diff --git a/Two-phase-Richards/multi-patch/layered_soil/TP-R-layered_soil-g-but-same-perm-coarse-dt-longterm.py b/Two-phase-Richards/multi-patch/layered_soil/TP-R-layered_soil-g-but-same-perm-coarse-dt-longterm.py
new file mode 100644
index 0000000000000000000000000000000000000000..c41f753444423526f6ce50901918730e81f7ea9a
--- /dev/null
+++ b/Two-phase-Richards/multi-patch/layered_soil/TP-R-layered_soil-g-but-same-perm-coarse-dt-longterm.py
@@ -0,0 +1,798 @@
+#!/usr/bin/python3
+"""Layered soil simulation.
+
+This program sets up an LDD simulation
+"""
+
+import dolfin as df
+import sympy as sym
+import functools as ft
+import LDDsimulation as ldd
+import helpers as hlp
+import datetime
+import os
+import pandas as pd
+
+# check if output directory exists
+if not os.path.exists('./output'):
+ os.mkdir('./output')
+ print("Directory ", './output', " created ")
+else:
+ print("Directory ", './output', " already exists. Will use as output \
+ directory")
+
+date = datetime.datetime.now()
+datestr = date.strftime("%Y-%m-%d")
+
+# init sympy session
+sym.init_printing()
+# solver_tol = 6E-7
+use_case = "TP-R-layered-soil-realistic-g-same-intrinsic-perm"
+# name of this very file. Needed for log output.
+thisfile = "TP-R-layered_soil-g-but-same-perm-coarse-dt-longterm.py"
+
+max_iter_num = 1000
+FEM_Lagrange_degree = 1
+mesh_study = False
+resolutions = {
+ # 1: 9e-6, # h=2
+ # 2: 9e-6, # h=1.1180
+ # 4: 9e-6, # h=0.5590
+ # 8: 9e-6, # h=0.2814
+ # 16: 5e-6, # h=0.1412
+ 32: 5e-6,
+ # 64: 1e-6,
+ # 128: 1e-6
+ }
+
+# GRID #######################
+# mesh_resolution = 20
+timestep_size = 0.01
+number_of_timesteps = 400
+plot_timestep_every = 1
+# 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 = 5
+starttimes = [0.0]
+
+Lw1 = 0.025 # /timestep_size
+Lnw1 = Lw1
+Lw2 = 0.025 # /timestep_size
+Lnw2 = Lw2
+Lw3 = 0.025 # /timestep_size
+Lnw3 = Lw3
+Lw4 = 0.025 # /timestep_size
+Lnw4 = Lw4
+
+lambda12_w = 4
+lambda12_nw = 4
+lambda23_w = 4
+lambda23_nw = 4
+lambda34_w = 4
+lambda34_nw = 4
+
+include_gravity = True
+debugflag = False
+analyse_condition = False
+
+
+output_string = "./output/{}-{}_timesteps{}_P{}".format(
+ datestr, use_case, number_of_timesteps, FEM_Lagrange_degree
+ )
+
+# toggle what should be written to files
+if mesh_study:
+ write_to_file = {
+ 'space_errornorms': True,
+ 'meshes_and_markers': True,
+ 'L_iterations_per_timestep': False,
+ 'solutions': True,
+ 'absolute_differences': True,
+ 'condition_numbers': analyse_condition,
+ 'subsequent_errors': True
+ }
+else:
+ write_to_file = {
+ 'space_errornorms': True,
+ 'meshes_and_markers': True,
+ 'L_iterations_per_timestep': False,
+ 'solutions': True,
+ 'absolute_differences': True,
+ 'condition_numbers': analyse_condition,
+ 'subsequent_errors': True
+ }
+
+
+# global domain
+subdomain0_vertices = [
+ df.Point(-1.0, -1.0),
+ df.Point(1.0, -1.0),
+ df.Point(1.0, 1.0),
+ df.Point(-1.0, 1.0)
+ ]
+
+
+interface12_vertices = [df.Point(-1.0, 0.8),
+ df.Point(0.3, 0.8),
+ df.Point(0.5, 0.9),
+ df.Point(0.8, 0.7),
+ df.Point(1.0, 0.65)]
+# subdomain1.
+subdomain1_vertices = [
+ interface12_vertices[0],
+ interface12_vertices[1],
+ interface12_vertices[2],
+ interface12_vertices[3],
+ interface12_vertices[4], # southern boundary, 12 interface
+ subdomain0_vertices[2], # eastern boundary, outer boundary
+ subdomain0_vertices[3] # northern boundary, outer on_boundary
+ ]
+
+
+# vertex coordinates of the outer boundaries. If it can not be specified as a
+# polygon, use an entry per boundary polygon. This information is used for
+# defining the Dirichlet boundary conditions. If a domain is completely
+# internal, the dictionary entry should be 0: None
+subdomain1_outer_boundary_verts = {
+ 0: [interface12_vertices[4],
+ subdomain0_vertices[2], # eastern boundary, outer boundary
+ subdomain0_vertices[3],
+ interface12_vertices[0]]
+}
+
+
+# interface23
+interface23_vertices = [
+ df.Point(-1.0, 0.0),
+ df.Point(-0.35, 0.0),
+ # df.Point(6.5, 4.5),
+ df.Point(0.0, 0.0),
+ df.Point(0.5, 0.0),
+ # df.Point(11.5, 3.5),
+ # df.Point(13.0, 3)
+ df.Point(0.85, 0.0),
+ df.Point(1.0, 0.0)
+ ]
+
+# subdomain1
+subdomain2_vertices = [
+ interface23_vertices[0],
+ interface23_vertices[1],
+ interface23_vertices[2],
+ interface23_vertices[3],
+ interface23_vertices[4],
+ interface23_vertices[5], # southern boundary, 23 interface
+ subdomain1_vertices[4], # eastern boundary, outer boundary
+ subdomain1_vertices[3],
+ subdomain1_vertices[2],
+ subdomain1_vertices[1],
+ subdomain1_vertices[0] # northern boundary, 12 interface
+ ]
+
+subdomain2_outer_boundary_verts = {
+ 0: [interface23_vertices[5],
+ subdomain1_vertices[4]],
+ 1: [subdomain1_vertices[0],
+ interface23_vertices[0]]
+}
+
+
+# interface34
+interface34_vertices = [df.Point(-1.0, -0.6),
+ df.Point(-0.6, -0.45),
+ df.Point(0.3, -0.25),
+ df.Point(0.65, -0.6),
+ df.Point(1.0, -0.7)]
+
+# subdomain3
+subdomain3_vertices = [
+ interface34_vertices[0],
+ interface34_vertices[1],
+ interface34_vertices[2],
+ interface34_vertices[3],
+ interface34_vertices[4], # southern boundary, 34 interface
+ subdomain2_vertices[5], # eastern boundary, outer boundary
+ subdomain2_vertices[4],
+ subdomain2_vertices[3],
+ subdomain2_vertices[2],
+ subdomain2_vertices[1],
+ subdomain2_vertices[0] # northern boundary, 23 interface
+ ]
+
+subdomain3_outer_boundary_verts = {
+ 0: [interface34_vertices[4],
+ subdomain2_vertices[5]],
+ 1: [subdomain2_vertices[0],
+ interface34_vertices[0]]
+}
+
+# subdomain4
+subdomain4_vertices = [
+ subdomain0_vertices[0],
+ subdomain0_vertices[1], # southern boundary, outer boundary
+ subdomain3_vertices[4], # eastern boundary, outer boundary
+ subdomain3_vertices[3],
+ subdomain3_vertices[2],
+ subdomain3_vertices[1],
+ subdomain3_vertices[0]
+ ] # northern boundary, 34 interface
+
+
+subdomain4_outer_boundary_verts = {
+ 0: [subdomain4_vertices[6],
+ subdomain4_vertices[0],
+ subdomain4_vertices[1],
+ subdomain4_vertices[2]]
+}
+
+
+subdomain_def_points = [
+ subdomain0_vertices,
+ subdomain1_vertices,
+ subdomain2_vertices,
+ subdomain3_vertices,
+ subdomain4_vertices
+ ]
+
+# interface_vertices introduces a global numbering of interfaces.
+interface_def_points = [
+ interface12_vertices, interface23_vertices, interface34_vertices
+ ]
+
+adjacent_subdomains = [[1, 2], [2, 3], [3, 4]]
+
+# if a subdomain has no outer boundary write None instead, i.e.
+# i: None
+# if i is the index of the inner subdomain.
+outer_boundary_def_points = {
+ # subdomain number
+ 1: subdomain1_outer_boundary_verts,
+ 2: subdomain2_outer_boundary_verts,
+ 3: subdomain3_outer_boundary_verts,
+ 4: subdomain4_outer_boundary_verts
+}
+
+isRichards = {
+ 1: True,
+ 2: True,
+ 3: False,
+ 4: False
+ }
+
+
+# Dict of the form: { subdom_num : viscosity }
+viscosity = {
+ 1: {'wetting': 1,
+ 'nonwetting': 1/50},
+ 2: {'wetting': 1,
+ 'nonwetting': 1/50},
+ 3: {'wetting': 1,
+ 'nonwetting': 1/50},
+ 4: {'wetting': 1,
+ 'nonwetting': 1/50},
+}
+
+# Dict of the form: { subdom_num : density }
+# densities = {
+# 1: {'wetting': 9.97, # 997
+# 'nonwetting': 0.01225}, # 1.225}},
+# 2: {'wetting': 9.97, # 997
+# 'nonwetting': 0.01225}, # 1.225}},
+# 3: {'wetting': 9.97, # 997
+# 'nonwetting': 0.01225}, # 1.225}},
+# 4: {'wetting': 9.97, # 997
+# 'nonwetting': 0.01225}, # 1.225}}
+# }
+
+densities = {
+ 1: {'wetting': 997, # 997
+ 'nonwetting': 1.225}, # 1.225}},
+ 2: {'wetting': 997, # 997
+ 'nonwetting': 1.225}, # 1.225}},
+ 3: {'wetting': 997, # 997
+ 'nonwetting': 1.225}, # 1.225}},
+ 4: {'wetting': 997, # 997
+ 'nonwetting': 1.225}, # 1.225}}
+}
+
+gravity_acceleration = 9.81
+# porosities taken from
+# https://www.geotechdata.info/parameter/soil-porosity.html
+# Dict of the form: { subdom_num : porosity }
+porosity = {
+ 1: 0.37, # 0.2, # Clayey gravels, clayey sandy gravels
+ 2: 0.22, # 0.22, # Silty gravels, silty sandy gravels
+ 3: 0.2, # 0.37, # Clayey sands
+ 4: 0.22, # 0.2 # Silty or sandy clay
+}
+
+# subdom_num : subdomain L for L-scheme
+L = {
+ 1: {'wetting': Lw1,
+ 'nonwetting': Lnw1},
+ 2: {'wetting': Lw2,
+ 'nonwetting': Lnw2},
+ 3: {'wetting': Lw3,
+ 'nonwetting': Lnw3},
+ 4: {'wetting': Lw4,
+ 'nonwetting': Lnw4}
+}
+
+# interface_num : lambda parameter for the L-scheme on that interface.
+# Note that interfaces are numbered starting from 0, because
+# adjacent_subdomains is a list and not a dict. Historic fuckup, I know
+lambda_param = {
+ 0: {'wetting': lambda12_w,
+ 'nonwetting': lambda12_nw},
+ 1: {'wetting': lambda23_w,
+ 'nonwetting': lambda23_nw},
+ 2: {'wetting': lambda34_w,
+ 'nonwetting': lambda34_nw},
+}
+# lambda_param = {
+# 1: {'wetting': lambda_w,
+# 'nonwetting': lambda_nw},
+# 2: {'wetting': lambda_w,
+# 'nonwetting': lambda_nw},
+# 3: {'wetting': lambda_w,
+# 'nonwetting': lambda_nw},
+# 4: {'wetting': lambda_w,
+# 'nonwetting': lambda_nw},
+# }
+
+# after Lewis, see pdf file
+intrinsic_permeability = {
+ 1: 0.01, # sand
+ 2: 0.01, # sand, there is a range
+ 3: 0.01, # 10e-2, # clay has a range
+ 4: 0.01, # 10e-3
+}
+
+
+# relative permeabilty functions on subdomain 1
+def rel_perm1w(s):
+ # relative permeabilty wetting on subdomain1
+ return intrinsic_permeability[1]*s**2
+
+
+def rel_perm1nw(s):
+ # relative permeabilty nonwetting on subdomain1
+ return intrinsic_permeability[1]*(1-s)**2
+
+
+# relative permeabilty functions on subdomain 2
+def rel_perm2w(s):
+ # relative permeabilty wetting on subdomain2
+ return intrinsic_permeability[2]*s**2
+
+
+def rel_perm2nw(s):
+ # relative permeabilty nonwetting on subdomain2
+ return intrinsic_permeability[2]*(1-s)**2
+
+
+# relative permeabilty functions on subdomain 3
+def rel_perm3w(s):
+ # relative permeabilty wetting on subdomain3
+ return intrinsic_permeability[3]*s**3
+
+
+def rel_perm3nw(s):
+ # relative permeabilty nonwetting on subdomain3
+ return intrinsic_permeability[3]*(1-s)**3
+
+
+# relative permeabilty functions on subdomain 4
+def rel_perm4w(s):
+ # relative permeabilty wetting on subdomain4
+ return intrinsic_permeability[4]*s**3
+
+
+def rel_perm4nw(s):
+ # relative permeabilty nonwetting on subdomain4
+ return intrinsic_permeability[4]*(1-s)**3
+
+_rel_perm1w = ft.partial(rel_perm1w)
+_rel_perm1nw = ft.partial(rel_perm1nw)
+_rel_perm2w = ft.partial(rel_perm2w)
+_rel_perm2nw = ft.partial(rel_perm2nw)
+
+_rel_perm3w = ft.partial(rel_perm3w)
+_rel_perm3nw = ft.partial(rel_perm3nw)
+_rel_perm4w = ft.partial(rel_perm4w)
+_rel_perm4nw = ft.partial(rel_perm4nw)
+
+
+subdomain1_rel_perm = {
+ 'wetting': _rel_perm1w,
+ 'nonwetting': _rel_perm1nw
+}
+
+subdomain2_rel_perm = {
+ 'wetting': _rel_perm2w,
+ 'nonwetting': _rel_perm2nw
+}
+
+subdomain3_rel_perm = {
+ 'wetting': _rel_perm3w,
+ 'nonwetting': _rel_perm3nw
+}
+
+subdomain4_rel_perm = {
+ 'wetting': _rel_perm4w,
+ 'nonwetting': _rel_perm4nw
+}
+
+# dictionary of relative permeabilties on all domains.
+# relative_permeability = {
+# 1: subdomain1_rel_perm,
+# 2: subdomain1_rel_perm,
+# 3: subdomain2_rel_perm,
+# 4: subdomain2_rel_perm
+# }
+relative_permeability = {
+ 1: subdomain1_rel_perm,
+ 2: subdomain2_rel_perm,
+ 3: subdomain3_rel_perm,
+ 4: subdomain4_rel_perm
+}
+
+
+# definition of the derivatives of the relative permeabilities
+# relative permeabilty functions on subdomain 1
+def rel_perm1w_prime(s):
+ # relative permeabilty on subdomain1
+ return intrinsic_permeability[1]*2*s
+
+
+def rel_perm1nw_prime(s):
+ # relative permeabilty on subdomain1
+ return -1*intrinsic_permeability[1]*2*(1-s)
+
+
+def rel_perm2w_prime(s):
+ # relative permeabilty on subdomain2
+ return intrinsic_permeability[2]*2*s
+
+
+def rel_perm2nw_prime(s):
+ # relative permeabilty on subdomain2
+ return -1*intrinsic_permeability[2]*2*(1-s)
+
+
+# definition of the derivatives of the relative permeabilities
+# relative permeabilty functions on subdomain 3
+def rel_perm3w_prime(s):
+ # relative permeabilty on subdomain3
+ return intrinsic_permeability[3]*3*s**2
+
+
+def rel_perm3nw_prime(s):
+ # relative permeabilty on subdomain3
+ return -1*intrinsic_permeability[3]*3*(1-s)**2
+
+
+# definition of the derivatives of the relative permeabilities
+# relative permeabilty functions on subdomain 4
+def rel_perm4w_prime(s):
+ # relative permeabilty on subdomain4
+ return intrinsic_permeability[4]*3*s**2
+
+
+def rel_perm4nw_prime(s):
+ # relative permeabilty on subdomain4
+ return -1*intrinsic_permeability[4]*3*(1-s)**2
+
+
+_rel_perm1w_prime = ft.partial(rel_perm1w_prime)
+_rel_perm1nw_prime = ft.partial(rel_perm1nw_prime)
+_rel_perm2w_prime = ft.partial(rel_perm2w_prime)
+_rel_perm2nw_prime = ft.partial(rel_perm2nw_prime)
+_rel_perm3w_prime = ft.partial(rel_perm3w_prime)
+_rel_perm3nw_prime = ft.partial(rel_perm3nw_prime)
+_rel_perm4w_prime = ft.partial(rel_perm4w_prime)
+_rel_perm4nw_prime = ft.partial(rel_perm4nw_prime)
+
+subdomain1_rel_perm_prime = {
+ 'wetting': _rel_perm1w_prime,
+ 'nonwetting': _rel_perm1nw_prime
+}
+
+
+subdomain2_rel_perm_prime = {
+ 'wetting': _rel_perm2w_prime,
+ 'nonwetting': _rel_perm2nw_prime
+}
+
+subdomain3_rel_perm_prime = {
+ 'wetting': _rel_perm3w_prime,
+ 'nonwetting': _rel_perm3nw_prime
+}
+
+
+subdomain4_rel_perm_prime = {
+ 'wetting': _rel_perm4w_prime,
+ 'nonwetting': _rel_perm4nw_prime
+}
+
+
+# dictionary of relative permeabilties on all domains.
+# ka_prime = {
+# 1: subdomain1_rel_perm_prime,
+# 2: subdomain1_rel_perm_prime,
+# 3: subdomain2_rel_perm_prime,
+# 4: subdomain2_rel_perm_prime
+# }
+ka_prime = {
+ 1: subdomain1_rel_perm_prime,
+ 2: subdomain2_rel_perm_prime,
+ 3: subdomain3_rel_perm_prime,
+ 4: subdomain4_rel_perm_prime
+}
+
+# S-pc-relation ship. We use the van Genuchten approach, i.e.
+# pc = 1/alpha*(S^{-1/m} -1)^1/n, where we set alpha = 0, assume m = 1-1/n
+# (see Helmig) and assume that residual saturation is Sw
+# this function needs to be monotonically decreasing in the capillary pressure
+# pc. Since in the richards case pc=-pw, this becomes as a function of pw a
+# mono tonically INCREASING function like in our Richards-Richards paper.
+# However since we unify the treatment in the code for Richards and two-phase,
+# we need the same requierment for both cases, two-phase and Richards.
+def saturation(pc, n_index, alpha):
+ # inverse capillary pressure-saturation-relationship
+ expr = df.conditional(
+ pc > 0, 1/((1 + (alpha*pc)**n_index)**((n_index - 1)/n_index)), 1)
+ return expr
+
+
+# S-pc-relation ship. We use the van Genuchten approach, i.e.
+# pc = 1/alpha*(S^{-1/m} -1)^1/n, where we set alpha = 0, assume m = 1-1/n
+# (see Helmig) and assume that residual saturation is Sw
+def saturation_sym(pc, n_index, alpha):
+ # inverse capillary pressure-saturation-relationship
+ # df.conditional(pc > 0,
+ return 1/((1 + (alpha*pc)**n_index)**((n_index - 1)/n_index))
+
+
+# derivative of S-pc relationship with respect to pc. This is needed for the
+# construction of a analytic solution.
+def saturation_sym_prime(pc, n_index, alpha):
+ # inverse capillary pressure-saturation-relationship
+ expr = -(alpha*(n_index - 1)*(alpha*pc)**(n_index - 1))\
+ / ((1 + (alpha*pc)**n_index)**((2*n_index - 1)/n_index))
+ return expr
+
+
+# note that the conditional definition of S-pc in the nonsymbolic part will be
+# incorporated in the construction of the exact solution below.
+S_pc_sym = {
+ 1: ft.partial(saturation_sym, n_index=3, alpha=0.001),
+ 2: ft.partial(saturation_sym, n_index=3, alpha=0.001),
+ 3: ft.partial(saturation_sym, n_index=6, alpha=0.001),
+ 4: ft.partial(saturation_sym, n_index=6, alpha=0.001)
+}
+
+
+S_pc_sym_prime = {
+ 1: ft.partial(saturation_sym_prime, n_index=3, alpha=0.001),
+ 2: ft.partial(saturation_sym_prime, n_index=3, alpha=0.001),
+ 3: ft.partial(saturation_sym_prime, n_index=6, alpha=0.001),
+ 4: ft.partial(saturation_sym_prime, n_index=6, alpha=0.001)
+}
+
+
+sat_pressure_relationship = {
+ 1: ft.partial(saturation, n_index=3, alpha=0.001),
+ 2: ft.partial(saturation, n_index=3, alpha=0.001),
+ 3: ft.partial(saturation, n_index=6, alpha=0.001),
+ 4: ft.partial(saturation, n_index=6, alpha=0.001)
+}
+
+
+#############################################
+# Manufacture source expressions with sympy #
+#############################################
+x, y = sym.symbols('x[0], x[1]') # needed by UFL
+t = sym.symbols('t', positive=True)
+
+p_e_sym_2patch = {
+ 1: {'wetting': -6 - (1+t*t)*(1 + x*x + y*y),
+ 'nonwetting': 0.0*t}, # -1-t*(1.1 + y + x**2)**2},
+ 2: {'wetting': -6.0 - (1.0 + t*t)*(1.0 + x*x),
+ 'nonwetting': (-1-t*(1.1+y + x**2))*y**2},
+ # 'nonwetting': (-1-t*(1.1 + x**2)**2 - sym.sqrt(5+t**2))*y**2},
+}
+
+p_e_sym = {
+ 1: {'wetting': p_e_sym_2patch[1]['wetting'],
+ 'nonwetting': p_e_sym_2patch[1]['nonwetting']},
+ 2: {'wetting': p_e_sym_2patch[1]['wetting'],
+ 'nonwetting': p_e_sym_2patch[1]['nonwetting']},
+ 3: {'wetting': p_e_sym_2patch[2]['wetting'],
+ 'nonwetting': p_e_sym_2patch[2]['nonwetting']},
+ 4: {'wetting': p_e_sym_2patch[2]['wetting'],
+ 'nonwetting': p_e_sym_2patch[2]['nonwetting']}
+}
+
+
+# p_e_sym = {
+# 1: {'wetting': 1.0 - (1.0 + t*t)*(10.0 + x*x + (y-5.0)*(y-5.0)),
+# 'nonwetting':
+# -2 - t*(1 + (y - 5.0) + x**2)**2
+# - sym.sqrt(2 + t**2)*(1 + (y - 5.0))
+# },
+# 2: {'wetting': 1.0 - (1.0 + t*t)*(10.0 + x*x + (y-5.0)*(y-5.0)),
+# 'nonwetting':
+# - 2 - t*(1 + (y - 5.0) + x**2)**2
+# - sym.sqrt(2 + t**2)*(1 + (y - 5.0))
+# },
+# 3: {'wetting':
+# 1.0 - (1.0 + t*t)*(10.0 + x*x + (y - 5.0)*(y - 5.0))
+# - (y - 5.0)*(y - 5.0)*3*sym.sin(-2*t + 2*x)*sym.sin(1/2*y - 1.2*t),
+# 'nonwetting': - 2 - t*(1 + x**2)**2 - sym.sqrt(2 + t**2)
+# },
+# 4: {'wetting':
+# 1.0 - (1.0 + t*t)*(10.0 + x*x + (y - 5.0)*(y - 5.0))
+# - (y - 5.0)*(y - 5.0)*3*sym.sin(-2*t + 2*x)*sym.sin(1/2*y - 1.2*t),
+# 'nonwetting': - 2 - t*(1 + x**2)**2 - sym.sqrt(2 + t**2)
+# }
+# }
+
+pc_e_sym = dict()
+for subdomain, isR in isRichards.items():
+ if isR:
+ pc_e_sym.update({subdomain: -p_e_sym[subdomain]['wetting']})
+ else:
+ pc_e_sym.update(
+ {subdomain: p_e_sym[subdomain]['nonwetting']
+ - p_e_sym[subdomain]['wetting']}
+ )
+
+
+symbols = {"x": x,
+ "y": y,
+ "t": t}
+# turn above symbolic code into exact solution for dolphin and
+# construct the rhs that matches the above exact solution.
+exact_solution_example = hlp.generate_exact_solution_expressions(
+ symbols=symbols,
+ isRichards=isRichards,
+ symbolic_pressure=p_e_sym,
+ symbolic_capillary_pressure=pc_e_sym,
+ saturation_pressure_relationship=S_pc_sym,
+ saturation_pressure_relationship_prime=S_pc_sym_prime,
+ viscosity=viscosity,
+ porosity=porosity,
+ relative_permeability=relative_permeability,
+ relative_permeability_prime=ka_prime,
+ densities=densities,
+ gravity_acceleration=gravity_acceleration,
+ include_gravity=include_gravity,
+ )
+source_expression = exact_solution_example['source']
+exact_solution = exact_solution_example['exact_solution']
+initial_condition = exact_solution_example['initial_condition']
+
+# Dictionary of dirichlet boundary conditions.
+dirichletBC = dict()
+# similarly to the outer boundary dictionary, if a patch has no outer boundary
+# None should be written instead of an expression.
+# This is a bit of a brainfuck:
+# dirichletBC[ind] gives a dictionary of the outer boundaries of subdomain ind.
+# Since a domain patch can have several disjoint outer boundary parts, the
+# expressions need to get an enumaration index which starts at 0.
+# So dirichletBC[ind][j] is the dictionary of outer dirichlet conditions of
+# subdomain ind and boundary part j.
+# Finally, dirichletBC[ind][j]['wetting'] and dirichletBC[ind][j]['nonwetting']
+# return the actual expression needed for the dirichlet condition for both
+# phases if present.
+
+# subdomain index: {outer boudary part index: {phase: expression}}
+for subdomain in isRichards.keys():
+ # if subdomain has no outer boundary, outer_boundary_def_points[subdomain]
+ # is None
+ if outer_boundary_def_points[subdomain] is None:
+ dirichletBC.update({subdomain: None})
+ else:
+ dirichletBC.update({subdomain: dict()})
+ # set the dirichlet conditions to be the same code as exact solution on
+ # the subdomain.
+ for outer_boundary_ind in outer_boundary_def_points[subdomain].keys():
+ dirichletBC[subdomain].update(
+ {outer_boundary_ind: exact_solution[subdomain]}
+ )
+
+
+# LOG FILE OUTPUT #############################################################
+# read this file and print it to std out. This way the simulation can produce a
+# log file with ./TP-R-layered_soil.py | tee simulation.log
+f = open(thisfile, 'r')
+print(f.read())
+f.close()
+
+
+# RUN #########################################################################
+for starttime in starttimes:
+ for mesh_resolution, solver_tol in resolutions.items():
+ # initialise LDD simulation class
+ simulation = ldd.LDDsimulation(
+ tol=1E-14,
+ LDDsolver_tol=solver_tol,
+ debug=debugflag,
+ max_iter_num=max_iter_num,
+ FEM_Lagrange_degree=FEM_Lagrange_degree,
+ mesh_study=mesh_study
+ )
+
+ simulation.set_parameters(
+ use_case=use_case,
+ output_dir=output_string,
+ subdomain_def_points=subdomain_def_points,
+ isRichards=isRichards,
+ interface_def_points=interface_def_points,
+ outer_boundary_def_points=outer_boundary_def_points,
+ adjacent_subdomains=adjacent_subdomains,
+ mesh_resolution=mesh_resolution,
+ viscosity=viscosity,
+ porosity=porosity,
+ L=L,
+ lambda_param=lambda_param,
+ relative_permeability=relative_permeability,
+ saturation=sat_pressure_relationship,
+ starttime=starttime,
+ number_of_timesteps=number_of_timesteps,
+ number_of_timesteps_to_analyse=number_of_timesteps_to_analyse,
+ plot_timestep_every=plot_timestep_every,
+ timestep_size=timestep_size,
+ sources=source_expression,
+ initial_conditions=initial_condition,
+ dirichletBC_expression_strings=dirichletBC,
+ exact_solution=exact_solution,
+ densities=densities,
+ include_gravity=include_gravity,
+ gravity_acceleration=gravity_acceleration,
+ write2file=write_to_file,
+ )
+
+ simulation.initialise()
+ output_dir = simulation.output_dir
+ # simulation.write_exact_solution_to_xdmf()
+ output = simulation.run(analyse_condition=analyse_condition)
+ for subdomain_index, subdomain_output in output.items():
+ mesh_h = subdomain_output['mesh_size']
+ for phase, error_dict in subdomain_output['errornorm'].items():
+ filename = output_dir \
+ + "subdomain{}".format(subdomain_index)\
+ + "-space-time-errornorm-{}-phase.csv".format(phase)
+ # for errortype, errornorm in error_dict.items():
+
+ # eocfile = open("eoc_filename", "a")
+ # eocfile.write( str(mesh_h) + " " + str(errornorm) + "\n" )
+ # eocfile.close()
+ # if subdomain.isRichards:mesh_h
+ data_dict = {
+ 'mesh_parameter': mesh_resolution,
+ 'mesh_h': mesh_h,
+ }
+ for norm_type, errornorm in error_dict.items():
+ data_dict.update(
+ {norm_type: errornorm}
+ )
+ errors = pd.DataFrame(data_dict, index=[mesh_resolution])
+ # check if file exists
+ if os.path.isfile(filename) is True:
+ with open(filename, 'a') as f:
+ errors.to_csv(
+ f,
+ header=False,
+ sep='\t',
+ encoding='utf-8',
+ index=False
+ )
+ else:
+ errors.to_csv(
+ filename,
+ sep='\t',
+ encoding='utf-8',
+ index=False
+ )