Dimensionless incorporation rate
E_w = T1.calc_rateOfSeaWaterIncorp(self.Q_v, E_wStar) # Rate of seawater incorporation
nQ_th = self.Q_th + T1.calc_changeIn_q_th(E_w, self.dt) # Update cell current thickness
# nQ_th[np.isnan(nQ_th)] = 0
tempQ_cj = T1.calc_new_qcj(self.Q_cj, self.Q_th, nQ_th)
# tempQ_cj[np.isnan(tempQ_cj)] = 0
# if (tempQ_cj.sum() - self.Q_cj.sum() > 1e+03):
# print("break")
self.Q_cj[1:-1, 1:-1] = np.round(tempQ_cj[1:-1, 1:-1],15)
self.Q_th[1:-1, 1:-1] = np.round(nQ_th[1:-1, 1:-1],15)
def calc_MaxRelaxationTime(self): # out: matrix
g_prime = ma.calc_g_prime(self.Nj,
self.Q_cj,
self.rho_j,
self.rho_a,
g=self.g)
r_j = self.calc_RunUpHeight(g_prime)
r_j[r_j == 0] = np.inf
g_prime[g_prime == 0] = np.inf
return (self.dx / 2) / np.sqrt(2 * r_j * g_prime)
def I_3(self): # Should be done
'''
Update of turbidity flow velocity (speed!). IN: Q_a,Q_th,Q_o,Q_cj. OUT: Q_v.
'''
# ipdb.set_trace()
# g_prime = np.ndarray(Ny,Nx)
g_prime = ma.calc_g_prime(self.Nj, self.Q_cj, self.rho_j, self.rho_a)
# print("self.Q_cj=\n",self.Q_cj)
# print("g_prime.shape=",g_prime.shape)
# print("self.Q_cj.shape=",self.Q_cj.shape)
# print("g_prime I_3 = ", g_prime)
# print("g_prime =\n", g_prime)
sum_q_cj = np.sum(self.Q_cj,
axis=2) # TCurrent sediment volume concentration
# # print("sum_q_cj = ", sum_q_cj)
# # q_o = self.Q_o[1:-1,1:-1,:]
# # print("q_o = ", q_o)
# # self.calc_Hdiff()
U_k = np.zeros((self.Ny - 2, self.Nx - 2, 6))
# # diff = self.diff.copy() # TODO! THIS IS WRONG
# # print("diff=\n",diff[:,:,0])
# # diff[np.isinf(diff)] = 0
diff = np.zeros((self.Ny - 2, self.Nx - 2, 6))
for i in range(6):
sum1 = self.Q_a + self.Q_th
diff[:, :, i] = (sum1)[1:-1, 1:-1] - (sum1)[self.NEIGHBOR[i]]
diff[np.isinf(diff)] = 0 # For borders. diff = 0 => U_k = 0. ok.
# diff[diff<0] = 0 # To avoid negative values in np.sqrt()
t1 = np.zeros((6, self.Ny - 2, self.Nx - 2))
for i in range(6):
t1[i, :, :] = diff[:, :, i]
for i in range(6):
comp1 = (8 * g_prime * sum_q_cj)[1:-1, 1:-1] / (self.f *
(1 + self.a))
comp2 = (self.Q_o[1:-1, 1:-1, i] * diff[:, :, i])
temp = np.sqrt(comp1 * comp2)
U_k[:, :, i] = temp
# # print("U_k[:,:,i]=\n",U_k[:,:,i])
self.Q_v[1:-1, 1:-1] = np.nan_to_num(ma.average_speed_hexagon(U_k))
def I_3(self, DEBUG = None): # Should be done
'''
Update of turbidity flow velocity (speed!). IN: Q_a,Q_th,Q_o,Q_cj. OUT: Q_v.
'''
# ipdb.set_trace()
# g_prime = np.ndarray(Ny,Nx)
g_prime = ma.calc_g_prime(self.Nj, self.Q_cj, self.rho_j, self.rho_a)
if DEBUG is True:
g_prime[:,:] = 1
# print("self.Q_cj=\n",self.Q_cj)
# print("g_prime.shape=",g_prime.shape)
# print("self.Q_cj.shape=",self.Q_cj.shape)
# print("g_prime I_3 = ", g_prime)
# print("g_prime =\n", g_prime)
sum_q_cj = np.sum(self.Q_cj, axis=2) # TCurrent sediment volume concentration
# # print("sum_q_cj = ", sum_q_cj)
# # q_o = self.Q_o[1:-1,1:-1,:]
# # print("q_o = ", q_o)
# # self.calc_Hdiff()
U_k = np.zeros((self.Ny - 2, self.Nx - 2, 6))
# # print("diff=\n",diff[:,:,0])
# # diff[np.isinf(diff)] = 0
diff = np.zeros((self.Ny - 2, self.Nx - 2, 6))
sum1 = self.Q_a + self.Q_th
for i in range(6):
diff[:, :, i] = np.abs((sum1)[1:-1, 1:-1] - (sum1)[self.NEIGHBOR[i]])
diff[np.isinf(diff)] = 0 # For borders. diff = 0 => U_k = 0. ok.
# diff[diff<0] = 0 # To avoid negative values in np.sqrt()
for i in range(6):
comp1 = (8 * g_prime * sum_q_cj)[1:-1, 1:-1] / (self.f * (1 + self.a))
comp2 = (self.Q_o[1:-1, 1:-1, i] * diff[:, :, i])
comp2[comp2<0] = 0 # TODO: Test om denne kan fjernes
with np.errstate(invalid='raise'):
temp = np.sqrt(comp1 * comp2)
U_k[:, :, i] = temp
# # print("U_k[:,:,i]=\n",U_k[:,:,i])
self.Q_v[1:-1, 1:-1] = np.round(np.nan_to_num(ma.average_speed_hexagon(U_k)),15)
def I_1(self, DEBUG=None):
'''
This function calculates the turbidity current outflows.\
IN: Q_a,Q_th,Q_v,Q_cj. OUT: Q_o
self.p_f = np.deg2rad(1) # Height threshold friction angle
'''
eligableCells = self.Q_th[1:-1, 1:-1] > 0
# Step (i): angles beta_i
g_prime = ma.calc_g_prime(self.Nj, self.Q_cj, self.rho_j, self.rho_a)
if DEBUG is True:
g_prime[:,:] = 1
# h_k = self.calc_BFroudeNo(g_prime)
g: np.ndarray = g_prime.copy()
g[g == 0] = np.inf
h_k = 0.5 * self.Q_v ** 2 / g
r = self.Q_th + h_k
# print("run up height = \n", r)
central_cell_height = (self.Q_a + r)[1:-1, 1:-1]
q_i = (self.Q_a + self.Q_th)
delta = np.zeros((self.Ny - 2, self.Nx - 2, 6))
delta[:, :, 0] = central_cell_height - q_i[0:self.Ny - 2, 1:self.Nx - 1]
delta[:, :, 1] = central_cell_height - q_i[0:self.Ny - 2, 2:self.Nx]
delta[:, :, 2] = central_cell_height - q_i[1:self.Ny - 1, 2:self.Nx]
delta[:, :, 3] = central_cell_height - q_i[2:self.Ny, 1:self.Nx - 1]
delta[:, :, 4] = central_cell_height - q_i[2:self.Ny, 0:self.Nx - 2]
delta[:, :, 5] = central_cell_height - q_i[1:self.Ny - 1, 0:self.Nx - 2]
delta[np.isinf(delta)] = 0 # q_i is inf at borders. delta = 0 => angle =0 => no transfer
# print("my rank = {0}\nmy delta[:,:,0]=\n{1}".format(self.my_rank,delta[:,:,0]))
# debug_delta = np.zeros((6, self.Ny - 2, self.Nx - 2))
# for i in range(6):
# debug_delta[i, :, :] = delta[:, :, i]
dx = self.dx
angle = ne.evaluate('arctan2(delta, dx)')
# debug_angle = np.zeros((6, self.Ny - 2, self.Nx - 2))
# for i in range(6):
# debug_angle[i, :, :] = angle[:, :, i]
# print("angle =\n", np.rad2deg(angle))
indices = angle > self.p_f # indices(Ny,Nx,6). Dette er basically set A.
indices *= eligableCells[:, :, np.newaxis]
# print("indices\n",indices)
# debug_indices = np.zeros((6, self.Ny - 2, self.Nx - 2))
# for i in range(6):
# debug_indices[i, :, :] = indices[:, :, i]
# Average, indices = I_1_doubleforloop(indices, q_i, r, self.Ny, self.Nx, self.p_adh, self.indexMat) # numba function
for ii in range(6): # Step (iii) says to go back to step (ii) if a cell is removed.
NumberOfCellsInA = np.sum(indices, axis=2) # Cardinality of set A
# print("NumberOfCellsInA =\n",NumberOfCellsInA)
# Step (ii) calculate average
neighborValues = np.zeros((self.Ny - 2, self.Nx - 2))
# print("neighbors=\n", self.NEIGHBOR[0])
for i in range(6):
q_i_nb = q_i[self.NEIGHBOR[i]]
with np.errstate(invalid='ignore'):
indices_for_ne = indices[:, :, i]
neighborValues_thread = q_i_nb * indices_for_ne
# neighborValues_thread = vectorMultiply(q_i_nb, indices_for_ne)
neighborValues_thread[np.isnan(neighborValues_thread)] = 0
neighborValues += neighborValues_thread
# neighborValues += np.nan_to_num(q_i_nb * indices[:, :,i])
# Vi vil bare legge til verdier hvor angle>self.p_f
# print("neighborValues=\n", neighborValues)
p = (r - self.p_adh)[1:-1, 1:-1]
# p[p<0]=0
with np.errstate(divide='ignore', invalid='ignore'):
Average = ne.evaluate('(p + neighborValues) / NumberOfCellsInA')
Average[np.isinf(Average)] = 0 # for når NumberOfCellsInA =0
Average[np.isnan(Average)] = 0
# print("Average=\n", Average)
# print("indices=\n", indices)
# Step (iii) Eliminate adjacent cells i with q_i >= Average from A.
for i in range(6): # Skal sette posisjoner (j) hvor q_i (til nabocelle) > average (i celle j) til 0
nb = q_i[self.NEIGHBOR[i]]
itemp = (nb >= Average)
indices[itemp, i] = 0
# Step (iv)
Average[np.isinf(Average)] = 0
nonNormalizedOutFlow = np.ones((Average.shape + (6,))) * Average[:, :, np.newaxis]
for i in range(6):
with np.errstate(invalid='ignore'):
nonNormalizedOutFlow[:, :, i] -= np.nan_to_num(q_i[self.NEIGHBOR[i]] * indices[:, :, i])
nonNormalizedOutFlow *= indices
# Step (v)
with np.errstate(divide='ignore', invalid='ignore'):
normalization = self.Q_th / r # nu_nf
normalization[np.isnan(normalization)] = 0
# print("normalization=\n", normalization)
with np.errstate(invalid='ignore'):
relaxation = np.sqrt(2 * r * g_prime) * self.dt / (self.dx/2) # dx is intercellular distance. apothem is used in Salles.
relaxation[relaxation >0.8] = 0.8 # Demper outflow litt
if np.any(relaxation > 1) or np.any(relaxation < 0): # D'Ambrosio demand
raise Exception("Warning! I_1 Relaxation > 1!")
factor = (normalization * relaxation)[1:-1, 1:-1]
self.Q_o[1:-1, 1:-1,:] = np.round(np.nan_to_num(factor[:,:,None] * nonNormalizedOutFlow), 15)
if ((np.sum(self.Q_o, axis=2) > self.Q_th)[1:-1,1:-1].sum() > 0):
ii, jj = np.where(np.sum(self.Q_o,axis=2)>self.Q_th)
s = ''.join("\nsum(Q_o[%i,%i]) = %.10f > Q_th = %.10f\n" % (ii[x],jj[x],sum(self.Q_o[ii[x],jj[x]]),self.Q_th[ii[x],jj[x]]) for x in range(len(ii)))
s = s + ''.join("Relaxation = %03f, Normalization = %03f\n" %(relaxation[ii[x],jj[x]], normalization[ii[x],jj[x]]) for x in range(len(ii)))
raise Exception("I_1 warning! More outflow Q_o than thickness Q_th!" + s)
def I_1(self): # TODO Tror det er feil her!!
'''
This function calculates the turbidity current outflows.\
IN: Q_a,Q_th,Q_v,Q_cj. OUT: Q_o
self.p_f = np.deg2rad(1) # Height threshold friction angle
'''
eligableCells = self.Q_th[1:-1, 1:-1] > 0
# Step (i): angles beta_i
g_prime = ma.calc_g_prime(self.Nj, self.Q_cj, self.rho_j, self.rho_a)
h_k = self.calc_BFroudeNo(g_prime)
r = self.Q_th + h_k
# print("run up height = \n", r)
central_cell_height = (self.Q_a + r)[1:-1, 1:-1]
q_i = (self.Q_a + self.Q_th)
delta = np.zeros((self.Ny - 2, self.Nx - 2, 6))
for i in range(6):
delta[:, :, i] = central_cell_height - q_i[self.NEIGHBOR[i]]
delta[np.isinf(
delta
)] = 0 # q_i is inf at borders. delta = 0 => angle =0 => no transfer
debug_delta = np.zeros((6, self.Ny - 2, self.Nx - 2))
for i in range(6):
debug_delta[i, :, :] = delta[:, :, i]
angle = np.arctan2(delta, self.dx)
debug_angle = np.zeros((6, self.Ny - 2, self.Nx - 2))
for i in range(6):
debug_angle[i, :, :] = angle[:, :, i]
# print("angle =\n", np.rad2deg(angle))
indices = angle > self.p_f # indices(Ny,Nx,6). Dette er basically set A.
indices *= eligableCells[:, :, np.newaxis]
# print("indices\n",indices)
debug_indices = np.zeros((6, self.Ny - 2, self.Nx - 2))
for i in range(6):
debug_indices[i, :, :] = indices[:, :, i]
for ii in range(
6
): # Step (iii) says to go back to step (ii) if a cell is removed.
NumberOfCellsInA = np.sum(indices, axis=2) # Cardinality of set A
# print("NumberOfCellsInA =\n",NumberOfCellsInA)
# Step (ii) calculate average
neighborValues = np.zeros((self.Ny - 2, self.Nx - 2))
# print("neighbors=\n", self.NEIGHBOR[0])
for i in range(6):
q_i_nb = q_i[self.NEIGHBOR[i]]
neighborValues += np.nan_to_num(
q_i_nb * indices[:, :, i]
) # Vi vil bare legge til verdier hvor angle>self.p_f
# print("neighborValues=\n", neighborValues)
p = (r - self.p_adh)[1:-1, 1:-1]
# p[p<0]=0
with np.errstate(divide='ignore', invalid='ignore'):
Average = (p + neighborValues) / NumberOfCellsInA
Average[np.isinf(Average)] = 0 # for når NumberOfCellsInA =0
Average[np.isnan(Average)] = 0
# print("Average=\n", Average)
# print("indices=\n", indices)
# Step (iii) Eliminate adjacent cells i with q_i >= Average from A.
for i in range(
6
): # Skal sette posisjoner (j) hvor q_i (til nabocelle) > average (i celle j) til 0
nb = q_i[self.NEIGHBOR[i]]
itemp = (nb >= Average)
indices[itemp, i] = 0
for i in range(6):
debug_indices[i, :, :] = indices[:, :, i]
# Step (iv)
Average[np.isinf(Average)] = 0 # TODO Testing!
nonNormalizedOutFlow = np.ones(
(Average.shape + (6, ))) * Average[:, :, np.newaxis]
for i in range(6):
nonNormalizedOutFlow[:, :, i] -= np.nan_to_num(
q_i[self.NEIGHBOR[i]] * indices[:, :, i])
nonNormalizedOutFlow *= indices
# Step (v)
with np.errstate(divide='ignore', invalid='ignore'):
normalization = self.Q_th / r # nu_nf
# print("normalization=\n", normalization)
with np.errstate(invalid='ignore'):
relaxation = np.sqrt(2 * r * g_prime) * self.dt / (0.5 * self.dx)
if (relaxation.any() > 1):
print("Warning! Relaxation > 1!")
for i in range(6):
self.Q_o[1:-1, 1:-1, i] = np.nan_to_num(
(normalization * relaxation)[1:-1, 1:-1] *
nonNormalizedOutFlow[:, :, i])
if ((np.sum(self.Q_o, axis=2) > self.Q_th).sum() > 0):
print("more outflow than thickness!")