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Commit 60d6185f authored by David Seus's avatar David Seus
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fix weird git fuckug

parent 03ca7a10
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...@@ -7,11 +7,36 @@ import typing as tp ...@@ -7,11 +7,36 @@ import typing as tp
import domainPatch as dp import domainPatch as dp
import LDDsimulation as ldd import LDDsimulation as ldd
import functools as ft import functools as ft
import helpers as hlp
#import ufl as ufl #import ufl as ufl
# init sympy session # init sympy session
sym.init_printing() sym.init_printing()
use_case = "TP-R-two-patch"
solver_tol = 5E-7
############ GRID #######################ü
mesh_resolution = 40
timestep_size = 0.000001
number_of_timesteps = 15
# decide how many timesteps you want analysed. Analysed means, that we write out
# subsequent errors of the L-iteration within the timestep.
number_of_timesteps_to_analyse = 10
starttime = 0
Lw = 5 #/timestep_size
Lnw=Lw
l_param_w = 100
l_param_nw = 100
include_gravity = True
debugflag = True
analyse_condition = False
output_string = "./output/after_reimplementing_gravity_term_number_of_timesteps{}_".format(number_of_timesteps)
##### Domain and Interface #### ##### Domain and Interface ####
# global simulation domain domain # global simulation domain domain
sub_domain0_vertices = [df.Point(-1.0, -1.0), sub_domain0_vertices = [df.Point(-1.0, -1.0),
...@@ -80,53 +105,44 @@ isRichards = { ...@@ -80,53 +105,44 @@ isRichards = {
} }
############ GRID #######################ü
mesh_resolution = 50
timestep_size = 0.01
number_of_timesteps = 160
# decide how many timesteps you want analysed. Analysed means, that we write out
# subsequent errors of the L-iteration within the timestep.
number_of_timesteps_to_analyse = 11
starttime = 0
viscosity = {# viscosity = {#
# subdom_num : viscosity # subdom_num : viscosity
1 : {'wetting' :1}, 1 : {'wetting' :1},
#'nonwetting': 1}, # #'nonwetting': 1}, #
2 : {'wetting' :1, 2 : {'wetting' :1,
'nonwetting': 1} 'nonwetting': 1/50}
} }
porosity = {# porosity = {#
# subdom_num : porosity # subdom_num : porosity
1 : 1,#0.22,# 1 : 0.22,#
2 : 1#0.022 2 : 0.0022
} }
# Dict of the form: { subdom_num : density } # Dict of the form: { subdom_num : density }
densities = { densities = {
1: {'wetting': 1}, 1: {'wetting': 997},
2: {'wetting': 1, 2: {'wetting': 997,
'nonwetting': 1}, 'nonwetting': 1.225},
} }
gravity_acceleration = 9.81 gravity_acceleration = 9.81
L = {# L = {#
# subdom_num : subdomain L for L-scheme # subdom_num : subdomain L for L-scheme
1 : {'wetting' :0.25}, 1 : {'wetting' :Lw},
# 'nonwetting': 0.25},# # 'nonwetting': 0.25},#
2 : {'wetting' :0.25, 2 : {'wetting' :Lw,
'nonwetting': 0.25} 'nonwetting': Lnw}
} }
l_param = 40
lambda_param = {# lambda_param = {#
# subdom_num : lambda parameter for the L-scheme # subdom_num : lambda parameter for the L-scheme
1 : {'wetting' :l_param}, 1 : {'wetting' :l_param_w},
# 'nonwetting': l_param},# # 'nonwetting': l_param},#
2 : {'wetting' :l_param, 2 : {'wetting' :l_param_w,
'nonwetting': l_param} 'nonwetting': l_param_nw}
} }
## relative permeabilty functions on subdomain 1 ## relative permeabilty functions on subdomain 1
...@@ -185,7 +201,7 @@ def rel_perm2w_prime(s): ...@@ -185,7 +201,7 @@ def rel_perm2w_prime(s):
def rel_perm2nw_prime(s): def rel_perm2nw_prime(s):
# relative permeabilty on subdomain1 # relative permeabilty on subdomain1
return 3*(1-s)**2 return -3*(1-s)**2
_rel_perm1w_prime = ft.partial(rel_perm1w_prime) _rel_perm1w_prime = ft.partial(rel_perm1w_prime)
# _rel_perm1nw_prime = ft.partial(rel_perm1nw_prime) # _rel_perm1nw_prime = ft.partial(rel_perm1nw_prime)
...@@ -307,87 +323,44 @@ x, y = sym.symbols('x[0], x[1]') # needed by UFL ...@@ -307,87 +323,44 @@ x, y = sym.symbols('x[0], x[1]') # needed by UFL
t = sym.symbols('t', positive=True) t = sym.symbols('t', positive=True)
p_e_sym = { p_e_sym = {
1: {'wetting': 1.0 - (1.0 + t*t)*(1.0 + x*x + y*y)}, 1: {'wetting': (-5.0 - (1.0 + t*t)*(1.0 + x*x + y*y))}, #*(1-x)**2*(1+x)**2*(1-y)**2},
2: {'wetting': 1.0 - (1.0 + t*t)*(1.0 + x*x), 2: {'wetting': (-5.0 - (1.0 + t*t)*(1.0 + x*x)), #*(1-x)**2*(1+x)**2*(1+y)**2,
'nonwetting': (-t*(1-y - x**2)**2 - sym.sqrt(2+t**2))*y}, 'nonwetting': (-1-t*(1.1+y + x**2))*y**2}, #*(1-x)**2*(1+x)**2*(1+y)**2},
} #-y*y*(sym.sin(-2*t+2*x)*sym.sin(1/2*y-1.2*t)) - t*t*x*(0.5-y)*y*(1-x) } #-y*y*(sym.sin(-2*t+2*x)*sym.sin(1/2*y-1.2*t)) - t*t*x*(0.5-y)*y*(1-x)
pc_e_sym = { pc_e_sym = dict()
1: -1*p_e_sym[1]['wetting'],
2: p_e_sym[2]['nonwetting'] - p_e_sym[2]['wetting']
}
# turn above symbolic code into exact solution for dolphin and
# construct the rhs that matches the above exact solution.
dtS = dict()
div_flux = dict()
source_expression = dict()
exact_solution = dict()
initial_condition = dict()
for subdomain, isR in isRichards.items(): for subdomain, isR in isRichards.items():
dtS.update({subdomain: dict()})
div_flux.update({subdomain: dict()})
source_expression.update({subdomain: dict()})
exact_solution.update({subdomain: dict()})
initial_condition.update({subdomain: dict()})
if isR: if isR:
subdomain_has_phases = ["wetting"] pc_e_sym.update({subdomain: -p_e_sym[subdomain]['wetting'].copy()})
else: else:
subdomain_has_phases = ["wetting", "nonwetting"] pc_e_sym.update({subdomain: p_e_sym[subdomain]['nonwetting'].copy()
- p_e_sym[subdomain]['wetting'].copy()})
# conditional for S_pc_prime
pc = pc_e_sym[subdomain]
dtpc = sym.diff(pc, t, 1) symbols = {"x": x,
dxpc = sym.diff(pc, x, 1) "y": y,
dypc = sym.diff(pc, y, 1) "t": t}
S = sym.Piecewise((S_pc_sym[subdomain](pc), pc > 0), (1, True)) # turn above symbolic code into exact solution for dolphin and
dS = sym.Piecewise((S_pc_sym_prime[subdomain](pc), pc > 0), (0, True)) # construct the rhs that matches the above exact solution.
for phase in subdomain_has_phases: exact_solution_example = hlp.generate_exact_solution_expressions(
# Turn above symbolic expression for exact solution into c code symbols=symbols,
exact_solution[subdomain].update( isRichards=isRichards,
{phase: sym.printing.ccode(p_e_sym[subdomain][phase])} symbolic_pressure=p_e_sym,
) symbolic_capillary_pressure=pc_e_sym,
# save the c code for initial conditions saturation_pressure_relationship=S_pc_sym,
initial_condition[subdomain].update( saturation_pressure_relationship_prime=S_pc_sym_prime,
{phase: sym.printing.ccode(p_e_sym[subdomain][phase].subs(t, 0))} viscosity=viscosity,
) porosity=porosity,
if phase == "nonwetting": relative_permeability=relative_permeability,
dtS[subdomain].update( relative_permeability_prime=ka_prime,
{phase: -porosity[subdomain]*dS*dtpc} densities=densities,
) gravity_acceleration=gravity_acceleration,
else: include_gravity=include_gravity,
dtS[subdomain].update( )
{phase: porosity[subdomain]*dS*dtpc} source_expression = exact_solution_example['source']
) exact_solution = exact_solution_example['exact_solution']
pa = p_e_sym[subdomain][phase] initial_condition = exact_solution_example['initial_condition']
dxpa = sym.diff(pa, x, 1)
dxdxpa = sym.diff(pa, x, 2)
dypa = sym.diff(pa, y, 1)
dydypa = sym.diff(pa, y, 2)
mu = viscosity[subdomain][phase]
ka = relative_permeability[subdomain][phase]
dka = ka_prime[subdomain][phase]
rho = densities[subdomain][phase]
g = gravity_acceleration
if phase == "nonwetting":
# x part of div(flux) for nonwetting
dxdxflux = -1/mu*dka(1-S)*dS*dxpc*dxpa + 1/mu*dxdxpa*ka(1-S)
# y part of div(flux) for nonwetting
dydyflux = -1/mu*dka(1-S)*dS*dypc*(dypa - rho*g) \
+ 1/mu*dydypa*ka(1-S)
else:
# x part of div(flux) for wetting
dxdxflux = 1/mu*dka(S)*dS*dxpc*dxpa + 1/mu*dxdxpa*ka(S)
# y part of div(flux) for wetting
dydyflux = 1/mu*dka(S)*dS*dypc*(dypa - rho*g) + 1/mu*dydypa*ka(S)
div_flux[subdomain].update({phase: dxdxflux + dydyflux})
contructed_rhs = dtS[subdomain][phase] - div_flux[subdomain][phase]
source_expression[subdomain].update(
{phase: sym.printing.ccode(contructed_rhs)}
)
# print(f"source_expression[{subdomain}][{phase}] =", source_expression[subdomain][phase])
# Dictionary of dirichlet boundary conditions. # Dictionary of dirichlet boundary conditions.
dirichletBC = dict() dirichletBC = dict()
...@@ -431,8 +404,9 @@ write_to_file = { ...@@ -431,8 +404,9 @@ write_to_file = {
# initialise LDD simulation class # initialise LDD simulation class
simulation = ldd.LDDsimulation(tol = 1E-14, LDDsolver_tol = 1E-7, debug = False) simulation = ldd.LDDsimulation(tol = 1E-14, LDDsolver_tol=solver_tol, debug=debugflag)
simulation.set_parameters(output_dir = "./output/",# simulation.set_parameters(use_case=use_case,
output_dir = output_string,#
subdomain_def_points = subdomain_def_points,# subdomain_def_points = subdomain_def_points,#
isRichards = isRichards,# isRichards = isRichards,#
interface_def_points = interface_def_points,# interface_def_points = interface_def_points,#
...@@ -454,10 +428,10 @@ simulation.set_parameters(output_dir = "./output/",# ...@@ -454,10 +428,10 @@ simulation.set_parameters(output_dir = "./output/",#
dirichletBC_expression_strings = dirichletBC,# dirichletBC_expression_strings = dirichletBC,#
exact_solution = exact_solution,# exact_solution = exact_solution,#
densities=densities, densities=densities,
include_gravity=True, include_gravity=include_gravity,
write2file = write_to_file,# write2file = write_to_file,#
) )
simulation.initialise() simulation.initialise()
# simulation.write_exact_solution_to_xdmf() # simulation.write_exact_solution_to_xdmf()
simulation.run() simulation.run(analyse_condition=analyse_condition)
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