From 867e84c77ff776afffaef04d889ed6e3bcc19a1e Mon Sep 17 00:00:00 2001
From: David Seus <david.seus@ians.uni-stuttgart.de>
Date: Fri, 16 Aug 2019 15:38:25 +0200
Subject: [PATCH] fix weird git fuckug
---
.gitignore | 1 +
...ed_soil_with_inner_patch_const_solution.py | 4 +-
.../TP-TP-layered_soil-second-example.py | 537 ++++++++++++++++++
TP-TP-layered-soil-case/TP-TP-layered_soil.py | 172 +++---
4 files changed, 608 insertions(+), 106 deletions(-)
create mode 100755 TP-TP-layered-soil-case/TP-TP-layered_soil-second-example.py
diff --git a/.gitignore b/.gitignore
index eb49951..d767444 100644
--- a/.gitignore
+++ b/.gitignore
@@ -60,6 +60,7 @@
*.ipynb
core
+
# Ignoriere Bilder und Graphiken sowie Videos und Musik
*.png
*.jpg
diff --git a/TP-TP-layered-soil-case-with-inner-patch-constant-solution/TP-TP-layered_soil_with_inner_patch_const_solution.py b/TP-TP-layered-soil-case-with-inner-patch-constant-solution/TP-TP-layered_soil_with_inner_patch_const_solution.py
index d666eac..a12790d 100755
--- a/TP-TP-layered-soil-case-with-inner-patch-constant-solution/TP-TP-layered_soil_with_inner_patch_const_solution.py
+++ b/TP-TP-layered-soil-case-with-inner-patch-constant-solution/TP-TP-layered_soil_with_inner_patch_const_solution.py
@@ -23,12 +23,12 @@ sym.init_printing()
# ----------------------------------------------------------------------------#
# ------------------- MESH ---------------------------------------------------#
# ----------------------------------------------------------------------------#
-mesh_resolution = 14
+mesh_resolution = 40
# ----------------------------------------:-----------------------------------#
# ------------------- TIME ---------------------------------------------------#
# ----------------------------------------------------------------------------#
timestep_size = 0.0001
-number_of_timesteps = 10
+number_of_timesteps = 100
# 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
diff --git a/TP-TP-layered-soil-case/TP-TP-layered_soil-second-example.py b/TP-TP-layered-soil-case/TP-TP-layered_soil-second-example.py
new file mode 100755
index 0000000..23d75d3
--- /dev/null
+++ b/TP-TP-layered-soil-case/TP-TP-layered_soil-second-example.py
@@ -0,0 +1,537 @@
+#!/usr/bin/python3
+"""This program sets up a domain together with a decomposition into subdomains
+modelling layered soil. This is used for our LDD article with tp-tp and tp-r
+coupling.
+
+Along with the subdomains and the mesh domain markers are set upself.
+The resulting mesh is saved into files for later use.
+"""
+
+#!/usr/bin/python3
+import dolfin as df
+import mshr
+import numpy as np
+import sympy as sym
+import typing as tp
+import functools as ft
+import domainPatch as dp
+import LDDsimulation as ldd
+import helpers as hlp
+
+# init sympy session
+sym.init_printing()
+
+use_case="TP-TP-layered-soil-2nd-example"
+solver_tol = 1E-5
+
+############ GRID #######################ü
+mesh_resolution = 5
+timestep_size = 0.001
+number_of_timesteps = 10
+# 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 = 0
+starttime = 0
+
+Lw = 0.25 #/timestep_size
+Lnw=Lw
+
+l_param_w = 40
+l_param_nw = 40
+
+include_gravity = True
+debugflag = False
+analyse_condition = True
+
+output_string = "./output/test_postprocessing_number_of_timesteps{}_".format(number_of_timesteps)
+
+# 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: False,
+ 2: False,
+ 3: False,
+ 4: False
+ }
+
+# isRichards = {
+# 1: True,
+# 2: True,
+# 3: True,
+# 4: True
+# }
+
+# 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': 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: 0.2, #0.2, # Clayey gravels, clayey sandy gravels
+ 2: 0.22, #0.22, # Silty gravels, silty sandy gravels
+ 3: 0.37, #0.37, # Clayey sands
+ 4: 0.2, #0.2 # Silty or sandy clay
+}
+
+# subdom_num : subdomain L for L-scheme
+L = {
+ 1: {'wetting' :Lw,
+ 'nonwetting': Lnw},
+ 2: {'wetting' :Lw,
+ 'nonwetting': Lnw},
+ 3: {'wetting' :Lw,
+ 'nonwetting': Lnw},
+ 4: {'wetting' :Lw,
+ 'nonwetting': Lnw}
+}
+
+# subdom_num : lambda parameter for the L-scheme
+lambda_param = {
+ 1: {'wetting': l_param_w,
+ 'nonwetting': l_param_nw},#
+ 2: {'wetting': l_param_w,
+ 'nonwetting': l_param_nw},#
+ 3: {'wetting': l_param_w,
+ 'nonwetting': l_param_nw},#
+ 4: {'wetting': l_param_w,
+ 'nonwetting': l_param_nw},#
+}
+
+
+## relative permeabilty functions on subdomain 1
+def rel_perm1w(s):
+ # relative permeabilty wetting on subdomain1
+ return s**2
+
+
+def rel_perm1nw(s):
+ # relative permeabilty nonwetting on subdomain1
+ return (1-s)**2
+
+
+## relative permeabilty functions on subdomain 2
+def rel_perm2w(s):
+ # relative permeabilty wetting on subdomain2
+ return s**3
+
+
+def rel_perm2nw(s):
+ # relative permeabilty nonwetting on subdosym.cos(0.8*t - (0.8*x + 1/7*y))main2
+ return (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)
+
+subdomain1_rel_perm = {
+ 'wetting': _rel_perm1w,#
+ 'nonwetting': _rel_perm1nw
+}
+
+subdomain2_rel_perm = {
+ 'wetting': _rel_perm2w,#
+ 'nonwetting': _rel_perm2nw
+}
+
+# _rel_perm3 = ft.partial(rel_perm2)
+# subdomain3_rel_perm = subdomain2_rel_perm.copy()
+#
+# _rel_perm4 = ft.partial(rel_perm1)
+# subdomain4_rel_perm = subdomain1_rel_perm.copy()
+
+# 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
+}
+
+# definition of the derivatives of the relative permeabilities
+# relative permeabilty functions on subdomain 1
+def rel_perm1w_prime(s):
+ # relative permeabilty on subdomain1
+ return 2*s
+
+def rel_perm1nw_prime(s):
+ # relative permeabilty on subdomain1
+ return -2*(1-s)
+
+# definition of the derivatives of the relative permeabilities
+# relative permeabilty functions on subdomain 1
+def rel_perm2w_prime(s):
+ # relative permeabilty on subdomain1
+ return 3*s**2
+
+def rel_perm2nw_prime(s):
+ # relative permeabilty on subdomain1
+ return -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)
+
+subdomain1_rel_perm_prime = {
+ 'wetting': _rel_perm1w_prime,
+ 'nonwetting': _rel_perm1nw_prime
+}
+
+
+subdomain2_rel_perm_prime = {
+ 'wetting': _rel_perm2w_prime,
+ 'nonwetting': _rel_perm2nw_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
+}
+
+
+
+# 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
+ return df.conditional(pc > 0, 1/((1 + (alpha*pc)**n_index)**((n_index - 1)/n_index)), 1)
+
+# 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
+ return -(alpha*(n_index - 1)*(alpha*pc)**(n_index - 1)) / ( (1 + (alpha*pc)**n_index)**((2*n_index - 1)/n_index) )
+
+
+# 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': -5 - (1+t*t)*(1 + x*x + y*y),
+ 'nonwetting': -1-t*(1.1+y + x**2)}, # - sym.sqrt(2+t**2)*(1-y)**2},
+ 2: {'wetting': -5.0 - (1.0 + t*t)*(1.0 + x*x),
+ 'nonwetting': -1-t*(1.1 + x**2)},# - sym.sqrt(2+t**2)*(1-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]}
+ )
+
+write_to_file = {
+ 'meshes_and_markers': True,
+ 'L_iterations': True
+}
+
+# initialise LDD simulation class
+simulation = ldd.LDDsimulation(tol=1E-14, debug=debugflag, LDDsolver_tol=solver_tol)
+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,
+ 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,
+ write2file=write_to_file,
+ )
+
+simulation.initialise()
+# print(simulation.__dict__)
+simulation.run(analyse_condition=analyse_condition)
+# simulation.LDDsolver(time=0, debug=True, analyse_timestep=True)
+# df.info(parameters, True)
diff --git a/TP-TP-layered-soil-case/TP-TP-layered_soil.py b/TP-TP-layered-soil-case/TP-TP-layered_soil.py
index 68d799f..a56a37a 100755
--- a/TP-TP-layered-soil-case/TP-TP-layered_soil.py
+++ b/TP-TP-layered-soil-case/TP-TP-layered_soil.py
@@ -16,27 +16,35 @@ import typing as tp
import functools as ft
import domainPatch as dp
import LDDsimulation as ldd
+import helpers as hlp
# init sympy session
sym.init_printing()
-# ----------------------------------------------------------------------------#
-# ------------------- MESH ---------------------------------------------------#
-# ----------------------------------------------------------------------------#
-mesh_resolution = 19
-# ----------------------------------------:-------------------------------------#
-# ------------------- TIME ---------------------------------------------------#
-# ----------------------------------------------------------------------------#
-timestep_size = 0.001
-number_of_timesteps = 1000
-# 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
+use_case="TP-TP-layered-soil"
+solver_tol = 5E-7
+
+############ GRID #######################ü
+mesh_resolution = 30
+timestep_size = 0.0005
+number_of_timesteps = 20
+# 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
starttime = 0
+Lw = 0.25 #/timestep_size
+Lnw=Lw
+
l_param_w = 40
l_param_nw = 40
+include_gravity = True
+debugflag = True
+analyse_condition = False
+
+output_string = "./output/number_of_timesteps{}_".format(number_of_timesteps)
+
# global domain
subdomain0_vertices = [df.Point(-1.0,-1.0), #
df.Point(1.0,-1.0),#
@@ -220,14 +228,14 @@ porosity = {
# subdom_num : subdomain L for L-scheme
L = {
- 1: {'wetting' :0.25,
- 'nonwetting': 0.25},
- 2: {'wetting' :0.25,
- 'nonwetting': 0.25},
- 3: {'wetting' :0.25,
- 'nonwetting': 0.25},
- 4: {'wetting' :0.25,
- 'nonwetting': 0.25}
+ 1: {'wetting' :Lw,
+ 'nonwetting': Lnw},
+ 2: {'wetting' :Lw,
+ 'nonwetting': Lnw},
+ 3: {'wetting' :Lw,
+ 'nonwetting': Lnw},
+ 4: {'wetting' :Lw,
+ 'nonwetting': Lnw}
}
# subdom_num : lambda parameter for the L-scheme
@@ -302,7 +310,7 @@ def rel_perm1w_prime(s):
def rel_perm1nw_prime(s):
# relative permeabilty on subdomain1
- return 2*(1-s)
+ return -2*(1-s)
# definition of the derivatives of the relative permeabilities
# relative permeabilty functions on subdomain 1
@@ -312,7 +320,7 @@ def rel_perm2w_prime(s):
def rel_perm2nw_prime(s):
# relative permeabilty on subdomain1
- return 2*(1-s)
+ return -2*(1-s)
_rel_perm1w_prime = ft.partial(rel_perm1w_prime)
_rel_perm1nw_prime = ft.partial(rel_perm1nw_prime)
@@ -398,10 +406,10 @@ x, y = sym.symbols('x[0], x[1]') # needed by UFL
t = sym.symbols('t', positive=True)
p_e_sym_2patch = {
- 1: {'wetting': -1 - (1+t*t)*(1 + x*x + y*y),
- 'nonwetting': -t*(1-y - x**2)**2 - sym.sqrt(2+t**2)*(1-y)},
- 2: {'wetting': -1.0 - (1.0 + t*t)*(1.0 + x*x),
- 'nonwetting': -t*(1- x**2)**2 - sym.sqrt(2+t**2)*(1-y)},
+ 1: {'wetting': -3 - (1+t*t)*(1 + x*x + y*y),
+ 'nonwetting': -1-t*(1-y - x**2)**2 - sym.sqrt(2+t**2)*(1-y)**2},
+ 2: {'wetting': -3.0 - (1.0 + t*t)*(1.0 + x*x),
+ 'nonwetting': -1-t*(1- x**2)**2 - sym.sqrt(2+t**2)*(1-y)**2},
}
p_e_sym = {
@@ -427,83 +435,38 @@ p_e_sym = {
# 'nonwetting': - 2 - t*(1 + x**2)**2 -sym.sqrt(2+t**2)}
# }
-pc_e_sym = {
- 1: p_e_sym[1]['nonwetting'] - p_e_sym[1]['wetting'],
- 2: p_e_sym[2]['nonwetting'] - p_e_sym[2]['wetting'],
- 3: p_e_sym[3]['nonwetting'] - p_e_sym[3]['wetting'],
- 4: p_e_sym[4]['nonwetting'] - p_e_sym[4]['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()
+pc_e_sym = dict()
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:
- subdomain_has_phases = ["wetting"]
+ pc_e_sym.update({subdomain: -p_e_sym[subdomain]['wetting']})
else:
- subdomain_has_phases = ["wetting", "nonwetting"]
-
- # conditional for S_pc_prime
- pc = pc_e_sym[subdomain]
- dtpc = sym.diff(pc, t, 1)
- dxpc = sym.diff(pc, x, 1)
- dypc = sym.diff(pc, y, 1)
- S = sym.Piecewise((S_pc_sym[subdomain](pc), pc > 0), (1, True))
- dS = sym.Piecewise((S_pc_sym_prime[subdomain](pc), pc > 0), (0, True))
- for phase in subdomain_has_phases:
- # Turn above symbolic expression for exact solution into c code
- exact_solution[subdomain].update(
- {phase: sym.printing.ccode(p_e_sym[subdomain][phase])}
- )
- # save the c code for initial conditions
- initial_condition[subdomain].update(
- {phase: sym.printing.ccode(p_e_sym[subdomain][phase].subs(t, 0))}
- )
- if phase == "nonwetting":
- dtS[subdomain].update(
- {phase: -porosity[subdomain]*dS*dtpc}
- )
- else:
- dtS[subdomain].update(
- {phase: porosity[subdomain]*dS*dtpc}
- )
- pa = p_e_sym[subdomain][phase]
- 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])
+ 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()
@@ -539,8 +502,9 @@ write_to_file = {
}
# initialise LDD simulation class
-simulation = ldd.LDDsimulation(tol=1E-14, debug=True, LDDsolver_tol=1E-7)
-simulation.set_parameters(output_dir="./output/",
+simulation = ldd.LDDsimulation(tol=1E-14, debug=debugflag, LDDsolver_tol=solver_tol)
+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,
@@ -562,12 +526,12 @@ simulation.set_parameters(output_dir="./output/",
dirichletBC_expression_strings=dirichletBC,
exact_solution=exact_solution,
densities=densities,
- include_gravity=True,
+ include_gravity=include_gravity,
write2file=write_to_file,
)
simulation.initialise()
# print(simulation.__dict__)
-simulation.run()
+simulation.run(analyse_condition=analyse_condition)
# simulation.LDDsolver(time=0, debug=True, analyse_timestep=True)
# df.info(parameters, True)
--
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