class ParabolicFEMModel():
def __init__(self, pde, mesh, p=1, q=6):
from fealpy.functionspace import LagrangeFiniteElementSpace
from fealpy.boundarycondition import BoundaryCondition
self.space = LagrangeFiniteElementSpace(mesh, p)
self.mesh = self.space.mesh
self.pde = pde
self.ftype = self.mesh.ftype
self.itype = self.mesh.itype
self.M = self.space.mass_matrix()
self.A = self.space.stiff_matrix()
def init_solution(self, timeline):
NL = timeline.number_of_time_levels()
gdof = self.space.number_of_global_dofs()
uh = np.zeros((gdof, NL), dtype=self.mesh.ftype)
uh[:,
0] = self.space.interpolation(lambda x: self.pde.solution(x, 0.0))
return uh
def interpolation(self, u, timeline):
NL = timeline.number_of_time_levels()
gdof = self.space.number_of_global_dofs()
ps = self.space.interpolation_points()
uI = np.zeros((gdof, NL), dtype=self.mesh.ftype)
times = timeline.all_time_levels()
for i, t in enumerate(times):
uI[..., i] = u(ps, t)
return uI
def get_current_left_matrix(self, timeline):
dt = timeline.current_time_step_length()
return self.M + 0.5 * dt * self.A
def get_current_right_vector(self, uh, timeline):
i = timeline.current
dt = timeline.current_time_step_length()
t0 = timeline.current_time_level()
t1 = timeline.next_time_level()
f = lambda x: self.pde.source(x, t0) + self.pde.source(x, t1)
F = self.space.source_vector(f)
return self.M @ uh[:, i] - 0.5 * dt * self.A @ uh[:, i]
def apply_boundary_condition(self, A, b, timeline):
t1 = timeline.next_time_level()
bc = BoundaryCondition(self.space,
neuamn=lambda x: self.pde.neuman(x, t1))
b = bc.apply_neuman_bc(b)
return A, b
def solve(self, uh, A, b, solver, timeline):
i = timeline.current
uh[:, i + 1] = solver(A, b)
def solve_poisson_3d(self, n=2):
from fealpy.pde.poisson_3d import CosCosCosData as PDE
from fealpy.mesh import MeshFactory
from fealpy.functionspace import LagrangeFiniteElementSpace
from fealpy.boundarycondition import DirichletBC
pde = PDE()
mf = MeshFactory()
m = 2**n
box = [0, 1, 0, 1, 0, 1]
mesh = mf.boxmesh3d(box, nx=m, ny=m, nz=m, meshtype='tet')
space = LagrangeFiniteElementSpace(mesh, p=1)
gdof = space.number_of_global_dofs()
NC = mesh.number_of_cells()
print('gdof:', gdof, 'NC:', NC)
bc = DirichletBC(space, pde.dirichlet)
uh = space.function()
A = space.stiff_matrix()
A = space.parallel_stiff_matrix(q=1)
M = space.parallel_mass_matrix(q=2)
M = space.mass_matrix()
F = space.source_vector(pde.source)
A, F = bc.apply(A, F, uh)
solver = PETScSolver()
solver.solve(A, F, uh)
error = space.integralalg.L2_error(pde.solution, uh)
print(error)
def test_poisson_fem_2d():
degree = 1
dim = 2
nrefine = 4
maxit = 4
from fealpy.pde.poisson_2d import CosCosData as PDE
pde = PDE()
mesh = pde.init_mesh(n=nrefine)
errorMatrix = np.zeros((2, maxit), dtype=np.float64)
NDof = np.zeros(maxit, dtype=np.int64)
for i in range(maxit):
space = LagrangeFiniteElementSpace(mesh, p=degree)
NDof[i] = space.number_of_global_dofs()
bc = DirichletBC(space, pde.dirichlet)
uh = space.function()
A = space.stiff_matrix()
F = space.source_vector(pde.source)
A, F = bc.apply(A, F, uh)
uh[:] = spsolve(A, F).reshape(-1)
errorMatrix[0, i] = space.integralalg.L2_error(pde.solution, uh)
errorMatrix[1, i] = space.integralalg.L2_error(pde.gradient, uh.grad_value)
if i < maxit-1:
mesh.uniform_refine()
def test_poisson():
p = 1 # 有限元空间次数, 可以增大 p， 看输出结果的变化
n = 4 # 初始网格加密次数
maxit = 4 # 最大迭代次数
pde = PDE()
mesh = pde.init_mesh(n=n)
errorMatrix = np.zeros((2, maxit), dtype=np.float)
NDof = np.zeros(maxit, dtype=np.float)
for i in range(maxit):
space = LagrangeFiniteElementSpace(mesh, p=p) # 建立有限元空间
NDof[i] = space.number_of_global_dofs() # 有限元空间自由度的个数
bc = DirichletBC(space, pde.dirichlet) # DirichletBC 条件
uh = space.function() # 有限元函数
A = space.stiff_matrix() # 刚度矩阵
F = space.source_vector(pde.source) # 载荷向量
A, F = bc.apply(A, F, uh) # 处理边界条件
uh[:] = spsolve(A, F).reshape(-1) # 稀疏矩阵直接解法器
# ml = pyamg.ruge_stuben_solver(A) # 代数多重网格解法器
# uh[:] = ml.solve(F, tol=1e-12, accel='cg').reshape(-1)
errorMatrix[0, i] = space.integralalg.L2_error(
pde.solution, uh
) # 计算 L2 误差
errorMatrix[1, i] = space.integralalg.L2_error(
pde.gradient, uh.grad_value
) # 计算 H1 误差
if i < maxit - 1:
mesh.uniform_refine() # 一致加密网格
assert (errorMatrix < 1.0).all()
elif d == 3:
from fealpy.pde.poisson_3d import CosCosCosData as PDE
#pde = PDE()
pde = CosCosData()
mesh = pde.init_mesh(n=3)
errorType = [
'$|| u - u_h||_{\Omega,0}$', '$||\\nabla u - \\nabla u_h||_{\Omega, 0}$'
]
errorMatrix = np.zeros((2, maxit), dtype=np.float)
NDof = np.zeros(maxit, dtype=np.float)
for i in range(maxit):
space = LagrangeFiniteElementSpace(mesh, p=p)
NDof[i] = space.number_of_global_dofs()
uh = space.function()
A = space.stiff_matrix()
F = space.source_vector(pde.source)
bc = NeumannBC(space, pde.neumann)
A, F = bc.apply(
F, A=A) # Here is the case for pure Neumann bc, we also need modify A
# bc.apply(F) # Not pure Neumann bc case
uh[:] = spsolve(A, F)[:-1] # we add a addtional dof
# Here can not work
#ml = pyamg.ruge_stuben_solver(A)
#x = ml.solve(F, tol=1e-12, accel='cg').reshape(-1)
#uh[:] = x[-1]
p = int(sys.argv[1])
maxit = int(sys.argv[2])
start = timer()
solver = MatlabSolver()
end = timer()
print("The matalb start time:", end - start)
pde = CosCosData()
mesh = pde.init_mesh(4)
for i in range(maxit):
space = LagrangeFiniteElementSpace(mesh, p=1)
gdof = space.number_of_global_dofs()
print("The num of dofs:", gdof)
A = space.stiff_matrix()
b = space.source_vector(pde.source)
bc = DirichletBC(space, pde.dirichlet)
AD, b = bc.apply(A, b)
uh0 = solver.divide(AD, b)
start = timer()
uh1 = spsolve(AD, b)
end = timer()
print("The spsolver time:", end - start)
print(np.sum(np.abs(uh0 - uh1)))
#uh1 = solver.mumps_solver(A, b)
#uh2 = solver.ifem_amg_solver(A, b)
if i < maxit - 1:
class WaterFloodingModelSolver():
"""
Notes
-----
"""
def __init__(self, model, T=800 * 3600 * 24, NS=32, NT=800 * 24):
self.model = model
self.mesh = model.space_mesh(n=NS)
self.timeline = model.time_mesh(T=T, n=NT)
self.GD = model.GD
if self.GD == 2:
self.vspace = RaviartThomasFiniteElementSpace2d(self.mesh,
p=0) # 速度空间
elif self.GD == 3:
self.vspace = RaviartThomasFiniteElementSpace3d(self.mesh, p=0)
self.pspace = self.vspace.smspace # 压强和饱和度所属的空间, 分片常数
self.cspace = LagrangeFiniteElementSpace(self.mesh, p=1) # 位移空间
# 上一时刻物理量的值
self.v = self.vspace.function() # 速度函数
self.p = self.pspace.function() # 压强函数
self.s = self.pspace.function() # 水的饱和度函数 默认为0, 初始时刻区域内水的饱和度为0
self.u = self.cspace.function(dim=self.GD) # 位移函数
self.phi = self.pspace.function() # 孔隙度函数, 分片常数
# 当前时刻物理量的值, 用于保存临时计算出的值, 模型中系数的计算由当前时刻
# 的物理量的值决定
self.cv = self.vspace.function() # 速度函数
self.cp = self.pspace.function() # 压强函数
self.cs = self.pspace.function() # 水的饱和度函数 默认为0, 初始时刻区域内水的饱和度为0
self.cu = self.cspace.function(dim=self.GD) # 位移函数
self.cphi = self.pspace.function() # 孔隙度函数, 分片常数
# 初值
self.p[:] = model.rock['initial pressure'] # MPa
self.phi[:] = model.rock['porosity'] # 初始孔隙度
self.cp[:] = model.rock['initial pressure'] # 初始地层压强
self.cphi[:] = model.rock['porosity'] # 当前孔隙度系数
# 源项, TODO: 注意这里假设用的是结构网格, 换其它的网格需要修改代码
self.fo = self.cspace.function()
self.fo[-1] = -self.model.oil['production rate'] # 产出
self.fw = self.cspace.function()
self.fw[0] = self.model.water['injection rate'] # 注入
# 一些常数矩阵和向量
# 速度散度矩阵, 速度方程对应的散度矩阵, (\nabla\cdot v, w)
self.B = self.vspace.div_matrix()
# 压强方程对应的位移散度矩阵, (\nabla\cdot u, w) 位移散度矩阵
# * 注意这里利用了压强空间分片常数, 线性函数导数也是分片常数的事实
c = self.mesh.entity_measure('cell')
c *= self.model.rock['biot']
val = self.mesh.grad_lambda() # (NC, TD+1, GD)
val *= c[:, None, None]
pc2d = self.pspace.cell_to_dof()
cc2d = self.cspace.cell_to_dof()
pgdof = self.pspace.number_of_global_dofs()
cgdof = self.cspace.number_of_global_dofs()
I = np.broadcast_to(pc2d, shape=cc2d.shape)
J = cc2d
self.PU0 = csr_matrix((val[..., 0].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof))
self.PU1 = csr_matrix((val[..., 1].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof))
if self.GD == 3:
self.PU2 = csr_matrix((val[..., 2].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof))
# 线弹性矩阵的右端向量
sigma0 = self.pspace.function()
sigma0[:] = self.model.rock['initial stress']
self.FU = np.zeros(self.GD * cgdof, dtype=np.float64)
self.FU[0 * cgdof:1 * cgdof] -= self.p @ self.PU0
self.FU[1 * cgdof:2 * cgdof] -= self.p @ self.PU1
if self.GD == 3:
self.FU[2 * cgdof:3 * cgdof] -= self.p @ self.PU2
# 初始应力和等效应力项
self.FU[0 * cgdof:1 * cgdof] -= sigma0 @ self.PU0
self.FU[1 * cgdof:2 * cgdof] -= sigma0 @ self.PU1
if self.GD == 3:
self.FU[2 * cgdof:3 * cgdof] -= sigma0 @ self.PU2
# vtk 文件输出
node, cell, cellType, NC = self.mesh.to_vtk()
self.points = vtk.vtkPoints()
self.points.SetData(vnp.numpy_to_vtk(node))
self.cells = vtk.vtkCellArray()
self.cells.SetCells(NC, vnp.numpy_to_vtkIdTypeArray(cell))
self.cellType = cellType
def recover(self, val):
"""
Notes
-----
给定一个分片常数的量, 恢复为分片连续的量
"""
mesh = self.mesh
cell = self.mesh.entity('cell')
NC = mesh.number_of_cells()
NN = mesh.number_of_nodes()
w = 1 / self.mesh.entity_measure('cell') # 恢复权重
w = np.broadcast_to(w[:, None], shape=cell.shape)
r = np.zeros(NN, dtype=np.float64)
d = np.zeros(NN, dtype=np.float64)
np.add.at(d, cell, w)
np.add.at(r, cell, val[:, None] * w)
return r / d
def pressure_coefficient(self):
"""
Notes
-----
计算当前物理量下的压强质量矩阵系数
"""
b = self.model.rock['biot']
Ks = self.model.rock['solid grain stiffness']
cw = self.model.water['compressibility']
co = self.model.oil['compressibility']
Sw = self.cs[:].copy() # 当前的水饱和度 (NQ, NC)
phi = self.cphi[:].copy() # 当前的孔隙度
val = phi * Sw * cw
val += phi * (1 - Sw) * co # 注意这里的 co 是常数, 但在水气混合物条件下应该依赖于压力
val += (b - phi) / Ks
return val
def saturation_pressure_coefficient(self):
"""
Notes
-----
计算当前物理量下, 饱和度方程中, 压强项对应的系数
"""
b = self.model.rock['biot']
Ks = self.model.rock['solid grain stiffness']
cw = self.model.water['compressibility']
val = self.cs[:].copy() # 当前水饱和度
phi = self.cphi[:].copy() # 当前孔隙度
val *= (b - phi) / Ks + phi * cw
return val
def flux_coefficient(self):
"""
Notes
-----
计算**当前**物理量下, 速度方程对应的系数
计算通量的系数, 流体的相对渗透率除以粘性系数得到流动性系数
krg/mu_g 是气体的流动性系数
kro/mu_o 是油的流动性系数
krw/mu_w 是水的流动性系数
这里假设压强的单位是 MPa
1 d = 9.869 233e-13 m^2 = 1000 md
1 cp = 1 mPa s = 1e-9 MPa.s
"""
muw = self.model.water['viscosity'] # 单位是 1 cp = 1 mPa.s
muo = self.model.oil['viscosity'] # 单位是 1 cp = 1 mPa.s
# 岩石的绝对渗透率, 这里考虑了量纲的一致性, 压强是 MPa
k = self.model.rock['permeability'] * 9.869233e-4
Sw = self.cs[:].copy() # 当前水的饱和度系数
lamw = self.model.water_relative_permeability(Sw)
lamw /= muw
lamo = self.model.oil_relative_permeability(Sw)
lamo /= muo
val = 1 / (lamw + lamo) / k #
return val
def water_fractional_flow_coefficient(self):
"""
Notes
-----
计算**当前**物理量下, 饱和度方程中, 水的流动性系数
"""
Sw = self.cs[:].copy() # 当前水的饱和度系数
lamw = self.model.water_relative_permeability(Sw)
lamw /= self.model.water['viscosity']
lamo = self.model.oil_relative_permeability(Sw)
lamo /= self.model.oil['viscosity']
val = lamw / (lamw + lamo)
return val
def get_total_system(self):
"""
Notes
-----
构造整个系统
二维情形：
x = [v, p, s, u0, u1]
A = [[ V, VP, None, None, None]
[ PV, P, None, PU0, PU1]
[None, SP, S, SU0, SU1]
[None, UP0, None, U00, U01]
[None, UP1, None, U10, U11]
F = [FV, FP, FS, FU0, FU1]
三维情形：
x = [v, p, s, u0, u1, u2]
A = [[ V, VP, None, None, None, None]
[ PV, P, None, PU0, PU1, PU2]
[None, SP, S, SU0, SU1, SU2]
[None, UP0, None, U00, U01, U02]
[None, UP1, None, U10, U11, U12]
[None, UP2, None, U20, U21, U22]]
F = [FV, FP, FS, FU0, FU1, FU2]
FS 中考虑的迎风格式
"""
GD = self.GD
A0, FV, isBdDof0 = self.get_velocity_system(q=2)
A1, FP, isBdDof1 = self.get_pressure_system(q=2)
A2, FS, isBdDof2 = self.get_saturation_system(q=2)
if GD == 2:
A3, A4, FU, isBdDof3 = self.get_dispacement_system(q=2)
A = bmat([A0, A1, A2, A3, A4], format='csr')
F = np.r_['0', FV, FP, FS, FU]
elif GD == 3:
A3, A4, A5, FU, isBdDof3 = self.get_dispacement_system(q=2)
A = bmat([A0, A1, A2, A3, A4, A5], format='csr')
F = np.r_['0', FV, FP, FS, FU]
isBdDof = np.r_['0', isBdDof0, isBdDof1, isBdDof2, isBdDof3]
return A, F, isBdDof
def get_velocity_system(self, q=2):
"""
Notes
-----
计算速度方程对应的离散系统.
if GD == 2:
[ V, VP, None, None, None]
elif GD == 3:
[ V, VP, None, None, None, None]
"""
GD = self.GD
dt = self.timeline.current_time_step_length()
cellmeasure = self.mesh.entity_measure('cell')
qf = self.mesh.integrator(q, etype='cell')
bcs, ws = qf.get_quadrature_points_and_weights()
# 速度对应的矩阵 V
c = self.flux_coefficient()
c *= cellmeasure
phi = self.vspace.basis(bcs)
V = np.einsum('q, qcin, qcjn, c->cij', ws, phi, phi, c, optimize=True)
c2d = self.vspace.cell_to_dof()
I = np.broadcast_to(c2d[:, :, None], shape=V.shape)
J = np.broadcast_to(c2d[:, None, :], shape=V.shape)
gdof = self.vspace.number_of_global_dofs()
V = csr_matrix((V.flat, (I.flat, J.flat)), shape=(gdof, gdof))
# 压强矩阵
VP = -self.B
# 右端向量, 0 向量
FV = np.zeros(gdof, dtype=np.float64)
isBdDof = self.vspace.dof.is_boundary_dof()
if GD == 2:
return [V, VP, None, None, None], FV, isBdDof
elif GD == 3:
return [V, VP, None, None, None, None], FV, isBdDof
def get_pressure_system(self, q=2):
"""
Notes
-----
计算压强方程对应的离散系统
这里组装矩阵时, 利用了压强是分片常数的特殊性
[ PV, P, None, PU0, PU1]
[ PV, P, None, PU0, PU1, PU2]
"""
GD = self.GD
dt = self.timeline.current_time_step_length()
cellmeasure = self.mesh.entity_measure('cell')
qf = self.mesh.integrator(q, etype='cell')
bcs, ws = qf.get_quadrature_points_and_weights()
PV = dt * self.B.T
# P 是对角矩阵, 利用分片常数的
c = self.pressure_coefficient() # (NQ, NC)
c *= cellmeasure
P = diags(c, 0)
# 组装压强方程的右端向量
# * 这里利用了压强空间基是分片常数
FP = self.fo.value(bcs) + self.fw.value(bcs) # (NQ, NC)
FP *= ws[:, None]
FP = np.sum(FP, axis=0)
FP *= cellmeasure
FP *= dt
FP += P @ self.p # 上一步的压强向量
FP += self.PU0 @ self.u[:, 0]
FP += self.PU1 @ self.u[:, 1]
if GD == 3:
FP += self.PU2 @ self.u[:, 2]
gdof = self.pspace.number_of_global_dofs()
isBdDof = np.zeros(gdof, dtype=np.bool_)
if GD == 2:
return [PV, P, None, self.PU0, self.PU1], FP, isBdDof
elif GD == 3:
return [PV, P, None, self.PU0, self.PU1, self.PU2], FP, isBdDof
def get_saturation_system(self, q=2):
"""
Notes
----
计算饱和度方程对应的离散系统
[ None, SP, S, SU0, SU1]
"""
GD = self.GD
qf = self.mesh.integrator(q, etype='cell')
bcs, ws = qf.get_quadrature_points_and_weights()
cellmeasure = self.mesh.entity_measure('cell')
# SP 是对角矩阵
c = self.saturation_pressure_coefficient() # (NC, )
c *= cellmeasure
SP = diags(c, 0)
# S 质量矩阵组装, 对角矩阵
c = self.cphi[:] * cellmeasure
S = diags(c, 0)
# SU0, SU1, 饱和度方程中的位移散度对应的矩阵
val = self.mesh.grad_lambda() # (NC, TD+1, GD)
c = self.cs[:] * self.model.rock['biot'] # (NC, ), 注意用当前的水饱和度
c *= cellmeasure
val *= c[:, None, None]
pgdof = self.pspace.number_of_global_dofs() # 压力空间自由度个数
cgdof = self.cspace.number_of_global_dofs() # 连续空间自由度个数
pc2d = self.pspace.cell_to_dof()
cc2d = self.cspace.cell_to_dof()
I = np.broadcast_to(pc2d, shape=cc2d.shape)
J = cc2d
SU0 = csr_matrix((val[..., 0].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof))
SU1 = csr_matrix((val[..., 1].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof))
if GD == 3:
SU2 = csr_matrix((val[..., 2].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof))
# 右端矩阵
dt = self.timeline.current_time_step_length()
FS = self.fw.value(bcs) # (NQ, NC)
FS *= ws[:, None]
FS = np.sum(FS, axis=0)
FS *= cellmeasure
FS *= dt
FS += SP @ self.p # 上一时间步的压强
FS += S @ self.s # 上一时间步的饱和度
FS += SU0 @ self.u[:, 0] # 上一时间步的位移 x 分量
FS += SU1 @ self.u[:, 1] # 上一个时间步的位移 y 分量
if GD == 3:
FS += SU2 @ self.u[:, 2] # 上一个时间步的位移 y 分量
# 用当前时刻的速度场, 构造非线性迎风格式
face2cell = self.mesh.ds.face_to_cell()
isBdFace = face2cell[:, 0] == face2cell[:, 1]
qf = self.mesh.integrator(2, 'face') # 边上的积分公式
bcs, ws = qf.get_quadrature_points_and_weights()
facemeasure = self.mesh.entity_measure('face')
# 边的定向法线，它是左边单元的外法线，右边单元内法线。
fn = self.mesh.face_unit_normal()
val0 = np.einsum('qfm, fm->qf', self.cv.face_value(bcs),
fn) # 当前速度和法线的内积
# 水的流动分数, 与水的饱和度有关, 如果饱和度为0, 则为 0
Fw = self.water_fractional_flow_coefficient()
val1 = Fw[face2cell[:, 0]] # 边的左边单元的水流动分数
val2 = Fw[face2cell[:, 1]] # 边的右边单元的水流动分数
val2[isBdFace] = 0.0 # 边界的贡献是 0
flag = val0 < 0.0 # 对于左边单元来说，是流出项
# 对于右边单元来说，是流入项
# 左右单元流入流出的绝对量是一样的
val = val0 * val1[None, :] # val0 >= 0.0, 左边单元是流出
# val0 < 0.0, 左边单元是流入
val[flag] = (val0 * val2[None, :])[flag] # val0 >= 0, 右边单元是流入
# val0 < 0, 右边单元是流出
b = np.einsum('q, qf, f->f', ws, val, facemeasure)
b *= dt
np.subtract.at(FS, face2cell[:, 0], b)
isInFace = ~isBdFace # 只处理内部边
np.add.at(FS, face2cell[isInFace, 1], b[isInFace])
isBdDof = np.zeros(pgdof, dtype=np.bool_)
if GD == 2:
return [None, SP, S, SU0, SU1], FS, isBdDof
elif GD == 3:
return [None, SP, S, SU0, SU1, SU2], FS, isBdDof
def get_dispacement_system(self, q=2):
"""
Notes
-----
计算位移方程对应的离散系统, 即线弹性方程系统.
GD == 2:
[None, UP0, None, U00, U01]
[None, UP1, None, U10, U11]
GD == 3:
[None, UP0, None, U00, U01, U02]
[None, UP1, None, U10, U11, U12]
[None, UP2, None, U20, U21, U22]
"""
GD = self.GD
# 拉梅参数 (lambda, mu)
lam, mu = self.model.rock['lame']
U = self.cspace.linear_elasticity_matrix(lam, mu, format='list')
isBdDof = self.cspace.dof.is_boundary_dof()
if GD == 2:
return ([None, -self.PU0.T, None, U[0][0],
U[0][1]], [None, -self.PU1.T, None, U[1][0],
U[1][1]], self.FU, np.r_['0', isBdDof,
isBdDof])
elif GD == 3:
return ([None, -self.PU0.T, None, U[0][0], U[0][1], U[0][2]
], [None, -self.PU1.T, None, U[1][0], U[1][1], U[1][2]],
[None, -self.PU2.T, None, U[2][0], U[2][1],
U[2][2]], self.FU, np.r_['0', isBdDof, isBdDof, isBdDof])
def picard_iteration(self, maxit=10):
GD = self.GD
e0 = 1.0
k = 0
while e0 > 1e-10:
# 构建总系统
A, F, isBdDof = self.get_total_system()
# 处理边界条件, 这里是 0 边界
gdof = len(isBdDof)
bdIdx = np.zeros(gdof, dtype=np.int_)
bdIdx[isBdDof] = 1
Tbd = diags(bdIdx)
T = diags(1 - bdIdx)
A = T @ A @ T + Tbd
F[isBdDof] = 0.0
# 求解
x = spsolve(A, F)
vgdof = self.vspace.number_of_global_dofs()
pgdof = self.pspace.number_of_global_dofs()
cgdof = self.cspace.number_of_global_dofs()
e0 = 0.0
start = 0
end = vgdof
self.cv[:] = x[start:end]
start = end
end += pgdof
e0 += np.sum((self.cp - x[start:end])**2)
self.cp[:] = x[start:end]
start = end
end += pgdof
e0 += np.sum((self.cs - x[start:end])**2)
self.cs[:] = x[start:end]
e0 = np.sqrt(e0) # 误差的 l2 norm
k += 1
for i in range(GD):
start = end
end += cgdof
self.cu[:, i] = x[start:end]
print(e0)
if k >= maxit:
print('picard iteration arrive max iteration with error:', e0)
break
def update_solution(self):
self.v[:] = self.cv
self.p[:] = self.cp
self.s[:] = self.cs
#flag = self.s < 0.0
#self.s[flag] = 0.0
self.u[:] = self.cu
def solve(self, step=24):
"""
Notes
-----
计算所有的时间层。
"""
timeline = self.timeline
dt = timeline.current_time_step_length()
timeline.reset() # 时间置零
n = timeline.current
fname = 'test_' + str(n).zfill(10) + '.vtu'
self.write_to_vtk(fname)
while not timeline.stop():
ct = timeline.current_time_level() / 3600 / 24 # 天为单位
print('当前时刻为第', ct, '天')
self.picard_iteration()
self.update_solution()
timeline.current += 1
if timeline.current % step == 0:
n = timeline.current
fname = 'test_' + str(n).zfill(10) + '.vtu'
self.write_to_vtk(fname)
timeline.reset()
def write_to_vtk(self, fname):
# 重心处的值
mesh = self.mesh
GD = self.GD
bc = np.array((GD + 1) * [1 / (GD + 1)], dtype=np.float64)
ps = self.mesh.bc_to_point(bc)
vmesh = vtk.vtkUnstructuredGrid()
vmesh.SetPoints(self.points)
vmesh.SetCells(self.cellType, self.cells)
cdata = vmesh.GetCellData()
pdata = vmesh.GetPointData()
v = self.v
p = self.p
s = self.s
u = self.u
val = v.value(bc)
if GD == 2:
val = np.concatenate(
(val, np.zeros((val.shape[0], 1), dtype=val.dtype)), axis=1)
val = vnp.numpy_to_vtk(val)
val.SetName('velocity')
cdata.AddArray(val)
val = self.recover(p[:])
val = vnp.numpy_to_vtk(val)
val.SetName('pressure')
pdata.AddArray(val)
val = self.recover(s[:])
val = vnp.numpy_to_vtk(val)
val.SetName('saturation')
pdata.AddArray(val)
if GD == 2:
val = np.concatenate((u[:], np.zeros(
(u.shape[0], 1), dtype=u.dtype)),
axis=1)
val = vnp.numpy_to_vtk(val)
val.SetName('displacement')
pdata.AddArray(val)
writer = vtk.vtkXMLUnstructuredGridWriter()
writer.SetFileName(fname)
writer.SetInputData(vmesh)
writer.Write()
errorMatrix = [[], []]
k = 0
while True:
mesh = tritree.to_conformmesh()
fname = './test-' + str(k) + '.png'
mesh.add_plot(plt)
plt.savefig(fname)
plt.close()
space = LagrangeFiniteElementSpace(mesh, p=1)
A = space.stiff_matrix(q=2)
F = space.source_vector(pde.source)
NDof += [space.number_of_global_dofs()]
bc = DirichletBC(space, pde.dirichlet)
uh = space.function()
A, F = bc.apply(A, F, uh)
uh[:] = spsolve(A, F)
rguh = space.grad_recovery(uh)
errorL1 = space.integralalg.error(pde.solution, uh.value)
errorH1 = space.integralalg.error(pde.gradient, uh.grad_value)
errorMatrix[0] += [errorL1]
errorMatrix[1] += [errorH1]
if NDof[-1] < maxdof:
eta = space.recovery_estimate(uh)
errorType = [
'$|| u - u_h||_{\Omega,0}$', # L2 误差
'$||\\nabla u - \\nabla u_h||_{\Omega, 0}$' # H1 误差
]
errorMatrix = np.zeros((2, maxit), dtype=np.float)
NDof = np.zeros(maxit, dtype=np.float)
# 初始网格
fig = plt.figure()
axes = fig.gca()
mesh.add_plot(axes)
for i in range(maxit):
space = LagrangeFiniteElementSpace(mesh, p=p) # 建立有限元空间
NDof[i] = space.number_of_global_dofs() # 有限元空间自由度的个数
bc = DirichletBC(space, pde.dirichlet) # DirichletBC 条件
uh = space.function() # 有限元函数
A = space.stiff_matrix() # 刚度矩阵
F = space.source_vector(pde.source) # 载荷向量
A, F = bc.apply(A, F, uh) # 处理边界条件
uh[:] = spsolve(A, F).reshape(-1) # 稀疏矩阵直接解法器
#ml = pyamg.ruge_stuben_solver(A) # 代数多重网格解法器
#uh[:] = ml.solve(F, tol=1e-12, accel='cg').reshape(-1)
errorMatrix[0, i] = space.integralalg.L2_error(pde.solution,
uh) # 计算 L2 误差
class ParallelTwoFluidsWithGeostressSimulator():
"""
Notes
-----
这是一个并行模拟两种流体和地质力学耦合的程序, 这里只是线性系统是并行的
* S_0: 流体 0 的饱和度，一般是水
* S_1: 流体 1 的饱和度，可以为气或油
* v: 总速度
* p: 压力
* u: 岩石位移
饱和度 P0 间断元，单元边界用迎风格式
速度 RT0 元
压强 P0 元
岩石位移 P1 连续元离散
目前, 模型
* 忽略了毛细管压强和重力作用
* 饱和度用分片线性间断元求解, 非线性的迎风格式
渐近解决方案:
1. Picard 迭代
2. 气的可压性随着压强的变化而变化
3. 考虑渗透率随着孔隙度的变化而变化
4. 考虑裂缝，裂缝用自适应网格加密，设设置更大的渗透率实现
体积模量: K = E/3/(1 - 2*nu) = lambda + 2/3* mu
Develop
------
"""
def __init__(self, mesh, args, ctx):
self.ctx = ctx
self.args = args # 模拟相关参数
NT = int((args.T1 - args.T0)/args.DT)
self.timeline = UniformTimeLine(args.T0, args.T1, NT)
self.mesh = mesh
if self.ctx.myid == 0:
self.GD = mesh.geo_dimension()
if self.GD == 2:
self.vspace = RaviartThomasFiniteElementSpace2d(self.mesh, p=0) # 速度空间
elif self.GD == 3:
self.vspace = RaviartThomasFiniteElementSpace3d(self.mesh, p=0)
self.pspace = self.vspace.smspace # 压力和饱和度所属的空间, 分片常数
self.cspace = LagrangeFiniteElementSpace(self.mesh, p=1) # 位移空间
# 上一时刻物理量的值
self.v = self.vspace.function() # 速度函数
self.p = self.pspace.function() # 压力函数
self.s = self.pspace.function() # 水的饱和度函数 默认为0, 初始时刻区域内水的饱和度为0
self.u = self.cspace.function(dim=self.GD) # 位移函数
self.phi = self.pspace.function() # 孔隙度函数, 分片常数
# 当前时刻物理量的值, 用于保存临时计算出的值, 模型中系数的计算由当前时刻
# 的物理量的值决定
self.cv = self.vspace.function() # 速度函数
self.cp = self.pspace.function() # 压力函数
self.cs = self.pspace.function() # 水的饱和度函数 默认为0, 初始时刻区域内水的饱和度为0
self.cu = self.cspace.function(dim=self.GD) # 位移函数
self.cphi = self.pspace.function() # 孔隙度函数, 分片常数
# 初值
self.s[:] = self.mesh.celldata['fluid_0']
self.p[:] = self.mesh.celldata['pressure_0'] # MPa
self.phi[:] = self.mesh.celldata['porosity_0'] # 初始孔隙度
self.s[:] = self.mesh.celldata['fluid_0']
self.cp[:] = self.p # 初始地层压力
self.cphi[:] = self.phi # 当前孔隙度系数
# 源汇项
self.f0 = self.cspace.function()
self.f1 = self.cspace.function()
self.f0[:] = self.mesh.nodedata['source_0'] # 流体 0 的源汇项
self.f1[:] = self.mesh.nodedata['source_1'] # 流体 1 的源汇项
# 一些常数矩阵和向量
# 速度散度矩阵, 速度方程对应的散度矩阵, (\nabla\cdot v, w)
self.B = self.vspace.div_matrix()
# 压力方程对应的位移散度矩阵, (\nabla\cdot u, w) 位移散度矩阵
# * 注意这里利用了压力空间分片常数, 线性函数导数也是分片常数的事实
c = self.mesh.entity_measure('cell')
c *= self.mesh.celldata['biot']
val = self.mesh.grad_lambda() # (NC, TD+1, GD)
val *= c[:, None, None]
pc2d = self.pspace.cell_to_dof()
cc2d = self.cspace.cell_to_dof()
pgdof = self.pspace.number_of_global_dofs()
cgdof = self.cspace.number_of_global_dofs()
I = np.broadcast_to(pc2d, shape=cc2d.shape)
J = cc2d
self.PU0 = csr_matrix(
(val[..., 0].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof)
)
self.PU1 = csr_matrix(
(val[..., 1].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof)
)
if self.GD == 3:
self.PU2 = csr_matrix(
(val[..., 2].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof)
)
# 线弹性矩阵的右端向量
self.FU = np.zeros(self.GD*cgdof, dtype=np.float64)
self.FU[0*cgdof:1*cgdof] -= [email protected]
self.FU[1*cgdof:2*cgdof] -= [email protected]
if self.GD == 3:
self.FU[2*cgdof:3*cgdof] -= [email protected]
# 初始应力和等效应力项
sigma = self.mesh.celldata['stress_0'] + self.mesh.celldata['stress_eff']# 初始应力和等效应力之和
self.FU[0*cgdof:1*cgdof] -= [email protected]
self.FU[1*cgdof:2*cgdof] -= [email protected]
if self.GD == 3:
self.FU[2*cgdof:3*cgdof] -= [email protected]
def recover(self, val):
"""
Notes
-----
给定一个分片常数的量, 恢复为分片连续的量
"""
mesh = self.mesh
cell = self.mesh.entity('cell')
NC = mesh.number_of_cells()
NN = mesh.number_of_nodes()
w = 1/self.mesh.entity_measure('cell') # 恢复权重
w = np.broadcast_to(w[:, None], shape=cell.shape)
r = np.zeros(NN, dtype=np.float64)
d = np.zeros(NN, dtype=np.float64)
np.add.at(d, cell, w)
np.add.at(r, cell, val[:, None]*w)
return r/d
def add_time(self, n):
"""
Notes
----
增加 n 步的计算时间
"""
if self.ctx.myid == 0:
self.timeline.add_time(n)
def pressure_coefficient(self):
"""
Notes
-----
计算当前物理量下的压强质量矩阵系数
"""
b = self.mesh.celldata['biot']
K = self.mesh.celldata['K']
c0 = self.mesh.meshdata['fluid_0']['compressibility']
c1 = self.mesh.meshdata['fluid_1']['compressibility']
s = self.cs # 流体 0 当前的饱和度 (NQ, NC)
phi = self.cphi # 当前的孔隙度
val = phi*s*c0
val += phi*(1 - s)*c1 # 注意这里的 co 是常数, 但在水气混合物条件下应该依赖于压力
val += (b - phi)/K
return val
def saturation_pressure_coefficient(self):
"""
Notes
-----
计算当前物理量下, 饱和度方程中, 压强项对应的系数
"""
b = self.mesh.celldata['biot']
K = self.mesh.celldata['K']
c0 = self.mesh.meshdata['fluid_0']['compressibility']
s = self.cs # 当前流体 0 的饱和度
phi = self.cphi # 当前孔隙度
val = (b - phi)/K
val += phi*c0
val *= s
return val
def flux_coefficient(self):
"""
Notes
-----
计算**当前**物理量下, 速度方程对应的系数
计算通量的系数, 流体的相对渗透率除以粘性系数得到流动性系数
k_0/mu_0 是气体的流动性系数
k_1/mu_1 是油的流动性系数
这里假设压强的单位是 MPa
1 d = 9.869 233e-13 m^2 = 1000 md
1 cp = 1 mPa s = 1e-9 MPa*s = 1e-3 Pa*s
"""
mu0 = self.mesh.meshdata['fluid_0']['viscosity'] # 单位是 1 cp = 1 mPa.s
mu1 = self.mesh.meshdata['fluid_1']['viscosity'] # 单位是 1 cp = 1 mPa.s
# 岩石的绝对渗透率, 这里考虑了量纲的一致性, 压强单位是 Pa
k = self.mesh.celldata['permeability']*9.869233e-4
s = self.cs # 流体 0 当前的饱和度系数
lam0 = self.mesh.fluid_relative_permeability_0(s) # TODO: 考虑更复杂的饱和度和渗透的关系
lam0 /= mu0
lam1 = self.mesh.fluid_relative_permeability_1(s) # TODO: 考虑更复杂的饱和度和渗透的关系
lam1 /= mu1
val = 1/(lam0 + lam1)
val /=k
return val
def fluid_fractional_flow_coefficient_0(self):
"""
Notes
-----
计算**当前**物理量下, 饱和度方程中, 主流体的的流动性系数
"""
s = self.cs # 流体 0 当前的饱和度系数
mu0 = self.mesh.meshdata['fluid_0']['viscosity'] # 单位是 1 cp = 1 mPa.s
lam0 = self.mesh.fluid_relative_permeability_0(s)
lam0 /= mu0
lam1 = self.mesh.fluid_relative_permeability_1(s)
mu1 = self.mesh.meshdata['fluid_1']['viscosity'] # 单位是 1 cp = 1 mPa.s
lam1 /= mu1
val = lam0/(lam0 + lam1)
return val
def get_total_system(self):
"""
Notes
-----
构造整个系统
二维情形：
x = [s, v, p, u0, u1]
A = [[ S, None, SP, SU0, SU1]
[None, V, VP, None, None]
[None, PV, P, PU0, PU1]
[None, None, UP0, U00, U01]
[None, None, UP1, U10, U11]]
F = [FS, FV, FP, FU0, FU1]
三维情形：
x = [s, v, p, u0, u1, u2]
A = [[ S, None, SP, SU0, SU1, SU2]
[None, V, VP, None, None, None]
[None, PV, P, PU0, PU1, PU2]
[None, None, UP0, U00, U01, U02]
[None, None, UP1, U10, U11, U12]
[None, None, UP2, U20, U21, U22]]
F = [FS, FV, FP, FU0, FU1, FU2]
"""
GD = self.GD
A0, FS, isBdDof0 = self.get_saturation_system(q=2)
A1, FV, isBdDof1 = self.get_velocity_system(q=2)
A2, FP, isBdDof2 = self.get_pressure_system(q=2)
if GD == 2:
A3, A4, FU, isBdDof3 = self.get_dispacement_system(q=2)
A = bmat([A0, A1, A2, A3, A4], format='csr')
F = np.r_['0', FS, FV, FP, FU]
elif GD == 3:
A3, A4, A5, FU, isBdDof3 = self.get_dispacement_system(q=2)
A = bmat([A0, A1, A2, A3, A4, A5], format='csr')
F = np.r_['0', FS, FV, FP, FU]
isBdDof = np.r_['0', isBdDof0, isBdDof1, isBdDof2, isBdDof3]
return A, F, isBdDof
def get_saturation_system(self, q=2):
"""
Notes
----
计算饱和度方程对应的离散系统
[ S, None, SP, SU0, SU1]
[ S, None, SP, SU0, SU1, SU2]
"""
GD = self.GD
qf = self.mesh.integrator(q, etype='cell')
bcs, ws = qf.get_quadrature_points_and_weights()
cellmeasure = self.mesh.entity_measure('cell')
# SP 是对角矩阵
c = self.saturation_pressure_coefficient() # (NC, )
c *= cellmeasure
SP = diags(c, 0)
# S 质量矩阵组装, 对角矩阵
c = self.cphi[:]*cellmeasure
S = diags(c, 0)
# SU0, SU1, 饱和度方程中的位移散度对应的矩阵
b = self.mesh.celldata['biot']
val = self.mesh.grad_lambda() # (NC, TD+1, GD)
c = self.cs[:]*b # (NC, ), 注意用当前的水饱和度
c *= cellmeasure
val *= c[:, None, None]
pgdof = self.pspace.number_of_global_dofs() # 压力空间自由度个数
cgdof = self.cspace.number_of_global_dofs() # 连续空间自由度个数
pc2d = self.pspace.cell_to_dof()
cc2d = self.cspace.cell_to_dof()
I = np.broadcast_to(pc2d, shape=cc2d.shape)
J = cc2d
SU0 = csr_matrix(
(val[..., 0].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof)
)
SU1 = csr_matrix(
(val[..., 1].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof)
)
if GD == 3:
SU2 = csr_matrix(
(val[..., 2].flat, (I.flat, J.flat)),
shape=(pgdof, cgdof)
)
# 右端矩阵
dt = self.timeline.current_time_step_length()
FS = self.f0.value(bcs) # (NQ, NC)
FS *= ws[:, None]
FS = np.sum(FS, axis=0)
FS *= cellmeasure
FS *= dt
FS += [email protected] # 上一时间步的压强
FS += [email protected] # 上一时间步的饱和度
FS += [email protected][:, 0] # 上一时间步的位移 x 分量
FS += [email protected][:, 1] # 上一个时间步的位移 y 分量
if GD == 3:
FS += [email protected][:, 2] # 上一个时间步的位移 z 分量
# 用当前时刻的速度场, 构造迎风格式
face2cell = self.mesh.ds.face_to_cell()
isBdFace = face2cell[:, 0] == face2cell[:, 1]
qf = self.mesh.integrator(2, 'face') # 边或面上的积分公式
bcs, ws = qf.get_quadrature_points_and_weights()
facemeasure = self.mesh.entity_measure('face')
# 边的定向法线，它是左边单元的外法线，右边单元内法线。
fn = self.mesh.face_unit_normal()
val0 = np.einsum('qfm, fm->qf', self.cv.face_value(bcs), fn) # 当前速度和法线的内积
# 流体 0 的流动分数, 与水的饱和度有关, 如果饱和度为0, 则为 0
F0 = self.fluid_fractional_flow_coefficient_0()
val1 = F0[face2cell[:, 0]] # 边的左边单元的水流动分数
val2 = F0[face2cell[:, 1]] # 边的右边单元的水流动分数
val2[isBdFace] = 0.0 # 边界的贡献是 0，没有流入和流出
flag = val0 < 0.0 # 对于左边单元来说，是流入项
# 对于右边单元来说，是流出项
# 左右单元流入流出的绝对量是一样的
val = val0*val1[None, :] # val0 >= 0.0, 左边单元是流出
# val0 < 0.0, 左边单元是流入
val[flag] = (val0*val2[None, :])[flag] # val0 >= 0, 右边单元是流入
# val0 < 0, 右边单元是流出
b = np.einsum('q, qf, f->f', ws, val, facemeasure)
b *= dt
np.subtract.at(FS, face2cell[:, 0], b)
isInFace = ~isBdFace # 只处理内部边
np.add.at(FS, face2cell[isInFace, 1], b[isInFace])
isBdDof = np.zeros(pgdof, dtype=np.bool_)
if GD == 2:
return [ S, None, SP, SU0, SU1], FS, isBdDof
elif GD == 3:
return [ S, None, SP, SU0, SU1, SU2], FS, isBdDof
def get_velocity_system(self, q=2):
"""
Notes
-----
计算速度方程对应的离散系统.
[None, V, VP, None, None]
[None, V, VP, None, None, None]
"""
GD = self.GD
dt = self.timeline.current_time_step_length()
cellmeasure = self.mesh.entity_measure('cell')
qf = self.mesh.integrator(q, etype='cell')
bcs, ws = qf.get_quadrature_points_and_weights()
# 速度对应的矩阵 V
c = self.flux_coefficient()
c *= cellmeasure
phi = self.vspace.basis(bcs)
V = np.einsum('q, qcin, qcjn, c->cij', ws, phi, phi, c, optimize=True)
c2d = self.vspace.cell_to_dof()
I = np.broadcast_to(c2d[:, :, None], shape=V.shape)
J = np.broadcast_to(c2d[:, None, :], shape=V.shape)
gdof = self.vspace.number_of_global_dofs()
V = csr_matrix(
(V.flat, (I.flat, J.flat)),
shape=(gdof, gdof)
)
# 压强矩阵
VP = -self.B
# 右端向量, 0 向量
FV = np.zeros(gdof, dtype=np.float64)
isBdDof = self.vspace.dof.is_boundary_dof()
if GD == 2:
return [None, V, VP, None, None], FV, isBdDof
elif GD == 3:
return [None, V, VP, None, None, None], FV, isBdDof
def get_pressure_system(self, q=2):
"""
Notes
-----
计算压强方程对应的离散系统
这里组装矩阵时, 利用了压强是分片常数的特殊性
[ Noe, PV, P, PU0, PU1]
[ None, PV, P, PU0, PU1, PU2]
"""
GD = self.GD
dt = self.timeline.current_time_step_length()
cellmeasure = self.mesh.entity_measure('cell')
qf = self.mesh.integrator(q, etype='cell')
bcs, ws = qf.get_quadrature_points_and_weights()
PV = dt*self.B.T
# P 是对角矩阵, 利用分片常数的
c = self.pressure_coefficient() # (NQ, NC)
c *= cellmeasure
P = diags(c, 0)
# 组装压力方程的右端向量
# * 这里利用了压力空间基是分片常数
FP = self.f1.value(bcs) + self.f0.value(bcs) # (NQ, NC)
FP *= ws[:, None]
FP = np.sum(FP, axis=0)
FP *= cellmeasure
FP *= dt
FP += [email protected] # 上一步的压力向量
FP += [email protected][:, 0]
FP += [email protected][:, 1]
if GD == 3:
FP += [email protected][:, 2]
gdof = self.pspace.number_of_global_dofs()
isBdDof = np.zeros(gdof, dtype=np.bool_)
if GD == 2:
return [None, PV, P, self.PU0, self.PU1], FP, isBdDof
elif GD == 3:
return [None, PV, P, self.PU0, self.PU1, self.PU2], FP, isBdDof
def get_dispacement_system(self, q=2):
"""
Notes
-----
计算位移方程对应的离散系统, 即线弹性方程系统.
GD == 2:
[None, None, UP0, U00, U01]
[None, None, UP1, U10, U11]
GD == 3:
[None, None, UP0, U00, U01, U02]
[None, None, UP1, U10, U11, U12]
[None, None, UP2, U20, U21, U22]
"""
GD = self.GD
# 拉梅参数 (lambda, mu)
lam = self.mesh.celldata['lambda']
mu = self.mesh.celldata['mu']
U = self.linear_elasticity_matrix(lam, mu, format='list')
isBdDof = self.cspace.dof.is_boundary_dof()
if GD == 2:
return (
[None, None, -self.PU0.T, U[0][0], U[0][1]],
[None, None, -self.PU1.T, U[1][0], U[1][1]],
self.FU, np.r_['0', isBdDof, isBdDof]
)
elif GD == 3:
return (
[None, None, -self.PU0.T, U[0][0], U[0][1], U[0][2]],
[None, None, -self.PU1.T, U[1][0], U[1][1], U[1][2]],
[None, None, -self.PU2.T, U[2][0], U[2][1], U[2][2]],
self.FU, np.r_['0', isBdDof, isBdDof, isBdDof]
)
def linear_elasticity_matrix(self, lam, mu, format='csr', q=None):
"""
Notes
-----
注意这里的拉梅常数是单元分片常数
lam.shape == (NC, ) # MPa
mu.shape == (NC, ) # MPa
"""
GD = self.GD
if GD == 2:
idx = [(0, 0), (0, 1), (1, 1)]
imap = {(0, 0):0, (0, 1):1, (1, 1):2}
elif GD == 3:
idx = [(0, 0), (0, 1), (0, 2), (1, 1), (1, 2), (2, 2)]
imap = {(0, 0):0, (0, 1):1, (0, 2):2, (1, 1):3, (1, 2):4, (2, 2):5}
A = []
qf = self.cspace.integrator if q is None else self.mesh.integrator(q, 'cell')
bcs, ws = qf.get_quadrature_points_and_weights()
grad = self.cspace.grad_basis(bcs) # (NQ, NC, ldof, GD)
# 分块组装矩阵
gdof = self.cspace.number_of_global_dofs()
cellmeasure = self.cspace.cellmeasure
for k, (i, j) in enumerate(idx):
Aij = np.einsum('i, ijm, ijn, j->jmn', ws, grad[..., i], grad[..., j], cellmeasure)
A.append(Aij)
if GD == 2:
C = [[None, None], [None, None]]
D = mu[:, None, None]*(A[0] + A[2])
elif GD == 3:
C = [[None, None, None], [None, None, None], [None, None, None]]
D = mu[:, None, None]*(A[0] + A[3] + A[5])
cell2dof = self.cspace.cell_to_dof() # (NC, ldof)
ldof = self.cspace.number_of_local_dofs()
NC = self.mesh.number_of_cells()
shape = (NC, ldof, ldof)
I = np.broadcast_to(cell2dof[:, :, None], shape=shape)
J = np.broadcast_to(cell2dof[:, None, :], shape=shape)
for i in range(GD):
Aii = D + (mu + lam)[:, None, None]*A[imap[(i, i)]]
C[i][i] = csr_matrix((Aii.flat, (I.flat, J.flat)), shape=(gdof, gdof))
for j in range(i+1, GD):
Aij = lam[:, None, None]*A[imap[(i, j)]] + mu[:, None, None]*A[imap[(i, j)]].swapaxes(-1, -2)
C[i][j] = csr_matrix((Aij.flat, (I.flat, J.flat)), shape=(gdof, gdof))
C[j][i] = C[i][j].T
if format == 'csr':
return bmat(C, format='csr') # format = bsr ??
elif format == 'bsr':
return bmat(C, format='bsr')
elif format == 'list':
return C
def solve_linear_system_0(self):
# 构建总系统
if self.ctx.myid == 0:
A, F, isBdDof = self.get_total_system()
# 处理边界条件, 这里是 0 边界
gdof = len(isBdDof)
bdIdx = np.zeros(gdof, dtype=np.int_)
bdIdx[isBdDof] = 1
Tbd = diags(bdIdx)
T = diags(1-bdIdx)
A = T@A@T + Tbd
F[isBdDof] = 0.0
# 求解
self.ctx.set_centralized_sparse(A)
x = F.copy()
self.ctx.set_rhs(x) # Modified in place
self.ctx.run(job=6) # Analysis + Factorization + Solve
if self.ctx.myid == 0:
vgdof = self.vspace.number_of_global_dofs()
pgdof = self.pspace.number_of_global_dofs()
cgdof = self.cspace.number_of_global_dofs()
start = 0
end = pgdof
s = x[start:end]
start = end
end += vgdof
v = x[start:end]
start = end
end += pgdof
p = x[start:end]
start = end
u = x[start:]
return s, v, p, u
def solve_linear_system_1(self):
"""
Notes
-----
构造整个系统
二维情形：
x = [s, v, p, u0, u1]
A = [[ S, None, SP, SU0, SU1]
[None, V, VP, None, None]
[None, PV, P, PU0, PU1]
[None, None, UP0, U00, U01]
[None, None, UP1, U10, U11]]
F = [FS, FV, FP, FU0, FU1]
三维情形：
x = [s, v, p, u0, u1, u2]
A = [[ S, None, SP, SU0, SU1, SU2]
[None, V, VP, None, None, None]
[None, PV, P, PU0, PU1, PU2]
[None, None, UP0, U00, U01, U02]
[None, None, UP1, U10, U11, U12]
[None, None, UP2, U20, U21, U22]]
F = [FS, FV, FP, FU0, FU1, FU2]
"""
if self.ctx.myid == 0:
vgdof = self.vspace.number_of_global_dofs()
pgdof = self.pspace.number_of_global_dofs()
cgdof = self.cspace.number_of_global_dofs()
GD = self.GD
A0, FS, isBdDof0 = self.get_saturation_system(q=2)
A1, FV, isBdDof1 = self.get_velocity_system(q=2)
A2, FP, isBdDof2 = self.get_pressure_system(q=2)
if GD == 2:
A3, A4, FU, isBdDof3 = self.get_dispacement_system(q=2)
A = bmat([A1[1:], A2[1:], A3[1:], A4[1:]], format='csr')
F = np.r_['0', FV, FP, FU]
elif GD == 3:
A3, A4, A5, FU, isBdDof3 = self.get_dispacement_system(q=2)
A = bmat([A1[1:], A2[1:], A3[1:], A4[1:], A5[1:]], format='csr')
F = np.r_['0', FV, FP, FU]
isBdDof = np.r_['0', isBdDof1, isBdDof2, isBdDof3]
gdof = len(isBdDof)
bdIdx = np.zeros(gdof, dtype=np.int_)
bdIdx[isBdDof] = 1
Tbd = diags(bdIdx)
T = diags(1-bdIdx)
A = T@A@T + Tbd
F[isBdDof] = 0.0
#[ S, None, SP, SU0, SU1, SU2]
self.ctx.set_centralized_sparse(A)
x = F.copy()
self.ctx.set_rhs(x) # Modified in place
self.ctx.run(job=6) # Analysis + Factorization + Solve
if self.ctx.myid == 0:
v = x[0:vgdof]
p = x[vgdof:vgdof+pgdof]
u = x[vgdof+pgdof:]
s = FS
s -= A0[2]@p
s -= A0[3]@u[0*cgdof:1*cgdof]
s -= A0[4]@u[1*cgdof:2*cgdof]
if GD == 3:
s -= A0[5]@u[2*cgdof:3*cgdof]
s /= A0[0].diagonal()
return s, v, p, u
else:
return None, None, None, None
def picard_iteration(self):
"""
"""
e0 = 1.0
k = 0
while e0 > 1e-10:
s, v, p, u = self.solve_linear_system_1()
if self.ctx.myid == 0:
e0 = 0.0
e0 += np.sum((self.cs - s)**2)
self.cs[:] = s
e0 += np.sum((self.cp - p)**2)
self.cp[:] = p
self.cv[:] = v
self.cu.T.flat[:] = u
e0 = np.sqrt(e0) # 误差的 l2 norm
print(e0)
e0 = self.ctx.comm.bcast(e0, root=0)
k += 1
if k >= self.args.npicard:
if self.ctx.myid == 0:
print('picard iteration arrive max iteration with error:', e0)
break
if self.ctx.myid == 0:
# 更新解
self.s[:] = self.cs
self.v[:] = self.cv
self.p[:] = self.cp
self.u[:] = self.cu
def update_mesh_data(self):
"""
Notes
-----
更新 mesh 中的数据
"""
GD = self.GD
bc = np.array((GD+1)*[1/(GD+1)], dtype=np.float64)
mesh = self.mesh
v = self.v
p = self.p
s = self.s
u = self.u
# 单元中心的流体速度
val = v.value(bc)
if GD == 2:
val = np.concatenate((val, np.zeros((val.shape[0], 1), dtype=val.dtype)), axis=1)
mesh.celldata['velocity'] = val
# 分片常数的压强
val = self.recover(p[:])
mesh.nodedata['pressure'] = val
# 分片常数的饱和度
val = self.recover(s[:])
mesh.nodedata['fluid_0'] = val
val = self.recover(1 - s)
mesh.nodedata['fluid_1'] = val
# 节点处的位移
if GD == 2:
u = np.concatenate((u[:], np.zeros((u.shape[0], 1), dtype=u.dtype)), axis=1)
mesh.nodedata['displacement'] = u[:]
# 增加应变和应力的计算
NC = self.mesh.number_of_cells()
s0 = np.zeros((NC, 3, 3), dtype=np.float64)
s1 = np.zeros((NC, 3, 3), dtype=np.float64)
s = u.grad_value(bc) # (NC, GD, GD)
s0[:, 0:GD, 0:GD] = s + s.swapaxes(-1, -2)
s0[:, 0:GD, 0:GD] /= 2
lam = self.mesh.celldata['lambda']
mu = self.mesh.celldata['mu']
s1[:, 0:GD, 0:GD] = s0[:, 0:GD, 0:GD]
s1[:, 0:GD, 0:GD] *= mu[:, None, None]
s1[:, 0:GD, 0:GD] *= 2
s1[:, range(GD), range(GD)] += (lam*s0.trace(axis1=-1, axis2=-2))[:, None]
s1[:, range(GD), range(GD)] += mesh.celldata['stress_0'][:, None]
s1[:, range(GD), range(GD)] -= (mesh.celldata['biot']*(p - mesh.celldata['pressure_0']))[:, None]
mesh.celldata['strain'] = s0.reshape(NC, -1)
mesh.celldata['stress'] = s1.reshape(NC, -1)
def run(self, writer=None):
"""
Notes
-----
计算所有时间层物理量。
"""
args = self.args
timeline = self.timeline
dt = timeline.current_time_step_length()
if (self.ctx.myid == 0) and (writer is not None):
n = timeline.current
fname = args.output + str(n).zfill(10) + '.vtu'
self.update_mesh_data()
writer(fname, self.mesh)
while not timeline.stop():
if self.ctx.myid == 0:
ct = timeline.current_time_level()/3600/24 # 天为单位
print('当前时刻为第', ct, '天')
self.picard_iteration()
timeline.current += 1
if timeline.current%args.step == 0:
if (self.ctx.myid == 0) and (writer is not None):
n = timeline.current
fname = args.output + str(n).zfill(10) + '.vtu'
self.update_mesh_data()
writer(fname, self.mesh)
if (self.ctx.myid == 0) and (writer is not None):
n = timeline.current
fname = args.output + str(n).zfill(10) + '.vtu'
self.update_mesh_data()
writer(fname, self.mesh)
def alg_3_4(self, maxit=None):
"""
1. 最粗网格上求解最小特征特征值问题，得到最小特征值 d_H 和特征向量 u_H
2. 自适应求解 - \Delta u_h = u_H
* u_H 插值到下一层网格上做为新 u_H
3. 最新网格层上求出的 uh 做为一个基函数，加入到最粗网格的有限元空间中，
求解最小特征值问题。
自适应 maxit， picard:2
"""
print("算法 3.4")
if maxit is None:
maxit = self.maxit
start = timer()
if self.step == 0:
idx = []
else:
idx = list(range(0, self.maxit, self.step)) + [self.maxit - 1]
mesh = self.pde.init_mesh(n=self.numrefine)
integrator = mesh.integrator(self.q)
# 1. 粗网格上求解最小特征值问题
space = LagrangeFiniteElementSpace(mesh, 1)
AH = self.get_stiff_matrix(space)
MH = self.get_mass_matrix(space)
isFreeHDof = ~(space.boundary_dof())
gdof = space.number_of_global_dofs()
print("initial mesh :", gdof)
uH = np.zeros(gdof, dtype=np.float)
A = AH[isFreeHDof, :][:, isFreeHDof].tocsr()
M = MH[isFreeHDof, :][:, isFreeHDof].tocsr()
if self.matlab is False:
uH[isFreeHDof], d = self.eig(A, M)
else:
uH[isFreeHDof], d = self.meigs(A, M)
uh = space.function()
uh[:] = uH
GD = mesh.geo_dimension()
if (self.step > 0) and (0 in idx):
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_4_0_' + str(NN) + '.pdf')
plt.close()
self.savemesh(mesh,
self.resultdir + 'mesh_3_4_0_' + str(NN) + '.mat')
# 2. 以 u_H 为右端项自适应求解 -\Deta u = u_H
I = eye(gdof)
final = 0
for i in range(maxit):
eta = self.residual_estimate(uh)
markedCell = mark(eta, self.theta)
IM = mesh.bisect(markedCell, returnim=True)
print(i + 1, "refine :", mesh.number_of_nodes())
if (self.step > 0) and (i in idx):
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_4_' + str(i + 1) + '_' +
str(NN) + '.pdf')
plt.close()
self.savemesh(
mesh, self.resultdir + 'mesh_3_4_' + str(i + 1) + '_' +
str(NN) + '.mat')
I = IM @ I
uH = IM @ uH
space = LagrangeFiniteElementSpace(mesh, 1)
gdof = space.number_of_global_dofs()
A = self.get_stiff_matrix(space)
M = self.get_mass_matrix(space)
isFreeDof = ~(space.boundary_dof())
b = M @ uH
uh = space.function()
if self.matlab is False:
uh[isFreeDof] = self.psolve(
A[isFreeDof, :][:, isFreeDof].tocsr(), b[isFreeDof],
M[isFreeDof, :][:, isFreeDof].tocsr())
else:
uh[isFreeDof] = self.msolve(
A[isFreeDof, :][:, isFreeDof].tocsr(), b[isFreeDof])
final = i + 1
if gdof > self.maxdof:
break
if self.step > 0:
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_4_' + str(final) + '_' +
str(NN) + '.pdf')
plt.close()
self.savemesh(
mesh, self.resultdir + 'mesh_3_4_' + str(final) + '_' +
str(NN) + '.mat')
# 3. 把 uh 加入粗网格空间, 组装刚度和质量矩阵
w0 = uh @ A
w1 = w0 @ uh
w2 = w0 @ I
AA = bmat([[AH, w2.reshape(-1, 1)], [w2, w1]], format='csr')
w0 = uh @ M
w1 = w0 @ uh
w2 = w0 @ I
MM = bmat([[MH, w2.reshape(-1, 1)], [w2, w1]], format='csr')
isFreeDof = np.r_[isFreeHDof, True]
u = np.zeros(len(isFreeDof))
## 求解特征值
A = AA[isFreeDof, :][:, isFreeDof].tocsr()
M = MM[isFreeDof, :][:, isFreeDof].tocsr()
if self.multieigs is True:
self.A = A
self.M = M
self.ml = pyamg.ruge_stuben_solver(self.A)
self.eigs()
else:
if self.matlab is False:
u[isFreeDof], d = self.eig(A, M)
else:
u[isFreeDof], d = self.meigs(A, M)
print("smallest eigns:", d)
uh *= u[-1]
uh += I @ u[:-1]
uh /= np.max(np.abs(uh))
uh = space.function(array=uh)
return uh
end = timer()
print("with time: ", end - start)
def alg_3_3(self, maxit=None):
"""
1. 最粗网格上求解最小特征特征值问题，得到最小特征值 d_H 和特征向量 u_H
2. 自适应求解 - \Delta u_h = u_H
* u_H 插值到下一层网格上做为新 u_H
3. 在最细网格上求解一次最小特征值问题
自适应 maxit, picard:2
"""
print("算法 3.3")
if maxit is None:
maxit = self.maxit
start = timer()
if self.step == 0:
idx = []
else:
idx = list(range(0, self.maxit, self.step))
mesh = self.pde.init_mesh(n=self.numrefine)
# 1. 粗网格上求解最小特征值问题
space = LagrangeFiniteElementSpace(mesh, 1)
AH = self.get_stiff_matrix(space)
MH = self.get_mass_matrix(space)
isFreeHDof = ~(space.boundary_dof())
gdof = space.number_of_global_dofs()
uH = np.zeros(gdof, dtype=mesh.ftype)
print("initial mesh :", gdof)
A = AH[isFreeHDof, :][:, isFreeHDof].tocsr()
M = MH[isFreeHDof, :][:, isFreeHDof].tocsr()
if self.matlab is False:
uH[isFreeHDof], d = self.eig(A, M)
else:
uH[isFreeHDof], d = self.meigs(A, M)
uh = space.function()
uh[:] = uH
GD = mesh.geo_dimension()
if (self.step > 0) and (0 in idx):
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_3_0_' + str(NN) + '.pdf')
plt.close()
self.savemesh(mesh,
self.resultdir + 'mesh_3_3_0_' + str(NN) + '.mat')
# 2. 以 u_H 为右端项自适应求解 -\Deta u = u_H
I = eye(gdof)
final = 0
for i in range(maxit - 1):
eta = self.residual_estimate(uh)
markedCell = mark(eta, self.theta)
IM = mesh.bisect(markedCell, returnim=True)
NN = mesh.number_of_nodes()
print(i + 1, "refine : ", NN)
if (self.step > 0) and (i in idx):
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_3_' + str(i + 1) + '_' +
str(NN) + '.pdf')
plt.close()
self.savemesh(
mesh, self.resultdir + 'mesh_3_3_' + str(i + 1) + '_' +
str(NN) + '.mat')
final = i + 1
if NN > self.maxdof:
break
I = IM @ I
uH = IM @ uH
space = LagrangeFiniteElementSpace(mesh, 1)
gdof = space.number_of_global_dofs()
A = self.get_stiff_matrix(space)
M = self.get_mass_matrix(space)
isFreeDof = ~(space.boundary_dof())
b = M @ uH
uh = space.function()
if self.matlab is False:
uh[isFreeDof] = self.psolve(
A[isFreeDof, :][:, isFreeDof].tocsr(), b[isFreeDof],
M[isFreeDof, :][:, isFreeDof].tocsr())
else:
uh[isFreeDof] = self.msolve(
A[isFreeDof, :][:, isFreeDof].tocsr(), b[isFreeDof])
# 3. 在最细网格上求解一次最小特征值问题
if self.step > 0:
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_3_' + str(final) + '_' +
str(NN) + '.pdf')
plt.close()
self.savemesh(
mesh, self.resultdir + 'mesh_3_3_' + str(final) + '_' +
str(NN) + '.mat')
space = LagrangeFiniteElementSpace(mesh, 1)
gdof = space.number_of_global_dofs()
A = self.get_stiff_matrix(space)
M = self.get_mass_matrix(space)
isFreeDof = ~(space.boundary_dof())
uh = space.function(array=uh)
A = A[isFreeDof, :][:, isFreeDof].tocsr()
M = M[isFreeDof, :][:, isFreeDof].tocsr()
uh = space.function()
if self.multieigs is True:
self.A = A
self.M = M
self.ml = pyamg.ruge_stuben_solver(self.A)
self.eigs()
else:
if self.matlab is False:
uh[isFreeDof], d = self.eig(A, M)
else:
uh[isFreeDof], d = self.meigs(A, M)
print("smallest eigns:", d)
return uh
end = timer()
print("with time: ", end - start)
def alg_3_2(self, maxit=None):
"""
1. 自适应求解 -\Delta u = 1。
1. 在最细网格上求最小特征值和特征向量。
refine maxit， picard: 1
"""
print("算法 3.2")
if maxit is None:
maxit = self.maxit
start = timer()
if self.step == 0:
idx = []
else:
idx = list(range(0, self.maxit, self.step)) + [self.maxit - 1]
mesh = self.pde.init_mesh(n=self.numrefine)
GD = mesh.geo_dimension()
if (self.step > 0) and (0 in idx):
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_2_0_' + str(NN) + '.pdf')
plt.close()
self.savemesh(mesh,
self.resultdir + 'mesh_3_2_0_' + str(NN) + '.mat')
final = 0
integrator = mesh.integrator(self.q)
for i in range(maxit):
space = LagrangeFiniteElementSpace(mesh, 1)
gdof = space.number_of_global_dofs()
A = self.get_stiff_matrix(space)
M = self.get_mass_matrix(space)
b = M @ np.ones(gdof)
isFreeDof = ~(space.boundary_dof())
A = A[isFreeDof, :][:, isFreeDof].tocsr()
M = M[isFreeDof, :][:, isFreeDof].tocsr()
uh = space.function()
if self.matlab is False:
uh[isFreeDof] = self.psolve(A, b[isFreeDof], M)
else:
uh[isFreeDof] = self.msolve(A, b[isFreeDof])
eta = self.residual_estimate(uh)
markedCell = mark(eta, self.theta)
IM = mesh.bisect(markedCell, returnim=True)
NN = mesh.number_of_nodes()
print(i + 1, "refine :", NN)
if (self.step > 0) and (i in idx):
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_2_' + str(i + 1) + '_' +
str(NN) + '.pdf')
plt.close()
self.savemesh(
mesh, self.resultdir + 'mesh_3_2_' + str(i + 1) + '_' +
str(NN) + '.mat')
final = i + 1
if NN > self.maxdof:
break
if self.step > 0:
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_2_' + str(final) + '_' +
str(NN) + '.pdf')
plt.close()
self.savemesh(
mesh, self.resultdir + 'mesh_3_2_' + str(final) + '_' +
str(NN) + '.mat')
space = LagrangeFiniteElementSpace(mesh, 1)
gdof = space.number_of_global_dofs()
A = self.get_stiff_matrix(space)
M = self.get_mass_matrix(space)
isFreeDof = ~(space.boundary_dof())
A = A[isFreeDof, :][:, isFreeDof].tocsr()
M = M[isFreeDof, :][:, isFreeDof].tocsr()
if self.multieigs is True:
self.A = A
self.M = M
self.ml = pyamg.ruge_stuben_solver(self.A)
self.eigs()
else:
uh = IM @ uh
if self.matlab is False:
uh[isFreeDof], d = self.eig(A, M)
else:
uh[isFreeDof], d = self.meigs(A, M)
print("smallest eigns:", d)
return space.function(array=uh)
end = timer()
print("with time: ", end - start)
def alg_3_1(self, maxit=None):
"""
1. 自适应在每层网格上求解最小特征值问题
refine: maxit, picard： maxit + 1
"""
print("算法 3.1")
if maxit is None:
maxit = self.maxit
start = timer()
if self.step == 0:
idx = []
else:
idx = list(range(0, self.maxit, self.step)) + [self.maxit - 1]
mesh = self.pde.init_mesh(n=self.numrefine)
GD = mesh.geo_dimension()
if (self.step > 0) and (0 in idx):
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_1_0_' + str(NN) + '.pdf')
plt.close()
self.savemesh(mesh,
self.resultdir + 'mesh_3_1_0_' + str(NN) + '.mat')
space = LagrangeFiniteElementSpace(mesh, 1)
isFreeDof = ~(space.boundary_dof())
gdof = space.number_of_global_dofs()
uh = np.ones(gdof, dtype=mesh.ftype)
uh[~isFreeDof] = 0
IM = eye(gdof)
for i in range(maxit + 1):
area = mesh.entity_measure('cell')
A = self.get_stiff_matrix(space)
M = self.get_mass_matrix(space)
uh = IM @ uh
A = A[isFreeDof, :][:, isFreeDof].tocsr()
M = M[isFreeDof, :][:, isFreeDof].tocsr()
if self.matlab is False:
uh[isFreeDof], d = self.eig(A, M)
else:
uh[isFreeDof], d = self.meigs(A, M)
if i < maxit:
uh = space.function(array=uh)
eta = self.residual_estimate(uh)
markedCell = mark(eta, self.theta)
IM = mesh.bisect(markedCell, returnim=True)
print(i + 1, "refine: ", mesh.number_of_nodes())
if (self.step > 0) and (i in idx):
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_1_' + str(i + 1) +
'_' + str(NN) + '.pdf')
plt.close()
self.savemesh(
mesh, self.resultdir + 'mesh_3_1_' + str(i + 1) + '_' +
str(NN) + '.mat')
space = LagrangeFiniteElementSpace(mesh, 1)
isFreeDof = ~(space.boundary_dof())
gdof = space.number_of_global_dofs()
if gdof > self.maxdof:
break
if self.step > 0:
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_3_1_' + str(i + 1) + '_' +
str(NN) + '.pdf')
plt.close()
self.savemesh(
mesh, self.resultdir + 'mesh_3_1_' + str(i + 1) + '_' +
str(NN) + '.mat')
if self.multieigs is True:
self.A = A
self.M = M
self.ml = pyamg.ruge_stuben_solver(self.A)
self.eigs()
end = timer()
print("smallest eigns:", d, "with time: ", end - start)
uh = space.function(array=uh)
return uh
def alg_0(self, maxit=None):
"""
1. 最粗网格上求解最小特征特征值问题，得到最小特征值 d_H 和特征向量 u_H
2. 自适应求解 - \Delta u_h = d_H*u_H
* 每层网格上求出的 u_h，插值到下一层网格上做为 u_H
* 并更新 d_H = u_h@A@u_h/u_h@M@u_h， 其中 A 是当前网格层上的刚度矩
阵，M 为当前网格层的质量矩阵。
自适应 maxit， picard： 1
"""
print("算法 0")
if maxit is None:
maxit = self.maxit
start = timer()
if self.step == 0:
idx = []
else:
idx = list(range(0, self.maxit, self.step)) + [self.maxit - 1]
mesh = self.pde.init_mesh(n=self.numrefine)
# 1. 粗网格上求解最小特征值问题
space = LagrangeFiniteElementSpace(mesh, 1)
gdof = space.number_of_global_dofs()
print("initial mesh:", gdof)
uh = np.zeros(gdof, dtype=np.float)
AH = self.get_stiff_matrix(space)
MH = self.get_mass_matrix(space)
isFreeHDof = ~(space.boundary_dof())
A = AH[isFreeHDof, :][:, isFreeHDof].tocsr()
M = MH[isFreeHDof, :][:, isFreeHDof].tocsr()
if self.picard is True:
uh[isFreeHDof], d = picard(A,
M,
np.ones(sum(isFreeHDof)),
sigma=self.sigma)
else:
uh[isFreeHDof], d = self.eig(A, M)
GD = mesh.geo_dimension()
if (self.step > 0) and (0 in idx):
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_0_0_' + str(NN) + '.pdf')
plt.close()
self.savemesh(mesh,
self.resultdir + 'mesh_0_0_' + str(NN) + '.mat')
# 2. 以 u_h 为右端项自适应求解 -\Deta u = d*u_h
I = eye(gdof)
for i in range(maxit):
uh = space.function(array=uh)
eta = self.residual_estimate(uh)
markedCell = mark(eta, self.theta)
IM = mesh.bisect(markedCell, returnim=True)
print(i + 1, "refine: ", mesh.number_of_nodes())
if (self.step > 0) and (i in idx):
NN = mesh.number_of_nodes()
fig = plt.figure()
fig.set_facecolor('white')
if GD == 2:
axes = fig.gca()
else:
axes = Axes3D(fig)
mesh.add_plot(axes, cellcolor='w')
fig.savefig(self.resultdir + 'mesh_0_' + str(i + 1) + '_' +
str(NN) + '.pdf')
plt.close()
self.savemesh(
mesh, self.resultdir + 'mesh_0_' + str(i + 1) + '_' +
str(NN) + '.mat')
I = IM @ I
uh = IM @ uh
space = LagrangeFiniteElementSpace(mesh, 1)
gdof = space.number_of_global_dofs()
A = self.get_stiff_matrix(space)
M = self.get_mass_matrix(space)
isFreeDof = ~(space.boundary_dof())
b = d * M @ uh
if self.sigma is None:
ml = pyamg.ruge_stuben_solver(
A[isFreeDof, :][:, isFreeDof].tocsr())
uh[isFreeDof] = ml.solve(b[isFreeDof],
x0=uh[isFreeDof],
tol=1e-12,
accel='cg').reshape((-1, ))
else:
K = A[isFreeDof, :][:, isFreeDof].tocsr(
) + self.sigma * M[isFreeDof, :][:, isFreeDof].tocsr()
b += self.sigma * M @ uh
ml = pyamg.ruge_stuben_solver(K)
uh[isFreeDof] = ml.solve(b[isFreeDof],
x0=uh[isFreeDof],
tol=1e-12,
accel='cg').reshape(-1)
# uh[isFreeDof] = spsolve(A[isFreeDof, :][:, isFreeDof].tocsr(), b[isFreeDof])
d = uh @ A @ uh / (uh @ M @ uh)
if gdof > self.maxdof:
break
if self.multieigs is True:
self.A = A[isFreeDof, :][:, isFreeDof].tocsr()
self.M = M[isFreeDof, :][:, isFreeDof].tocsr()
self.ml = pyamg.ruge_stuben_solver(self.A)
self.eigs()
end = timer()
print("smallest eigns:", d, "with time: ", end - start)
uh = space.function(array=uh)
return uh
err = np.sqrt(np.sum(eta**2))
print('errrefine', err)
if err < tol:
break
else:
# 加密并插值
NN0 = smesh.number_of_nodes()
edge = smesh.entity('edge')
isMarkedCell = smesh.refine_marker(eta, rtheta, method='L2')
smesh.refine_triangle_rg(isMarkedCell)
i += 1
smesh.add_plot(plt)
plt.savefig('./test-' + str(i + 1) + '.png')
plt.close()
space = LagrangeFiniteElementSpace(smesh, p=1)
print('refinedof', space.number_of_global_dofs())
uh00 = space.function()
nn2e = smesh.newnode2edge
uh00[:NN0] = uh0
uh00[NN0:] = np.average(uh0[edge[nn2e]], axis=-1)
uh0 = space.function()
uh0[:] = uh00
# 粗化网格并插值
isMarkedCell = smesh.refine_marker(eta, ctheta, 'COARSEN')
smesh.coarsen_triangle_rg(isMarkedCell)
i += 1
smesh.add_plot(plt)
plt.savefig('./test-' + str(i + 1) + '.png')
plt.close()
space = LagrangeFiniteElementSpace(smesh, p=1)
print('coarsendof', space.number_of_global_dofs())
