tFix array copying during jacobi iterations - cngf-pf - continuum model for granular flows with pore-pressure dynamics (renamed from 1d_fd_simple_shear)
(HTM) git clone git://src.adamsgaard.dk/cngf-pf
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---
(DIR) commit 5e5b568af441c1457133afd2deb7e09867fbb31e
(DIR) parent 3bd8e7c3f474481aa9a925151da29945bac8e8e5
(HTM) Author: Anders Damsgaard <anders@adamsgaard.dk>
Date: Thu, 4 Apr 2019 21:53:21 +0200
Fix array copying during jacobi iterations
Diffstat:
M 1d_fd_simple_shear.jl | 62 ++++++++++++++++++-------------
1 file changed, 36 insertions(+), 26 deletions(-)
---
(DIR) diff --git a/1d_fd_simple_shear.jl b/1d_fd_simple_shear.jl
t@@ -9,26 +9,41 @@ import PyPlot
const G = 9.81
## Wall parameters
-const P_wall = 1.0e3 # normal stress [Pa]
-# const v_x_wall = 1e-0 # wall velocity along x [m/s]
+
+# A larger normal stress deepens the deformational depth
+const P_wall = 100.0e3 # normal stress [Pa]
+
const v_x_bot = 0.0 # bottom velocity along x [m/s]
-#const μ_wall = 0.392 # stress ratio at top wall
-const μ_wall = 0.52 # stress ratio at top wall
+const μ_wall = 0.40 # stress ratio at top wall
## Nodes along z
const nz = 100
## Material properties
+
+# lower values of A mean that the velocity curve can have sharper curves, e.g.
+# at the transition from μ ≈ μ_s
const A = 0.48 # nonlocal amplitude [-]
+
+
+# lower values of b mean larger shear velocity for a given stress ratio above
+# yield
const b = 0.9377 # rate dependence beyond yield [-]
+
const μ_s = 0.3819 # static yield friction coefficient [-]
const ϕ = 0.38 # porosity [-]
+
+# lower values of d mean that the shear velocity curve can have sharper curves,
+# e.g. at the transition from μ ≈ μ_s
const d = 1e-3 # representative grain size [m]
+
const ρ_s = 2.485e3 # grain material density [kg/m^3]
## Domain size
const origo_z = 0.0
-const L_z = 20.0*d
+#const L_z = 20.0*d
+#const L_z = 200.0*d
+const L_z = 0.5
## Spatial coordiantes
const z = collect(range(origo_z, L_z, length=nz))
t@@ -103,13 +118,15 @@ end
## A single iteration for solving a Poisson equation Laplace(phi) = f on a
## Cartesian grid. The function returns the normalized residual value
-function poisson_solver_1d_iteration(g_in, g_out, μ, p, i, Δz, verbose=false)
+function poisson_solver_1d_iteration(g_in, g_out, r_norm,
+ μ, p, i, Δz,
+ verbose=false)
coorp_term = Δz^2.0/(2.0*ξ(μ[i])^2.0)
g_local_ = g_local(p[i], μ[i])
g_out[i+1] = 1.0/(1.0 + coorp_term) * (coorp_term*g_local(p[i], μ[i])
+ g_in[i+1+1]/2.0 + g_in[i-1+1]/2.0)
- res_norm = (g_out[i+1] - g_in[i+1])^2.0 / (g_out[i+1]^2.0 + 1e-16)
+ r_norm[i] = (g_out[i+1] - g_in[i+1])^2.0 / (g_out[i+1]^2.0 + 1e-16)
if verbose
println("-- $i -----------------")
t@@ -117,20 +134,18 @@ function poisson_solver_1d_iteration(g_in, g_out, μ, p, i, Δz, verbose=false)
println(" g_local: $g_local_")
println(" g_in: $(g_in[i+1])")
println(" g_out: $(g_out[i+1])")
- println(" res_norm: $res_norm")
+ println(" r_norm: $(r_norm[i])")
end
- return res_norm
end
## Iteratively solve the system laplace(phi) = f
function implicit_1d_jacobian_poisson_solver(g, p, μ, Δz,
- rel_tol=1e-5,
- #max_iter=10_000,
- max_iter=10,
+ rel_tol=1e-6,
+ max_iter=10_000,
verbose=false)
# allocate second array of g for Jacobian solution procedure
- g_out = g
+ g_out = zero(g)
if verbose
println("g: ")
t@@ -141,7 +156,7 @@ function implicit_1d_jacobian_poisson_solver(g, p, μ, Δz,
end
# array of normalized residuals
- local r_norm = zero(g)
+ r_norm = zero(p)
for iter=1:max_iter
t@@ -152,16 +167,10 @@ function implicit_1d_jacobian_poisson_solver(g, p, μ, Δz,
end
# perform a single jacobi iteration in each cell
- # if iter%2 == 0
- for iz=1:length(p)
- r_norm[iz] = poisson_solver_1d_iteration(g, g_out, μ, p, iz, Δz)
- end
- # else # reverse direction every second time
- # for iz=length(g)-1:2
- # r_norm[iz] = poisson_solver_1d_iteration(g, g_out,
- # f, iz, Δz)
- # end
- # end
+ for iz=1:length(p)
+ poisson_solver_1d_iteration(g, g_out, r_norm,
+ μ, p, iz, Δz)
+ end
r_norm_max = maximum(r_norm)
if verbose
t@@ -184,12 +193,13 @@ function implicit_1d_jacobian_poisson_solver(g, p, μ, Δz,
# stop iterating if the relative tolerance is satisfied
if r_norm_max <= rel_tol
- if verbose
+ #if verbose
@info ".. Solution converged after $iter iterations"
- end
+ #end
return
end
end
+ @error "Solution did not converge after $iter (r_norm_max = $r_norm_max)"
end
function plot_profile(z, v, label, filename)