Jump terms in elasticity #760

bhaveshshrimali · 2021-10-15T16:19:17Z

bhaveshshrimali
Oct 15, 2021

Are there any examples that demonstrate how to ssemble bilinear forms with facet jumps? For e.g., in incompressible elasticity with CR elements (ElementTetCR) I want to assemble a stabilization term: something like ∫1/h*[[u]]⋅[[v]]. Here I am penalizing the jump in displacement (and not just the normal component of it). I am seeing ex04 (Mortar) but that is something different I guess, (or I'm not understanding things correctly).

Reference:

Eqn (3.6) in http://matematicas.uam.es/~carlos.mora/carlos.mora/About_Me_files/numerExpNoRed.pdf

For a template of what I am trying to do: (basically ex11 when Lambda >> Mu and using CR)

import numpy as np
from skfem.models.elasticity import linear_elasticity
from skfem import (
    MeshTet, ElementTetCR, ElementVectorH1, Basis, solve, condense, InteriorFacetBasis,
    BilinearForm
)

@BilinearForm
def jumterm(u, v, w): # how to basically this?
    h = w.h
    n = w.n
    ju = dot(u, n)
    jv = dot(v, n)
    return 1/h * dot(ju, jv)


mesh = MeshTet().refined(2)
element = ElementVectorH1(ElementTetCR()) # dofs at facet midpoints
basis = Basis(mesh, element)
u = basis.zeros()
fbasis = InteriorFacetBasis(mesh, element)
bilinf = linear_elasticity(Lambda=1e3, Mu=1.)
K = bilinf.assemble(basis) + jumterm.assemble(fbasis, w=fbasis.interpolate(u))

dofs = {
    'left' : basis.get_dofs(lambda x: x[0] == 0.0),
    'right': basis.get_dofs(lambda x: x[0] == 1.0),
}

u = basis.zeros()
u[dofs['right'].facet['u^1']] = 0.1
I = basis.complement_dofs(dofs)
u = solve(*condense(K, x=u, I=I))

kinnala · 2021-10-15T17:40:53Z

kinnala
Oct 15, 2021
Maintainer

Like this?

https://github.com/kinnala/scikit-fem/blob/master/docs/examples/ex07.py

After 4.0.0 if multiple bases are given to asm() then it will assemble the same form using all bases and sum the result.

25 replies

kinnala Oct 21, 2021
Maintainer

What if you assemble the matrix with two jumps and then take matrix product with the previous solution? Could it work?

bhaveshshrimali Oct 22, 2021
Author

What if you assemble the matrix with two jumps and then take matrix product with the previous solution? Could it work?

You mean like a "linearized" representation, as in assembling the bilinear form (same as linear elasticity) and then taking dot product with the previous solution vector (of dofs)? I could try that, but I have a hunch that it may not work. The nonlinear residual should be assemble-able as-is too, right? The form is [[u]] . [[v]] and in this case we know [[u]] (current iterate).

My original (naive) guess was to go brute force and write everything down explicitly (in a way similar to linear elasticity), but this doesn't work.

@LinearForm
def jumps_res(v, w):
    u = w["disp"]
    h = w.h
    ju = w.sign1 * u # fix: change to w.idx[0] and so on!
    jv = w.sign2 * v
    return 0.1/h * dot(ju, jv)
...
V_facet = [InteriorFacetBasis(mesh, elem, side=i) for i in range(2)]
disp_facet = [V_facet[i].interpolate(u) for i in range(2)]
...
asm(jumps_res, V_facet[0], disp=disp_facet[0]) + asm(jumps_res, V_facet[1], disp=disp_facet[1]) -\
        (asm(jumps_res, V_facet[0], disp=disp_facet[1]) + asm(jumps_res, V_facet[1], disp=disp_facet[0]))

I just upgraded to the master branch. Will try out some ideas tomorrow

bhaveshshrimali Oct 22, 2021
Author

The way I did something similar in dolfin (until now) is

F = Identity(len(u)) + grad(u)
J = det(F)
S = mu * F - mu * inv(F).T + lmbda * (J - 1.) * J * inv(F).T
q = Constant(mu)
h = FacetArea(mesh)
res = inner(S, grad(v)) * dx + q/avg(h) * dot(jump(u), jump(v)) * dS
Jac = derivative(res, u, du) # replace this with the derivative calculated by hand
problem = NonlinearVariationalProblem(res, u, bcs, J=Jac)
solver = NonlinearVariationalSolver(problem)
solver.solve()

kinnala Oct 22, 2021
Maintainer

Well if you know u = w in ([[u]], [[v]]) and w is in the finite element subspace then if B is the matrix corresponding to the above bilinear form then w.T @ B should be equivalent to the vector you get from asssembling ([[w]], [[v]]). But that's only because the form is linear, doesn't work for other nonlinear forms.

However, it's possible to assemble this linear form directly. It just might not be obvious how to do it since again you need the definition [[w]] = w1 - w2 and then pass both w1 and w2 separately to the forms.

bhaveshshrimali Oct 22, 2021
Author

Ah yes, indeed it is :) I was overlooking this bit and just considering the overall nonlinear form without properly looking at the jump term. A quick (albeit dirty) reimplementation of ex36 seems to work. (For more non-trivial meshes one might need to tweak the solver a bit). Here is the minimal code, final result and residual-norms at each time-step.

On a side note I am very happy that pypardiso worked well for this problem (although it didn't on the backward facing step). Even without numba/multithreading things are quite good.

Code

import numpy as np
from os.path import join
from os import getcwd, makedirs
from skfem import (
    MeshTet, solve, condense, ElementVectorH1, ElementTetCR, BilinearForm, LinearForm,
    Basis, InteriorFacetBasis, Functional, asm, DiscreteField, projection, ElementTetP1
)
from skfem.helpers import dot, grad, det, inv, identity, transpose

@LinearForm
def res(v, w):
    dw = grad(w["disp"])
    mu, lmbda = 1., 1.e3
    I = identity(dw)
    F = I + dw
    FinvT = transpose(inv(F))
    J = det(F)
    S = mu * F - mu * FinvT + lmbda * J * (J - 1.) * FinvT
    return np.einsum("ij...,ij...", S, grad(v)) 

@BilinearForm
def jac(u, v, w):
    dw = grad(w["disp"])
    du, dv =  grad(u), grad(v)
    mu, lmbda = 1., 1.e3
    I = identity(dw)
    F = I + dw
    Finv = inv(F)
    J = det(F)
    dSdF = mu * (np.einsum("ik...,jl...->ijkl...", I, I) + np.einsum("jk...,li...->ijkl...", Finv, Finv)) +\
        lmbda * (2. * J - 1.) * J * np.einsum("ji...,lk...->ijkl...", Finv, Finv) - lmbda * (J - 1.) * J * np.einsum("jk...,li...->ijkl...", Finv, Finv)
    return np.einsum("ij...,kl...,ijkl...", du, dv, dSdF)

@BilinearForm
def jumps_jac(u, v, w):
    h = w.h
    ju = w.sign1 * u
    jv = w.sign2 * v
    return 1./h * dot(ju, jv)

@Functional
def volume(w):
    dw = grad(w["disp"])
    I = identity(dw)
    F = I + dw
    J = det(F)
    return J


mesh = MeshTet()
elem = ElementVectorH1(ElementTetCR())
V = Basis(mesh, elem)
V_facet = [InteriorFacetBasis(mesh, elem, side=i) for i in range(2)]
u = V.zeros()
dofsu = [
    V.find_dofs(
        {"left": mesh.facets_satisfying(lambda x: x[0] < 1.e-6)},
        skip=["u^2", "u^3"]
    ),
    V.find_dofs(
        {"front": mesh.facets_satisfying(lambda x: abs(x[2] - 1.) < 1.e-6)},
        skip=["u^1", "u^2"]
    ),
    V.find_dofs(
        {"back": mesh.facets_satisfying(lambda x: x[2] < 1.e-6)},
        skip=["u^1", "u^2"]
    ),
    V.find_dofs(
        {"bottom": mesh.facets_satisfying(lambda x: x[1] < 1.e-6)},
        skip=["u^1", "u^3"]
    )
]
dofs = {}
for dof in dofsu:
    dofs.update(dof)
stretch_ = 0.05
u[dofs["left"].all()] = 0.
u[dofs["bottom"].all()] = 0.
u[dofs["back"].all()] = 0.

I = V.complement_dofs(dofs)
for t in np.linspace(0., 1., 101):
    u[dofs["front"].all()] = t
    print(f"Time step: {t:0.3f} of 1.0")
    for itr in range(100):
        disp = V.interpolate(u)
        facet_assembly = asm(jumps_jac, V_facet, V_facet)
        Fres = asm(res, V, disp=disp) + facet_assembly.dot(u)
        Kres = asm(jac, V, disp=disp)
        Kres += facet_assembly
        delu = solve(*condense(Kres, -Fres, I=I))
        u += delu 
        normu = np.linalg.norm(delu)
        normres = np.linalg.norm(Fres[I])
        print(f"Newton iter: {itr+1}, norm_du: {normu}, norm_res: {normres}")
        if  normres < 1e-8:
            break

vol = volume.assemble(V, disp=V.interpolate(u))
print(f"Deformed Volume: {vol}")
V_proj = Basis(mesh, ElementVectorH1(ElementTetP1()))
u_proj = projection(u, basis_from=V, basis_to=V_proj)
resultsDir = join(getcwd(), "FiniteElasticityCR")
makedirs(resultsDir, exist_ok=True)
mesh.save(join(resultsDir, "uCR.xdmf"), point_data={
    "u": u_proj[V_proj.nodal_dofs].T
})

Convergence

Time step: 0.000 of 1.0
Newton iter: 1, norm_du: 0.0, norm_res: 0.0
Time step: 0.010 of 1.0
Newton iter: 1, norm_du: 0.01814967159534676, norm_res: 43.40723889702308
Newton iter: 2, norm_du: 0.016929548485981355, norm_res: 1.2103443757618495
Newton iter: 3, norm_du: 0.03651958350201343, norm_res: 0.1852027096910379
Newton iter: 4, norm_du: 0.004761357680180346, norm_res: 4.582256692556642
Newton iter: 5, norm_du: 0.029640130373315213, norm_res: 0.0269537002734853
Newton iter: 6, norm_du: 0.0052081517331174455, norm_res: 3.4150671149056495
Newton iter: 7, norm_du: 0.008203243553594815, norm_res: 0.07387211195564916
Newton iter: 8, norm_du: 0.0014818881362587202, norm_res: 0.023221961793671468
Newton iter: 9, norm_du: 0.0001551201002280597, norm_res: 0.007543782563877529
Newton iter: 10, norm_du: 5.402133019676045e-06, norm_res: 7.83682076998291e-05
Newton iter: 11, norm_du: 1.995997747314188e-09, norm_res: 9.653333418328554e-08
Newton iter: 12, norm_du: 8.369094263357975e-16, norm_res: 2.6099182592938823e-13
Time step: 0.020 of 1.0
Newton iter: 1, norm_du: 0.017896550021647195, norm_res: 43.030946655445234
Newton iter: 2, norm_du: 0.016664962813230606, norm_res: 1.1885043456388324
Newton iter: 3, norm_du: 0.003335553737110235, norm_res: 0.17850611376575304
Newton iter: 4, norm_du: 0.0016903164138208006, norm_res: 0.004688790221354253
Newton iter: 5, norm_du: 1.7527587397412066e-05, norm_res: 0.00905846865962544
Newton iter: 6, norm_du: 7.067215299618001e-07, norm_res: 1.2990074034334943e-06
Newton iter: 7, norm_du: 3.4984624743050405e-12, norm_res: 1.760791520562011e-09
Time step: 0.030 of 1.0
Newton iter: 1, norm_du: 0.01766020788692112, norm_res: 42.66488620069044
Newton iter: 2, norm_du: 0.016423662799542618, norm_res: 1.1673774155641383
Newton iter: 3, norm_du: 0.0031010473034646666, norm_res: 0.17234492548670202
Newton iter: 4, norm_du: 0.0009186213758249705, norm_res: 0.003443826728598297
Newton iter: 5, norm_du: 1.540257194165374e-05, norm_res: 0.002456040478671517
Newton iter: 6, norm_du: 1.433897664664291e-07, norm_res: 7.978488735067248e-07
Newton iter: 7, norm_du: 4.617475602055136e-13, norm_res: 7.252503838925949e-11
Time step: 0.040 of 1.0
Newton iter: 1, norm_du: 0.01743644513248624, norm_res: 42.30863711787643
Newton iter: 2, norm_du: 0.01620598816670475, norm_res: 1.146905175949791
Newton iter: 3, norm_du: 0.0030007527675256604, norm_res: 0.1667536519830937
Newton iter: 4, norm_du: 0.0006225956088518926, norm_res: 0.0048000744961723685
Newton iter: 5, norm_du: 1.1770196948954306e-05, norm_res: 0.0010279342327174639
Newton iter: 6, norm_du: 4.341356233325588e-08, norm_res: 4.7558572479215256e-07
Newton iter: 7, norm_du: 7.759598684766002e-14, norm_res: 6.613776742148301e-12
Time step: 0.050 of 1.0
Newton iter: 1, norm_du: 0.017222604813470415, norm_res: 41.9618005539973
Newton iter: 2, norm_du: 0.016010078535288776, norm_res: 1.127047343371612
Newton iter: 3, norm_du: 0.0029118566193143897, norm_res: 0.16168081083967226
Newton iter: 4, norm_du: 0.0004681015416356204, norm_res: 0.005092870504879556
Newton iter: 5, norm_du: 8.154123797328224e-06, norm_res: 0.0005281844352184907
Newton iter: 6, norm_du: 1.4537844063555083e-08, norm_res: 2.297321316660482e-07
Newton iter: 7, norm_du: 1.1864429875787324e-14, norm_res: 7.484523464831889e-13
Time step: 0.060 of 1.0
Newton iter: 1, norm_du: 0.017016927667447803, norm_res: 41.623997931192164
Newton iter: 2, norm_du: 0.015833427288532112, norm_res: 1.1077724260093336
Newton iter: 3, norm_du: 0.0028259260116720564, norm_res: 0.15705339512710625
Newton iter: 4, norm_du: 0.0003737507511614464, norm_res: 0.0050559285727244975
Newton iter: 5, norm_du: 5.7168596796883085e-06, norm_res: 0.00030579617166614007
Newton iter: 6, norm_du: 5.547794599252386e-09, norm_res: 1.1329753365180648e-07
Newton iter: 7, norm_du: 2.05802930919566e-15, norm_res: 3.4358126015757557e-13
Time step: 0.070 of 1.0
Newton iter: 1, norm_du: 0.01681819974628657, norm_res: 41.29486974655022
Newton iter: 2, norm_du: 0.015673548473471265, norm_res: 1.0890536444648005
Newton iter: 3, norm_du: 0.0027428513867430253, norm_res: 0.15280236878905018
Newton iter: 4, norm_du: 0.00031031969213517056, norm_res: 0.0048943180009261246
Newton iter: 5, norm_du: 4.1028353826948895e-06, norm_res: 0.00019138996433141746
Newton iter: 6, norm_du: 2.3422918963626887e-09, norm_res: 5.849135068897909e-08
Newton iter: 7, norm_du: 6.208491139389873e-16, norm_res: 4.04806782439534e-13
Time step: 0.080 of 1.0
Newton iter: 1, norm_du: 0.016625550964807428, norm_res: 40.974074452001226
Newton iter: 2, norm_du: 0.015528222582570481, norm_res: 1.070867042011337
Newton iter: 3, norm_du: 0.0026630660386519254, norm_res: 0.14887002406669872
Newton iter: 4, norm_du: 0.0002647913766937687, norm_res: 0.00468279840734551
Newton iter: 5, norm_du: 3.0112463769504347e-06, norm_res: 0.0001265199051715114
Newton iter: 6, norm_du: 1.0678920258153248e-09, norm_res: 3.1574420751546166e-08
Newton iter: 7, norm_du: 3.3352512932205013e-16, norm_res: 2.9355382976639017e-13
Time step: 0.090 of 1.0
Newton iter: 1, norm_du: 0.016438335298451366, norm_res: 40.66128740836413
Newton iter: 2, norm_du: 0.015395556160423544, norm_res: 1.0531905610923347
Newton iter: 3, norm_du: 0.0025867718155538374, norm_res: 0.1452102964174989
Newton iter: 4, norm_du: 0.00023051406537726157, norm_res: 0.004453334453668632
Newton iter: 5, norm_du: 2.254139167003948e-06, norm_res: 8.707984663398325e-05
Newton iter: 6, norm_du: 5.168763267661351e-10, norm_res: 1.773180718806105e-08
Newton iter: 7, norm_du: 4.607620204713414e-16, norm_res: 4.391784677327772e-13
Time step: 0.100 of 1.0
Newton iter: 1, norm_du: 0.016256057062036276, norm_res: 40.35619990811593
Newton iter: 2, norm_du: 0.015273966219415788, norm_res: 1.036003566868564
Newton iter: 3, norm_du: 0.0025139602725685163, norm_res: 0.14178691315719671
Newton iter: 4, norm_du: 0.00020374885915081518, norm_res: 0.00422093214636747
Newton iter: 5, norm_du: 1.7167007150872746e-06, norm_res: 6.18124786811971e-05
Newton iter: 6, norm_du: 2.6251061491955287e-10, norm_res: 1.0309323676078703e-08
Newton iter: 7, norm_du: 2.226326127496791e-16, norm_res: 8.365335531578465e-13
Time step: 0.110 of 1.0
Newton iter: 1, norm_du: 0.016078324166074714, norm_res: 40.058518261874326
Newton iter: 2, norm_du: 0.015162139333845158, norm_res: 1.01928658587073
Newton iter: 3, norm_du: 0.002444501690073982, norm_res: 0.13857124320022987
Newton iter: 4, norm_du: 0.00018224077664282144, norm_res: 0.003992987613983686
Newton iter: 5, norm_du: 1.32739963706839e-06, norm_res: 4.4954544686135907e-05
Newton iter: 6, norm_du: 1.3880701270541525e-10, norm_res: 6.1800515918220545e-09
Time step: 0.120 of 1.0
Newton iter: 1, norm_du: 0.015904817681366867, norm_res: 39.767962943993396
Newton iter: 2, norm_du: 0.015058986554492046, norm_res: 1.0030211516640686
Newton iter: 3, norm_du: 0.0023782093254459805, norm_res: 0.13554044448460612
Newton iter: 4, norm_du: 0.00016455334810600293, norm_res: 0.003773181329550428
Newton iter: 5, norm_du: 1.0404310689586595e-06, norm_res: 3.333846538813876e-05
Newton iter: 6, norm_du: 7.604254044945799e-11, norm_res: 3.8078465433387145e-09
Time step: 0.130 of 1.0
Newton iter: 1, norm_du: 0.015735271545374616, norm_res: 39.48426779302829
Newton iter: 2, norm_du: 0.014963602245889504, norm_res: 0.9871897053576398
Newton iter: 3, norm_du: 0.002314877212196816, norm_res: 0.1326760232084504
Newton iter: 4, norm_du: 0.00014973028872847734, norm_res: 0.0035632703196780383
Newton iter: 5, norm_du: 8.256599608858396e-07, norm_res: 2.512210866346948e-05
Newton iter: 6, norm_du: 4.3049756584687696e-11, norm_res: 2.407409478886754e-09
Time step: 0.140 of 1.0
Newton iter: 1, norm_du: 0.015569458736640212, norm_res: 39.20717926317192
Newton iter: 2, norm_du: 0.014875229254541898, norm_res: 0.9717755250930196
Newton iter: 3, norm_du: 0.0022543004156262278, norm_res: 0.12996276073628066
Newton iter: 4, norm_du: 0.00013711134491964602, norm_res: 0.0033639751601035163
Newton iter: 5, norm_du: 6.627660479327494e-07, norm_res: 1.9183740205384405e-05
Newton iter: 6, norm_du: 2.516706543647867e-11, norm_res: 1.5568263031341788e-09
Time step: 0.150 of 1.0
Newton iter: 1, norm_du: 0.015407181668688097, norm_res: 38.93645572306021
Newton iter: 2, norm_du: 0.014793230479335529, norm_res: 0.9567626713401377
Newton iter: 3, norm_du: 0.002196284950971912, norm_res: 0.12738792820918834
Newton iter: 4, norm_du: 0.00012622641859665927, norm_res: 0.0031754436964748604
Newton iter: 5, norm_du: 5.377442609512852e-07, norm_res: 1.4813736867734767e-05
Newton iter: 6, norm_du: 1.5199767986424145e-11, norm_res: 1.0294745411533565e-09
Time step: 0.160 of 1.0
Newton iter: 1, norm_du: 0.015248265393396961, norm_res: 38.67186679861732
Newton iter: 2, norm_du: 0.014717066074446228, norm_res: 0.9421359412303397
Newton iter: 3, norm_du: 0.0021406519371328188, norm_res: 0.12494071415680796
Newton iter: 4, norm_du: 0.00011673187542721166, norm_res: 0.0029975040632914287
Newton iter: 5, norm_du: 4.4075438352932226e-07, norm_res: 1.1548549112546518e-05
Newton iter: 6, norm_du: 9.49040613363429e-12, norm_res: 6.958179787744826e-10
Time step: 0.170 of 1.0
Newton iter: 1, norm_du: 0.015092552710100684, norm_res: 38.41319275688653
Newton iter: 2, norm_du: 0.014646275313682667, norm_res: 0.9278808283590618
Newton iter: 3, norm_du: 0.002087238673300561, norm_res: 0.12261180597275716
Newton iter: 4, norm_du: 0.00010837073182075213, norm_res: 0.0028298074888254495
Newton iter: 5, norm_du: 3.647645793184404e-07, norm_res: 9.076982558470883e-06
Newton iter: 6, norm_du: 6.12562227037629e-12, norm_res: 4.799830049921884e-10
Time step: 0.180 of 1.0
Newton iter: 1, norm_du: 0.014939900589590493, norm_res: 38.160223928013416
Newton iter: 2, norm_du: 0.014580462197257334, norm_res: 0.9139834861668911
Newton iter: 3, norm_du: 0.002035898161592462, norm_res: 0.12039308148203184
Newton iter: 4, norm_du: 0.00010094693292404008, norm_res: 0.0026719110657927635
Newton iter: 5, norm_du: 3.046747896156585e-07, norm_res: 7.1851600636356134e-06
Newton iter: 6, norm_du: 4.0804625319677405e-12, norm_res: 3.36534059417795e-10
Time step: 0.190 of 1.0
Newton iter: 1, norm_du: 0.01479017751968985, norm_res: 37.91276016274869
Newton iter: 2, norm_du: 0.014519284017504944, norm_res: 0.9004306939080694
Newton iter: 3, norm_du: 0.00198649791742847, norm_res: 0.1182773792056465
Newton iter: 4, norm_du: 9.430824944725329e-05, norm_res: 0.0025233267022206443
Newton iter: 5, norm_du: 2.567376630930238e-07, norm_res: 5.723060102139513e-06
Newton iter: 6, norm_du: 2.795529269658516e-12, norm_res: 2.411743687418357e-10
Time step: 0.200 of 1.0
Newton iter: 1, norm_du: 0.014643261506196366, norm_res: 37.67061032307151
Newton iter: 2, norm_du: 0.014462442247277189, norm_res: 0.8872098246486837
Newton iter: 3, norm_du: 0.0019389185203239046, norm_res: 0.11625832511709888
Newton iter: 4, norm_du: 8.833461411529666e-05, norm_res: 0.0023835504268225156
Newton iter: 5, norm_du: 2.181683225586067e-07, norm_res: 4.583564401704378e-06
Newton iter: 6, norm_du: 1.9605413279401174e-12, norm_res: 1.7598116973842343e-10
Time step: 0.210 of 1.0
Newton iter: 1, norm_du: 0.014499038546247052, norm_res: 37.43359180367731
Newton iter: 2, norm_du: 0.014409675247456087, norm_res: 0.874308815006091
Newton iter: 3, norm_du: 0.0018930521393094623, norm_res: 0.1143302002345985
Newton iter: 4, norm_du: 8.292998757812517e-05, norm_res: 0.0022520800227501673
Newton iter: 5, norm_du: 1.868765528249055e-07, norm_res: 3.6889843905437093e-06
Newton iter: 6, norm_du: 1.4002281261750388e-12, norm_res: 1.2972934109778498e-10
Time step: 0.220 of 1.0
Newton iter: 1, norm_du: 0.014357401446601211, norm_res: 37.20153008226268
Newton iter: 2, norm_du: 0.0143607524001473, norm_res: 0.8617161364375275
Newton iter: 3, norm_du: 0.001848801146253879, norm_res: 0.11248783799993751
Newton iter: 4, norm_du: 7.801657140103438e-05, norm_res: 0.0021284255427607406
Newton iter: 5, norm_du: 1.6127990535343024e-07, norm_res: 2.9821839567036356e-06
Newton iter: 6, norm_du: 1.0137734213398986e-12, norm_res: 9.776701520901864e-11
Time step: 0.230 of 1.0
Newton iter: 1, norm_du: 0.014218248896803282, norm_res: 36.974258296681796
Newton iter: 2, norm_du: 0.014315469362122657, norm_res: 0.8494207679933371
Newton iter: 3, norm_du: 0.0018060768646057965, norm_res: 0.11072654362674343
Newton iter: 4, norm_du: 7.353061658019189e-05, norm_res: 0.0020121154474737847
Newton iter: 5, norm_du: 1.4017124908259402e-07, norm_res: 2.4206294815444955e-06
Newton iter: 6, norm_du: 7.409270896830349e-13, norm_res: 7.49423147441715e-11
Time step: 0.240 of 1.0
Newton iter: 1, norm_du: 0.01408148473288698, norm_res: 36.75161684720419
Newton iter: 2, norm_du: 0.014273644201748353, norm_res: 0.837412170430576
Newton iter: 3, norm_du: 0.001764798466306475, norm_res: 0.10904202982401225
Newton iter: 4, norm_du: 6.941933830398721e-05, norm_res: 0.0019026999650208077
Newton iter: 5, norm_du: 1.2262356764606008e-07, norm_res: 1.9723027811004034e-06
Newton iter: 6, norm_du: 5.44812748287277e-13, norm_res: 5.788641117760179e-11
Time step: 0.250 of 1.0
Newton iter: 1, norm_du: 0.013947017345071543, norm_res: 36.53345302219848
Newton iter: 2, norm_du: 0.014235114236028572, norm_res: 0.8256802616575671
Newton iter: 3, norm_du: 0.0017248920124553486, norm_res: 0.10743036487986551
Newton iter: 4, norm_du: 6.56386115389235e-05, norm_res: 0.0017997527192314155
Newton iter: 5, norm_du: 1.0792064879544436e-07, norm_res: 1.6128635985959236e-06
Newton iter: 6, norm_du: 4.0201230475118507e-13, norm_res: 4.486779031301153e-11
Time step: 0.260 of 1.0
Newton iter: 1, norm_du: 0.013814759195482772, norm_res: 36.319620645742006
Newton iter: 2, norm_du: 0.014199733425115555, norm_res: 0.8142153934211288
Newton iter: 3, norm_du: 0.0016862896251350961, norm_res: 0.10588793017787461
Newton iter: 4, norm_du: 6.215122671391742e-05, norm_res: 0.001702871246281791
Newton iter: 5, norm_du: 9.550604477119926e-08, norm_res: 1.323641548223556e-06
Newton iter: 6, norm_du: 2.971634710475775e-13, norm_res: 3.588990627160794e-11
Time step: 0.270 of 1.0
Newton iter: 1, norm_du: 0.013684626420790573, norm_res: 36.109979745701
Newton iter: 2, norm_du: 0.014167370213210132, norm_res: 0.8030083292145871
Newton iter: 3, norm_du: 0.0016489287751819362, norm_res: 0.1044113850136301
Newton iter: 4, norm_du: 5.8925553135742846e-05, norm_res: 0.0016116768044528852
Newton iter: 5, norm_du: 8.494511462240154e-08, norm_res: 1.0902029501137833e-06
Newton iter: 6, norm_du: 2.1958059808483434e-13, norm_res: 2.822756364264215e-11
Time step: 0.280 of 1.0
Newton iter: 1, norm_du: 0.013556538501076394, norm_res: 35.90439624098081
Newton iter: 2, norm_du: 0.014137905728857088, norm_res: 0.7920502233317791
Newton iter: 3, norm_du: 0.0016127516704020706, norm_res: 0.1029976371167743
Newton iter: 4, norm_du: 5.593450324078788e-05, norm_res: 0.001525813753164624
Newton iter: 5, norm_du: 7.589656011801328e-08, norm_res: 9.013007420125991e-07
Newton iter: 6, norm_du: 1.6199029853816529e-13, norm_res: 2.3622005933908453e-11
Time step: 0.290 of 1.0
Newton iter: 1, norm_du: 0.01343041798087852, norm_res: 35.70274164671108
Newton iter: 2, norm_du: 0.014111232276205971, norm_res: 0.7813326010271896
Newton iter: 3, norm_du: 0.0015777047297725892, norm_res: 0.1016438176946265
Newton iter: 4, norm_du: 5.315472156814712e-05, norm_res: 0.0014449486608872227
Newton iter: 5, norm_du: 6.809093557075689e-08, norm_res: 7.48116180402846e-07
Newton iter: 6, norm_du: 1.1913687941026257e-13, norm_res: 1.895813740732203e-11
Time step: 0.300 of 1.0
Newton iter: 1, norm_du: 0.013306190231754993, norm_res: 35.504892796222265
Newton iter: 2, norm_du: 0.014087252063212376, norm_res: 0.7708473397341908
Newton iter: 3, norm_du: 0.0015437381307973573, norm_res: 0.10034726010379123
Newton iter: 4, norm_du: 5.056594353399657e-05, norm_res: 0.001368769262899472
Newton iter: 5, norm_du: 6.131433273536948e-08, norm_res: 6.236857843798715e-07
Newton iter: 6, norm_du: 8.720010659301766e-14, norm_res: 1.4951791088424312e-11
Time step: 0.310 of 1.0
Newton iter: 1, norm_du: 0.013183783248224685, norm_res: 35.31073157874879
Newton iter: 2, norm_du: 0.014065876123871402, norm_res: 0.7605866512733487
Newton iter: 3, norm_du: 0.001510805418846658, norm_res: 0.09910548144897458
Newton iter: 4, norm_du: 4.815048385976857e-05, norm_res: 0.0012969833465056768
Newton iter: 5, norm_du: 5.539594024509051e-08, norm_res: 5.224771354572764e-07
Newton iter: 6, norm_du: 6.346693292413709e-14, norm_res: 1.2872412476289546e-11
Time step: 0.320 of 1.0
Newton iter: 1, norm_du: 0.013063127470808981, norm_res: 35.12014469187654
Newton iter: 2, norm_du: 0.014047023400226812, norm_res: 0.7505430650222334
Newton iter: 3, norm_du: 0.001478863168962289, norm_res: 0.09791616659118818
Newton iter: 4, norm_du: 4.589282499123153e-05, norm_res: 0.0012293176105310389
Newton iter: 5, norm_du: 5.0198523985059635e-08, norm_res: 4.400738241245288e-07
Newton iter: 6, norm_du: 4.574880928287022e-14, norm_res: 1.1408860771680771e-11
Time step: 0.330 of 1.0
Newton iter: 1, norm_du: 0.012944155631315924, norm_res: 34.933023407814204
Newton iter: 2, norm_du: 0.014030619956653502, norm_res: 0.7407094119730455
Newton iter: 3, norm_du: 0.0014478706920274527, norm_res: 0.0967771541487359
Newton iter: 4, norm_du: 4.3779283327278985e-05, norm_res: 0.0011655165202476884
Newton iter: 5, norm_du: 4.56111179035838e-08, norm_res: 3.729239604689115e-07
Newton iter: 6, norm_du: 3.2626843644844814e-14, norm_res: 8.49083156288609e-12
Time step: 0.340 of 1.0
Newton iter: 1, norm_du: 0.012826802616555942, norm_res: 34.749263352614896
Newton iter: 2, norm_du: 0.014016598304330436, norm_res: 0.7310788096594001
Newton iter: 3, norm_du: 0.0014177897785969647, norm_res: 0.09568642416014365
Newton iter: 4, norm_du: 4.179773653491248e-05, norm_res: 0.0011053412069507275
Newton iter: 5, norm_du: 4.1543398072769995e-08, norm_res: 3.1815565954627e-07
Newton iter: 6, norm_du: 2.2895025918636462e-14, norm_res: 7.205964126276793e-12
Time step: 0.350 of 1.0
Newton iter: 1, norm_du: 0.012711005347516097, norm_res: 34.56876429757088
Newton iter: 2, norm_du: 0.014004896817858834, norm_res: 0.7216446478711556
Newton iter: 3, norm_du: 0.0013885844746064156, norm_res: 0.09464208714550337
Newton iter: 4, norm_du: 3.993739920017841e-05, norm_res: 0.0010485684006147685
Newton iter: 5, norm_du: 3.792133866107725e-08, norm_res: 2.734385765729032e-07
Newton iter: 6, norm_du: 1.5764776889256265e-14, norm_res: 5.98589340710464e-12
Time step: 0.360 of 1.0
Newton iter: 1, norm_du: 0.012596702671614174, norm_res: 34.39142996202112
Newton iter: 2, norm_du: 0.013995459229544227, norm_res: 0.7124005751444203
Newton iter: 3, norm_du: 0.0013602208843404384, norm_res: 0.09364237437850984
Newton iter: 4, norm_du: 3.818863702308761e-05, norm_res: 0.0009949894039604523
Newton iter: 5, norm_du: 3.468384832968238e-08, norm_res: 2.3686877435356617e-07
Newton iter: 6, norm_du: 1.0537622517297349e-14, norm_res: 5.531333789928946e-12
Time step: 0.370 of 1.0
Newton iter: 1, norm_du: 0.012483835266177922, norm_res: 34.21716782688082
Newton iter: 2, norm_du: 0.01398823418920508, norm_res: 0.7033404859601303
Newton iter: 3, norm_du: 0.0013326669965154094, norm_res: 0.09268562916747872
Newton iter: 4, norm_du: 3.654281196677677e-05, norm_res: 0.0009444091339798788
Newton iter: 5, norm_du: 3.178015731441575e-08, norm_res: 2.0688867841120484e-07
Newton iter: 6, norm_du: 6.727476745658918e-15, norm_res: 4.826218441812244e-12
Time step: 0.380 of 1.0
Newton iter: 1, norm_du: 0.012372345551634642, norm_res: 34.045888958243566
Newton iter: 2, norm_du: 0.01398317487989488, norm_res: 0.6944585086243867
Newton iter: 3, norm_du: 0.001305892530334922, norm_res: 0.09177029902887404
Newton iter: 4, norm_du: 3.499215243749025e-05, norm_res: 0.0008966452126635218
Newton iter: 5, norm_du: 2.9167780246235498e-08, norm_res: 1.822211063466128e-07
Newton iter: 6, norm_du: 4.07557128234043e-15, norm_res: 4.202789007831463e-12
Time step: 0.390 of 1.0
Newton iter: 1, norm_du: 0.012262177613219825, norm_res: 33.87750784044457
Newton iter: 2, norm_du: 0.013980238681222508, norm_res: 0.6857489937856569
Newton iter: 3, norm_du: 0.0012798687985517015, norm_res: 0.09089492863803754
Newton iter: 4, norm_du: 3.352964383865271e-05, norm_res: 0.0008515271045301444
Newton iter: 5, norm_du: 2.681092284367813e-08, norm_res: 1.6182048910155849e-07
Newton iter: 6, norm_du: 2.1824725840898506e-15, norm_res: 2.5907542272739838e-12
Time step: 0.400 of 1.0
Newton iter: 1, norm_du: 0.012153277130252056, norm_res: 33.71194221803763
Newton iter: 2, norm_du: 0.013979386873688323, norm_res: 0.6772065035525978
Newton iter: 3, norm_du: 0.001254568585303457, norm_res: 0.09005815344615636
Newton iter: 4, norm_du: 3.2148935803047674e-05, norm_res: 0.0008088953265673604
Newton iter: 5, norm_du: 2.467922784657687e-08, norm_res: 1.4483396767055795e-07
Newton iter: 6, norm_du: 1.1071635432433182e-15, norm_res: 3.196010754125389e-12
Time step: 0.410 of 1.0
Newton iter: 1, norm_du: 0.012045591312184058, norm_res: 33.54911294612036
Newton iter: 2, norm_du: 0.013980584378337541, norm_res: 0.6688258011743258
Newton iter: 3, norm_du: 0.0012299660366884729, norm_res: 0.08925869390345344
Newton iter: 4, norm_du: 3.08442631657041e-05, norm_res: 0.0007686006871544904
Newton iter: 5, norm_du: 2.2746785893768284e-08, norm_res: 1.3057223189381977e-07
Newton iter: 6, norm_du: 9.486423996117518e-16, norm_res: 3.239459947141408e-12
Time step: 0.420 of 1.0
Newton iter: 1, norm_du: 0.011939068840836141, norm_res: 33.38894384857027
Newton iter: 2, norm_du: 0.013983799527098053, norm_res: 0.6606018412564878
Newton iter: 3, norm_du: 0.0012060365624174541, norm_res: 0.08849535020093034
Newton iter: 4, norm_du: 2.9610378316625222e-05, norm_res: 0.0007305035981792283
Newton iter: 5, norm_du: 2.0991351151940673e-08, norm_res: 1.1847957439790883e-07
Newton iter: 6, norm_du: 1.3341525567721292e-15, norm_res: 1.8107478889333929e-12
Time step: 0.430 of 1.0
Newton iter: 1, norm_du: 0.011833659818300398, norm_res: 33.231361583655136
Newton iter: 2, norm_du: 0.013989003859840476, norm_res: 0.6525297604743432
Newton iter: 3, norm_du: 0.0011827567471335745, norm_res: 0.08776699749025836
Newton iter: 4, norm_du: 2.8442493029666196e-05, norm_res: 0.0006944734153797802
Newton iter: 5, norm_du: 1.9393713162820004e-08, norm_res: 1.0811496763304209e-07
Newton iter: 6, norm_du: 1.4268185834013336e-15, norm_res: 2.395735048841382e-12
Time step: 0.440 of 1.0
Newton iter: 1, norm_du: 0.011729315720143879, norm_res: 33.076295516658234
Newton iter: 2, norm_du: 0.013996171944858716, norm_res: 0.6446048687598084
Newton iter: 3, norm_du: 0.0011601042701795822, norm_res: 0.08707258152471757
Newton iter: 4, norm_du: 2.7336228220213595e-05, norm_res: 0.0006603878365142866
Newton iter: 5, norm_du: 1.7937195093831577e-08, norm_res: 9.913052686811319e-08
Newton iter: 6, norm_du: 1.63094391877012e-15, norm_res: 2.028656776455741e-12
Time step: 0.450 of 1.0
Newton iter: 1, norm_du: 0.011625989353602676, norm_res: 32.9236775990643
Newton iter: 2, norm_du: 0.014005281219948325, norm_res: 0.6368226409225773
Newton iter: 3, norm_du: 0.001138057832776866, norm_res: 0.08641111467255712
Newton iter: 4, norm_du: 2.628757037143115e-05, norm_res: 0.0006281323341956036
Newton iter: 5, norm_du: 1.660724767538787e-08, norm_res: 9.125384206844753e-08
Newton iter: 6, norm_du: 1.5791894582108779e-15, norm_res: 1.4051650041821992e-12
Time step: 0.460 of 1.0
Newton iter: 1, norm_du: 0.011523634820547718, norm_res: 32.7734422539416
Newton iter: 2, norm_du: 0.014016311851754035, norm_res: 0.6291787086978444
Newton iter: 3, norm_du: 0.001116597091773133, norm_res: 0.08578167228257269
Newton iter: 4, norm_du: 2.5292833596780766e-05, norm_res: 0.0005975996296280924
Newton iter: 5, norm_du: 1.5391119071811288e-08, norm_res: 8.427409380775825e-08
Newton iter: 6, norm_du: 1.613777525738202e-15, norm_res: 1.3443843994512154e-12
Time step: 0.470 of 1.0
Newton iter: 1, norm_du: 0.011422207485059817, norm_res: 32.62552626717603
Newton iter: 2, norm_du: 0.01402924661128455, norm_res: 0.6216688531773478
Newton iter: 3, norm_du: 0.0010957025991374975, norm_res: 0.08518338935363545
Newton iter: 4, norm_du: 2.4348626480397345e-05, norm_res: 0.0005686892072875591
Newton iter: 5, norm_du: 1.4277586259879743e-08, norm_res: 7.802761621823468e-08
Newton iter: 6, norm_du: 1.7105605678762324e-15, norm_res: 1.029503673145908e-12
Time step: 0.480 of 1.0
Newton iter: 1, norm_du: 0.011321663945514593, norm_res: 32.479868684209016
Newton iter: 2, norm_du: 0.014044070763953281, norm_res: 0.6142889975999245
Newton iter: 3, norm_du: 0.0010753557465950476, norm_res: 0.0846154574890351
Newton iter: 4, norm_du: 2.3451822997139747e-05, norm_res: 0.0005413068549489041
Newton iter: 5, norm_du: 1.325673284646788e-08, norm_res: 7.238911369399254e-08
Newton iter: 6, norm_du: 1.5467036081160542e-15, norm_res: 1.4079291479243007e-12
Time step: 0.490 of 1.0
Newton iter: 1, norm_du: 0.01122196201112418, norm_res: 32.33641071197055
Newton iter: 2, norm_du: 0.014060771972623499, norm_res: 0.6070352005048064
Newton iter: 3, norm_du: 0.001055538714836337, norm_res: 0.08407712211001356
Newton iter: 4, norm_du: 2.2599536916585057e-05, norm_res: 0.0005153642470424127
Newton iter: 5, norm_du: 1.2319766204216944e-08, norm_res: 6.726242312038762e-08
Newton iter: 6, norm_du: 1.3191233327559264e-15, norm_res: 1.2354755014536358e-12
Time step: 0.500 of 1.0
Newton iter: 1, norm_du: 0.011123060682936588, norm_res: 32.19509562571956
Newton iter: 2, norm_du: 0.014079340212407369, norm_res: 0.5999036491889705
Newton iter: 3, norm_du: 0.0010362344267822372, norm_res: 0.08356767990452374
Newton iter: 4, norm_du: 2.1789099202698676e-05, norm_res: 0.0004907785477042904
Newton iter: 5, norm_du: 1.1458864887177703e-08, norm_res: 6.25734071485569e-08
Newton iter: 6, norm_du: 1.2843723515000712e-15, norm_res: 7.816672058495737e-13
Time step: 0.510 of 1.0
Newton iter: 1, norm_du: 0.011024920139334773, norm_res: 32.055868680518365
Newton iter: 2, norm_du: 0.014099767696130522, norm_res: 0.5928906534760664
Newton iter: 3, norm_du: 0.0010174265044799822, norm_res: 0.08308647649932087
Newton iter: 4, norm_du: 2.1018038001159322e-05, norm_res: 0.00046747204762956574
Newton iter: 5, norm_du: 1.0667050579081832e-08, norm_res: 5.826446562963348e-08
Newton iter: 6, norm_du: 1.1401688336595689e-15, norm_res: 6.156104777576254e-13
Time step: 0.520 of 1.0
Newton iter: 1, norm_du: 0.010927501726123138, norm_res: 31.918677027073162
Newton iter: 2, norm_du: 0.014122048809547158, norm_res: 0.5859926397645562
Newton iter: 3, norm_du: 0.0009990992292865048, norm_res: 0.08263290432415471
Newton iter: 4, norm_du: 2.0284060857659353e-05, norm_res: 0.000445371821208307
Newton iter: 5, norm_du: 9.938080496686452e-09, norm_res: 5.429021529716361e-08
Newton iter: 6, norm_du: 1.0312716718173947e-15, norm_res: 1.6623554656424838e-12
Time step: 0.530 of 1.0
Newton iter: 1, norm_du: 0.010830767951327142, norm_res: 31.783469631706343
Newton iter: 2, norm_du: 0.014146180055510712, norm_res: 0.5792061453526055
Newton iter: 3, norm_du: 0.0009812375049768225, norm_res: 0.08220640067548583
Newton iter: 4, norm_du: 1.958503888239176e-05, norm_res: 0.0004244094152130569
Newton iter: 5, norm_du: 9.266356555778102e-09, norm_res: 5.061615710549353e-08
Newton iter: 6, norm_du: 8.901961136450685e-16, norm_res: 9.90831065248189e-13
Time step: 0.540 of 1.0
Newton iter: 1, norm_du: 0.01073468248488488, norm_res: 31.650197200239578
Newton iter: 2, norm_du: 0.014172160006394895, norm_res: 0.5725278129931347
Newton iter: 3, norm_du: 0.0009638268235062537, norm_res: 0.0818064459438961
Newton iter: 4, norm_du: 1.8918992603336816e-05, norm_res: 0.0004045205524682903
Newton iter: 5, norm_du: 8.646846729268868e-09, norm_res: 4.721387013304636e-08
Newton iter: 6, norm_du: 8.545401278167899e-16, norm_res: 3.2591903434162115e-13
Time step: 0.550 of 1.0
Newton iter: 1, norm_du: 0.010639210163446154, norm_res: 31.518812105557082
Newton iter: 2, norm_du: 0.014199989264198581, norm_res: 0.5659543856982977
Newton iter: 3, norm_du: 0.000946853233157173, norm_res: 0.08143256200867037
Newton iter: 4, norm_du: 1.8284079302831866e-05, norm_res: 0.00038564485859049317
Newton iter: 5, norm_du: 8.075019109838206e-09, norm_res: 4.4062365631921884e-08
Newton iter: 6, norm_du: 7.225030512728136e-16, norm_res: 2.087439699205204e-13
Time step: 0.560 of 1.0
Newton iter: 1, norm_du: 0.010544317000535855, norm_res: 31.389268318665163
Newton iter: 2, norm_du: 0.014229670427829228, norm_res: 0.5594827017500664
Newton iter: 3, norm_du: 0.000930303308854852, norm_res: 0.08108431078360788
Newton iter: 4, norm_du: 1.7678581646711663e-05, norm_res: 0.00036772560699407876
Newton iter: 5, norm_du: 7.546784693041048e-09, norm_res: 4.1141774154290975e-08
Newton iter: 6, norm_du: 7.068941202831514e-16, norm_res: 3.482041701552713e-13
Time step: 0.570 of 1.0
Newton iter: 1, norm_du: 0.01044997020240514, norm_res: 31.2615213430467
Newton iter: 2, norm_du: 0.01426120806715484, norm_res: 0.5531096899223057
Newton iter: 3, norm_du: 0.000914164124454096, norm_res: 0.08076129291018112
Newton iter: 4, norm_du: 1.710089745946899e-05, norm_res: 0.00035070948171533206
Newton iter: 5, norm_du: 7.058446896972081e-09, norm_res: 3.8437791939382176e-08
Newton iter: 6, norm_du: 5.172092626453294e-16, norm_res: 4.582866015509482e-13
Time step: 0.580 of 1.0
Newton iter: 1, norm_du: 0.01035613818992137, norm_res: 31.13552815214957
Newton iter: 2, norm_du: 0.014294608703433936, norm_res: 0.546832364883985
Newton iter: 3, norm_du: 0.0008984232267997812, norm_res: 0.08046314657852578
Newton iter: 4, norm_du: 1.6549530501696883e-05, norm_res: 0.00033454636011847445
Newton iter: 5, norm_du: 6.6066597801281405e-09, norm_res: 3.593776607525032e-08
Newton iter: 6, norm_du: 4.3579894136030607e-16, norm_res: 4.3515587023077484e-13
Time step: 0.590 of 1.0
Newton iter: 1, norm_du: 0.010262790626919411, norm_res: 31.01124712982039
Newton iter: 2, norm_du: 0.014329880795859088, norm_res: 0.5406478227830464
Newton iter: 3, norm_du: 0.0008830686114132674, norm_res: 0.08018954648319924
Newton iter: 4, norm_du: 1.6023082140356497e-05, norm_res: 0.00031918910454029926
Newton iter: 5, norm_du: 6.188389100371352e-09, norm_res: 3.3631375588819786e-08
Newton iter: 6, norm_du: 3.897571807189436e-16, norm_res: 8.535177349320266e-13
Time step: 0.600 of 1.0
Newton iter: 1, norm_du: 0.010169898455491213, norm_res: 30.888638013540678
Newton iter: 2, norm_du: 0.01436703473395143, norm_res: 0.5345532369888204
Newton iter: 3, norm_du: 0.0008680886996621493, norm_res: 0.07994020290125985
Newton iter: 4, norm_du: 1.552024380679933e-05, norm_res: 0.00030459337169997064
Newton iter: 5, norm_du: 5.800880695167154e-09, norm_res: 3.15104752021207e-08
Newton iter: 6, norm_du: 3.21087834846748e-16, norm_res: 2.2987183118274513e-14
Time step: 0.610 of 1.0
Newton iter: 1, norm_du: 0.010077433938751464, norm_res: 30.767661840324564
Newton iter: 2, norm_du: 0.014406082835604885, norm_res: 0.5285458539900411
Newton iter: 3, norm_du: 0.0008534723172749654, norm_res: 0.07971486088326821
Newton iter: 4, norm_du: 1.5039790158612571e-05, norm_res: 0.0002907174357434655
Newton iter: 5, norm_du: 5.441630621352934e-09, norm_res: 2.9565853367294726e-08
Newton iter: 6, norm_du: 2.781889896647466e-16, norm_res: 2.6639841636464644e-13
Time step: 0.620 of 1.0
Newton iter: 1, norm_du: 0.009985370711694527, norm_res: 30.64828089510876
Newton iter: 2, norm_du: 0.014447039350637236, norm_res: 0.5226229894292311
Newton iter: 3, norm_du: 0.0008392086740885149, norm_res: 0.07951329956699127
Newton iter: 4, norm_du: 1.458057286671976e-05, norm_res: 0.00027752201657290303
Newton iter: 5, norm_du: 5.108360077968778e-09, norm_res: 2.7792442724126948e-08
Newton iter: 6, norm_du: 2.7097629634834176e-16, norm_res: 1.1195230501066277e-12
Time step: 0.630 of 1.0
Newton iter: 1, norm_du: 0.009893683840825633, norm_res: 30.530458661548142
Newton iter: 2, norm_du: 0.014489920469706501, norm_res: 0.5167820242639077
Newton iter: 3, norm_du: 0.0008252873449054372, norm_res: 0.07933533158297155
Newton iter: 4, norm_du: 1.4141514959956598e-05, norm_res: 0.00026497013493673426
Newton iter: 5, norm_du: 4.798993066384787e-09, norm_res: 2.618456604459357e-08
Newton iter: 6, norm_du: 2.2196447694667814e-16, norm_res: 3.4732737439693696e-13
Time step: 0.640 of 1.0
Newton iter: 1, norm_du: 0.00980234989333974, norm_res: 30.414159775047143
Newton iter: 2, norm_du: 0.014534744338566075, norm_res: 0.5110204010463798
Newton iter: 3, norm_du: 0.0008116982514105727, norm_res: 0.07918080259013639
Newton iter: 4, norm_du: 1.3721605674968507e-05, norm_res: 0.00025302695790018696
Newton iter: 5, norm_du: 4.511635983861849e-09, norm_res: 2.4736680910966917e-08
Newton iter: 6, norm_du: 2.315528019594953e-16, norm_res: 2.1394454238084985e-13
Time step: 0.650 of 1.0
Newton iter: 1, norm_du: 0.009711347016713546, norm_res: 30.299349977947955
Newton iter: 2, norm_du: 0.014581531077533625, norm_res: 0.5053356203111046
Newton iter: 3, norm_du: 0.0007984316449800234, norm_res: 0.07904959089432093
Newton iter: 4, norm_du: 1.331989575225688e-05, norm_res: 0.00024165966963035984
Newton iter: 5, norm_du: 4.2445604646712945e-09, norm_res: 2.3444468077919135e-08
Newton iter: 6, norm_du: 2.3488827067134175e-16, norm_res: 6.90884368933495e-13
Time step: 0.660 of 1.0
Newton iter: 1, norm_du: 0.009620655029673167, norm_res: 30.18599607673696
Newton iter: 2, norm_du: 0.014630302806252807, norm_res: 0.4997252370535307
Newton iter: 3, norm_du: 0.0007854780903901853, norm_res: 0.0789416071765682
Newton iter: 4, norm_du: 1.2935493140077495e-05, norm_res: 0.00023083734390041033
Newton iter: 5, norm_du: 3.996186780796764e-09, norm_res: 2.230382712025131e-08
Newton iter: 6, norm_du: 2.640487388514901e-16, norm_res: 6.285349590266257e-13
Time step: 0.670 of 1.0
Newton iter: 1, norm_du: 0.009530255525616533, norm_res: 30.07406590119215
Newton iter: 2, norm_du: 0.014681083673691976, norm_res: 0.494186857298452
Newton iter: 3, norm_du: 0.000772828450266937, norm_res: 0.07885679432797829
Newton iter: 4, norm_du: 1.2567559062656365e-05, norm_res: 0.00022053082627069556
Newton iter: 5, norm_du: 3.765070590774243e-09, norm_res: 2.130984238303785e-08
Newton iter: 6, norm_du: 2.1258032400727368e-16, norm_res: 6.984529989161394e-13
Time step: 0.680 of 1.0
Newton iter: 1, norm_du: 0.009440131989689045, norm_res: 29.963528265346167
Newton iter: 2, norm_du: 0.014733899893479178, norm_res: 0.4887181347457287
Newton iter: 3, norm_du: 0.0007604738702826271, norm_res: 0.07879512737339872
Newton iter: 4, norm_du: 1.2215304420981677e-05, norm_res: 0.00021071262625675872
Newton iter: 5, norm_du: 3.5498897702725234e-09, norm_res: 2.0458454972123358e-08
Newton iter: 6, norm_du: 2.66255256009001e-16, norm_res: 4.240901780897354e-13
Time step: 0.690 of 1.0
Newton iter: 1, norm_du: 0.00935026993085091, norm_res: 29.854352930199294
Newton iter: 2, norm_du: 0.014788779784624817, norm_res: 0.48331676747613045
Newton iter: 3, norm_du: 0.0007484057649620141, norm_res: 0.07875661350470393
Newton iter: 4, norm_du: 1.1877986492504111e-05, norm_res: 0.00020135681139812385
Newton iter: 5, norm_du: 3.34943295781703e-09, norm_res: 1.974570529175908e-08
Newton iter: 6, norm_du: 2.2646881312047487e-16, norm_res: 3.9938277383733716e-13
Time step: 0.700 of 1.0
Newton iter: 1, norm_du: 0.00926065703041158, norm_res: 29.746510568067535
Newton iter: 2, norm_du: 0.01484575381776854, norm_res: 0.47798049472187215
Newton iter: 3, norm_du: 0.0007366158041057701, norm_res: 0.07874129220768293
Newton iter: 4, norm_du: 1.1554905905324386e-05, norm_res: 0.0001924389143403896
Newton iter: 5, norm_du: 3.1625903080877986e-09, norm_res: 1.9165298064986057e-08
Newton iter: 6, norm_du: 1.8117449286956698e-16, norm_res: 4.619212419304764e-13
Time step: 0.710 of 1.0
Newton iter: 1, norm_du: 0.009171283308694943, norm_res: 29.639972728513698
Newton iter: 2, norm_du: 0.01490485466706427, norm_res: 0.4727070936791869
Newton iter: 3, norm_du: 0.0007250958997274232, norm_res: 0.07874923549656121
Newton iter: 4, norm_du: 1.1245403861838501e-05, norm_res: 0.00018393584005267278
Newton iter: 5, norm_du: 2.9883428689803008e-09, norm_res: 1.8711585473612e-08
Newton iter: 6, norm_du: 3.039445395812465e-16, norm_res: 9.287066865670167e-13
Time step: 0.720 of 1.0
Newton iter: 1, norm_du: 0.009082141311642939, norm_res: 29.53471180575955
Newton iter: 2, norm_du: 0.014966117267872489, norm_res: 0.467494376355957
Newton iter: 3, norm_du: 0.0007138381934486796, norm_res: 0.07878054824310107
Newton iter: 4, norm_du: 1.0948859590256375e-05, norm_res: 0.00017582578920642212
Newton iter: 5, norm_du: 2.8257558838980467e-09, norm_res: 1.838014883359495e-08
Newton iter: 6, norm_du: 2.754767435322011e-16, norm_res: 4.284401689395231e-13
Time step: 0.730 of 1.0
Newton iter: 1, norm_du: 0.008993226319391189, norm_res: 29.430701007510535
Newton iter: 2, norm_du: 0.015029578880502319, norm_res: 0.4623401864564376
Newton iter: 3, norm_du: 0.0007028350443535173, norm_res: 0.078835368622465
Newton iter: 4, norm_du: 1.0664688006506023e-05, norm_res: 0.0001680881706114641
Newton iter: 5, norm_du: 2.673970377206194e-09, norm_res: 1.816142527796635e-08
Newton iter: 6, norm_du: 1.5958256601243895e-16, norm_res: 5.472230505900114e-13
Time step: 0.740 of 1.0
Newton iter: 1, norm_du: 0.008904536579053388, norm_res: 29.327914325140938
Newton iter: 2, norm_du: 0.015095279160167814, norm_res: 0.4572423962696492
Newton iter: 3, norm_du: 0.0006920790171784198, norm_res: 0.07891386865552161
Newton iter: 4, norm_du: 1.0392337570538905e-05, norm_res: 0.00016070353187092485
Newton iter: 5, norm_du: 2.532196831464693e-09, norm_res: 1.804922039971562e-08
Newton iter: 6, norm_du: 2.290377673915766e-16, norm_res: 1.0542419700198233e-12
Time step: 0.750 of 1.0
Newton iter: 1, norm_du: 0.008816073564170964, norm_res: 29.226326505142566
Newton iter: 2, norm_du: 0.015163260233494465, norm_res: 0.4521989035813747
Newton iter: 3, norm_du: 0.0006815628708620504, norm_res: 0.07901625487237308
Newton iter: 4, norm_du: 1.0131288323506089e-05, norm_res: 0.00015365349448187882
Newton iter: 5, norm_du: 2.3997089580524885e-09, norm_res: 1.8037532288560503e-08
Newton iter: 6, norm_du: 2.4816007028966155e-16, norm_res: 3.494740368437063e-13
Time step: 0.760 of 1.0
Newton iter: 1, norm_du: 0.008727842263559773, norm_res: 29.125913021802475
Newton iter: 2, norm_du: 0.015233566781801041, norm_res: 0.4472076285685432
Newton iter: 3, norm_du: 0.0006712795473386298, norm_res: 0.07914276907846067
Newton iter: 4, norm_du: 9.881050091758055e-06, norm_res: 0.00014692068350767416
Newton iter: 5, norm_du: 2.2758385674487396e-09, norm_res: 1.811653929709228e-08
Newton iter: 6, norm_du: 2.2007170072004789e-16, norm_res: 7.221942853292082e-13
Time step: 0.770 of 1.0
Newton iter: 1, norm_du: 0.0086398515025319, norm_res: 29.026650051039535
Newton iter: 2, norm_du: 0.015306246131573614, norm_res: 0.44226651069582923
Newton iter: 3, norm_du: 0.0006612221606030164, norm_res: 0.07929368926052406
Newton iter: 4, norm_du: 9.641160849810989e-06, norm_res: 0.0001404886691072266
Newton iter: 5, norm_du: 2.1599697338233274e-09, norm_res: 1.8282188796954128e-08
Newton iter: 6, norm_du: 3.4273196357292586e-16, norm_res: 3.461792200533572e-13
Time step: 0.780 of 1.0
Newton iter: 1, norm_du: 0.008552114299776734, norm_res: 28.9285144453433
Newton iter: 2, norm_du: 0.015381348352419095, norm_res: 0.43737350557415106
Newton iter: 3, norm_du: 0.0006513839859316426, norm_res: 0.0794693305985066
Newton iter: 4, norm_du: 9.411185234174094e-06, norm_res: 0.00013434191516543885
Newton iter: 5, norm_du: 2.051535765779651e-09, norm_res: 1.8525330695326496e-08
Newton iter: 6, norm_du: 3.0286737851923995e-16, norm_res: 4.084747773133234e-13
Time step: 0.790 of 1.0
Newton iter: 1, norm_du: 0.008464648263472091, norm_res: 28.831483709764274
Newton iter: 2, norm_du: 0.015458926362993081, norm_res: 0.4325265817943721
Newton iter: 3, norm_du: 0.0006417584492770459, norm_res: 0.0796700466214735
Newton iter: 4, norm_du: 9.190713200516115e-06, norm_res: 0.0001284657185327534
Newton iter: 5, norm_du: 1.950013465341759e-09, norm_res: 1.8842785143779998e-08
Newton iter: 6, norm_du: 3.3291328751067025e-16, norm_res: 8.600837689374924e-13
Time step: 0.800 of 1.0
Newton iter: 1, norm_du: 0.008377476030534995, norm_res: 28.735535978898447
Newton iter: 2, norm_du: 0.015539036045303902, norm_res: 0.4277237177096379
Newton iter: 3, norm_du: 0.0006323391167429138, norm_res: 0.07989623049814856
Newton iter: 4, norm_du: 8.979358819822786e-06, norm_res: 0.0001228461667563051
Newton iter: 5, norm_du: 1.8549206059338352e-09, norm_res: 1.922827724851414e-08
Newton iter: 6, norm_du: 3.2761329142142343e-16, norm_res: 3.572105172867302e-13
Time step: 0.810 of 1.0
Newton iter: 1, norm_du: 0.008290625753243632, norm_res: 28.640649994845617
Newton iter: 2, norm_du: 0.015621736367918476, norm_res: 0.42296289815909044
Newton iter: 3, norm_du: 0.0006231196841207899, norm_res: 0.08014831647880588
Newton iter: 4, norm_du: 8.776759214384894e-06, norm_res: 0.0001174700872634091
Newton iter: 5, norm_du: 1.7658121320481003e-09, norm_res: 1.9681312406147642e-08
Newton iter: 6, norm_du: 4.449181440269015e-16, norm_res: 5.514982145863019e-13
Time step: 0.820 of 1.0
Newton iter: 1, norm_du: 0.008204131637778793, norm_res: 28.546805086053464
Newton iter: 2, norm_du: 0.015707089518634643, norm_res: 0.4182421111214079
Newton iter: 3, norm_du: 0.0006140939664383938, norm_res: 0.0804267814890357
Newton iter: 4, norm_du: 8.58257362776663e-06, norm_res: 0.00011232500283060708
Newton iter: 5, norm_du: 1.6822769332230547e-09, norm_res: 2.0195690467654505e-08
Newton iter: 6, norm_du: 2.357266986717515e-16, norm_res: 2.8534133929218666e-14
Time step: 0.830 of 1.0
Newton iter: 1, norm_du: 0.008118034539600189, norm_res: 28.45398114703821
Newton iter: 2, norm_du: 0.015795161047204, norm_res: 0.41355934428107205
Newton iter: 3, norm_du: 0.0006052558874525066, norm_res: 0.0807321468865915
Newton iter: 4, norm_du: 8.396482637259979e-06, norm_res: 0.00010739910137157863
Newton iter: 5, norm_du: 1.6039352535742243e-09, norm_res: 2.0771832067828362e-08
Newton iter: 6, norm_du: 3.3272506148056273e-16, norm_res: 1.0902458778799066e-12
Time step: 0.840 of 1.0
Newton iter: 1, norm_du: 0.008032382620846133, norm_res: 28.362158618937823
Newton iter: 2, norm_du: 0.015886020018829307, norm_res: 0.4089125814906741
Newton iter: 3, norm_du: 0.0005965994690839901, norm_res: 0.08106498041659048
Newton iter: 4, norm_du: 8.21818751027957e-06, norm_res: 0.00010268118047193938
Newton iter: 5, norm_du: 1.5304358706030245e-09, norm_res: 2.140863454096168e-08
Newton iter: 6, norm_du: 3.472851653315428e-16, norm_res: 1.1756032727377714e-14
Time step: 0.850 of 1.0
Newton iter: 1, norm_du: 0.00794723207525473, norm_res: 28.271318470842505
Newton iter: 2, norm_du: 0.015979739179091748, norm_res: 0.404299799127661
Newton iter: 3, norm_du: 0.0005881188206719856, norm_res: 0.08142589832594122
Newton iter: 4, norm_du: 8.04740971333808e-06, norm_res: 9.816062813463993e-05
Newton iter: 5, norm_du: 1.4614544590379897e-09, norm_res: 2.2107850451527718e-08
Newton iter: 6, norm_du: 2.6852384858722365e-16, norm_res: 7.238683717151386e-14
Time step: 0.860 of 1.0
Newton iter: 1, norm_du: 0.007862647926319435, norm_res: 28.181442181883376
Newton iter: 2, norm_du: 0.01607639513119827, norm_res: 0.3997189623017547
Newton iter: 3, norm_du: 0.0005798081280672619, norm_res: 0.08181556770603969
Newton iter: 4, norm_du: 7.883890588226321e-06, norm_res: 9.382738019589103e-05
Newton iter: 5, norm_du: 1.3966904539582685e-09, norm_res: 2.28704874673602e-08
Newton iter: 6, norm_du: 3.060789742521457e-16, norm_res: 5.5958329355135455e-14
Time step: 0.870 of 1.0
Newton iter: 1, norm_du: 0.007778704904539951, norm_res: 28.09251172403156
Newton iter: 2, norm_du: 0.016176068526366372, norm_res: 0.3951680209300076
Newton iter: 3, norm_du: 0.0005716616424388738, norm_res: 0.08223470905150979
Newton iter: 4, norm_du: 7.72739121216085e-06, norm_res: 8.967189392167147e-05
Newton iter: 5, norm_du: 1.3358656825213926e-09, norm_res: 2.3700248856329898e-08
Newton iter: 6, norm_du: 3.6042678298022397e-16, norm_res: 6.034611783435072e-13
Time step: 0.880 of 1.0
Newton iter: 1, norm_du: 0.007695488409675238, norm_res: 28.00450954559532
Newton iter: 2, norm_du: 0.016278844268362923, norm_res: 0.39064490562615595
Newton iter: 3, norm_du: 0.0005636736687917455, norm_res: 0.08268409904985746
Newton iter: 4, norm_du: 7.5776924636897e-06, norm_res: 8.568511472742829e-05
Newton iter: 5, norm_du: 1.2787228336313708e-09, norm_res: 2.4601672771566264e-08
Newton iter: 6, norm_du: 3.0341923307117513e-16, norm_res: 3.7567876184960043e-13
Time step: 0.890 of 1.0
Newton iter: 1, norm_du: 0.00761309556375047, norm_res: 27.91741855535113
Newton iter: 2, norm_du: 0.01638481173323174, norm_res: 0.3861475234069111
Newton iter: 3, norm_du: 0.0005558385540800453, norm_res: 0.083164573629375
Newton iter: 4, norm_du: 7.4345953275169235e-06, norm_res: 8.185845277697694e-05
Newton iter: 5, norm_du: 1.2250231689323125e-09, norm_res: 2.5579435902561426e-08
Newton iter: 6, norm_du: 3.2075484399020346e-16, norm_res: 7.429990318779255e-13
Time step: 0.900 of 1.0
Newton iter: 1, norm_du: 0.007531636360262697, norm_res: 27.831222107322592
Newton iter: 2, norm_du: 0.016494065005379796, norm_res: 0.38167375318872176
Newton iter: 3, norm_du: 0.0005481506748811567, norm_res: 0.08367703130007396
Newton iter: 4, norm_du: 7.297921481515619e-06, norm_res: 7.818375499643913e-05
Newton iter: 5, norm_du: 1.1745451309381616e-09, norm_res: 2.6642788428858417e-08
Newton iter: 6, norm_du: 2.927522443819145e-16, norm_res: 2.816917714678048e-13
Time step: 0.910 of 1.0
Newton iter: 1, norm_du: 0.007451234914367411, norm_res: 27.745903986130397
Newton iter: 2, norm_du: 0.016606703131310543, norm_res: 0.3772214410311297
Newton iter: 3, norm_du: 0.0005406044245218169, norm_res: 0.08422243676679649
Newton iter: 4, norm_du: 7.167514211024878e-06, norm_res: 7.465328686944799e-05
Newton iter: 5, norm_du: 1.1270837407444869e-09, norm_res: 2.7795941574485075e-08
Newton iter: 6, norm_du: 4.1150490216926246e-16, norm_res: 2.391543303801687e-13
Time step: 0.920 of 1.0
Newton iter: 1, norm_du: 0.007372030817884272, norm_res: 27.66144839293559
Newton iter: 2, norm_du: 0.016722830392381805, norm_res: 0.3727883951350609
Newton iter: 3, norm_du: 0.0005331941996010844, norm_res: 0.08480182490904878
Newton iter: 4, norm_du: 7.043239733172035e-06, norm_res: 7.125970619447524e-05
Newton iter: 5, norm_du: 1.0824481313914314e-09, norm_res: 2.9053694080790193e-08
Newton iter: 6, norm_du: 4.0932887002716846e-16, norm_res: 7.161144291327447e-13
Time step: 0.930 of 1.0
Newton iter: 1, norm_du: 0.007294180601462032, norm_res: 27.5778399319048
Newton iter: 2, norm_du: 0.016842556598165515, norm_res: 0.36837238052708743
Newton iter: 3, norm_du: 0.00052591438578865, norm_res: 0.0854163050822138
Newton iter: 4, norm_du: 6.924989008118781e-06, norm_res: 6.799604649827111e-05
Newton iter: 5, norm_du: 1.040461319710857e-09, norm_res: 3.042636438452313e-08
Newton iter: 6, norm_du: 3.0035954689960363e-16, norm_res: 3.5635268699442677e-13
Time step: 0.940 of 1.0
Newton iter: 1, norm_du: 0.007217859304285718, norm_res: 27.495063597217847
Newton iter: 2, norm_du: 0.01696599740204854, norm_res: 0.36397111343961014
Newton iter: 3, norm_du: 0.0005187593428229456, norm_res: 0.08606706584358659
Newton iter: 4, norm_du: 6.812680168697271e-06, norm_res: 6.485569444408625e-05
Newton iter: 5, norm_du: 1.0009585304123974e-09, norm_res: 3.192748572244758e-08
Newton iter: 6, norm_du: 2.6525888861258526e-16, norm_res: 6.171678001279023e-13
Time step: 0.950 of 1.0
Newton iter: 1, norm_du: 0.007143262148939562, norm_res: 27.41310476055391
Newton iter: 2, norm_du: 0.017093274641005577, norm_res: 0.3595822553071533
Newton iter: 3, norm_du: 0.0005117233885953514, norm_res: 0.08675538007477274
Newton iter: 4, norm_du: 6.706261716070433e-06, norm_res: 6.183238752144614e-05
Newton iter: 5, norm_du: 9.637863545019885e-10, norm_res: 3.357924852451979e-08
Newton iter: 6, norm_du: 3.007886615381165e-16, norm_res: 1.108097349880251e-12
Time step: 0.960 of 1.0
Newton iter: 1, norm_du: 0.007070606315572243, norm_res: 27.33194915906775
Newton iter: 2, norm_du: 0.017224516701503757, norm_res: 0.35520340638058984
Newton iter: 3, norm_du: 0.0005048007821736874, norm_res: 0.08748261059442014
Newton iter: 4, norm_du: 6.605716689198307e-06, norm_res: 5.892018327339491e-05
Newton iter: 5, norm_du: 9.288018824740962e-10, norm_res: 3.539903070880098e-08
Newton iter: 6, norm_du: 2.536097229793895e-16, norm_res: 1.2193922489275971e-13
Time step: 0.970 of 1.0
Newton iter: 1, norm_du: 0.007000132804952178, norm_res: 27.2515828838093
Newton iter: 2, norm_du: 0.01735985891388152, norm_res: 0.3508320988967245
Newton iter: 3, norm_du: 0.0004979857056806571, norm_res: 0.0882502162723987
Newton iter: 4, norm_du: 6.5110680847088176e-06, norm_res: 5.6113463257193866e-05
Newton iter: 5, norm_du: 8.958715237912453e-10, norm_res: 3.741104387365119e-08
Newton iter: 6, norm_du: 2.475612049165772e-16, norm_res: 4.3951669396335806e-13
Time step: 0.980 of 1.0
Newton iter: 1, norm_du: 0.006932108374478094, norm_res: 27.171992368587702
Newton iter: 2, norm_du: 0.017499443977629404, norm_res: 0.3464657897553011
Newton iter: 3, norm_du: 0.0004912722448344036, norm_res: 0.08905975871562137
Newton iter: 4, norm_du: 6.422385888260018e-06, norm_res: 5.340692000794115e-05
Newton iter: 5, norm_du: 8.648702625792762e-10, norm_res: 3.9648929886279855e-08
Newton iter: 6, norm_du: 4.3476739315171575e-16, norm_res: 6.127362517652514e-13
Time step: 0.990 of 1.0
Newton iter: 1, norm_du: 0.0068668275244112495, norm_res: 27.093164379248996
Newton iter: 2, norm_du: 0.01764342242032809, norm_res: 0.3421018526651075
Newton iter: 3, norm_du: 0.00048465436802375795, norm_res: 0.08991290956315821
Newton iter: 4, norm_du: 6.339796206109888e-06, norm_res: 5.079554367020995e-05
Newton iter: 5, norm_du: 8.356804894712678e-10, norm_res: 4.2145931745348985e-08
Newton iter: 6, norm_du: 4.89767924251233e-16, norm_res: 4.53366503369938e-13
Time step: 1.000 of 1.0
Newton iter: 1, norm_du: 0.006804614503610238, norm_res: 27.015086003348923
Newton iter: 2, norm_du: 0.017791953093319907, norm_res: 0.33773756969670254
Newton iter: 3, norm_du: 0.00047812590375603483, norm_res: 0.09081145847454354
Newton iter: 4, norm_du: 6.26349316919986e-06, norm_res: 4.827463849493396e-05
Newton iter: 5, norm_du: 8.081914519335502e-10, norm_res: 4.4948273065694404e-08
Newton iter: 6, norm_du: 2.446565429046995e-16, norm_res: 1.6590340839097458e-13
Deformed Volume: 1.0004995007486277

Provide feedback

Saved searches

Use saved searches to filter your results more quickly

Jump terms in elasticity #760

{{title}}

{{editor}}'s edit

{{editor}}'s edit

Replies: 1 comment 25 replies

{{title}}

{{title}}

{{title}}

{{title}}

{{title}}

{{title}}

{{editor}}'s edit

{{editor}}'s edit

Select a reply

Jump terms in elasticity #760

bhaveshshrimali Oct 15, 2021

Reference:

Replies: 1 comment · 25 replies

kinnala Oct 15, 2021 Maintainer

kinnala Oct 21, 2021 Maintainer

bhaveshshrimali Oct 22, 2021 Author

bhaveshshrimali Oct 22, 2021 Author

kinnala Oct 22, 2021 Maintainer

bhaveshshrimali Oct 22, 2021 Author

Code

Convergence

Results

bhaveshshrimali
Oct 15, 2021

Replies: 1 comment 25 replies

kinnala
Oct 15, 2021
Maintainer

kinnala Oct 21, 2021
Maintainer

bhaveshshrimali Oct 22, 2021
Author

bhaveshshrimali Oct 22, 2021
Author

kinnala Oct 22, 2021
Maintainer

bhaveshshrimali Oct 22, 2021
Author