mirror of
https://github.com/JuliaFEM/JuliaFEM.jl.git
synced 2026-08-06 04:21:33 +00:00
added assembly example
This commit is contained in:
Jukka Aho
@@ -1,2 +1,3 @@
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*~
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.DS_Store
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.ipynb_checkpoints
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@@ -0,0 +1,565 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#Assembly\n",
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"\n",
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"Let's make two element model and assemble it. We split the previous one element model to 2 quadrilaterals, make assembly and solve it. Small modifications to functions, I think it's better that they don't allocate memory but do in place operations. \n",
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"\n",
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"**TODO**\n",
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"- Tangent stiffness is calculated using forward finite difference. I think we should try ReverseDiffSparse for it's sparse matrix support, but I don't know how to use it. Or alternatively use FAD like before and assemble after linearization. It would be nice experiment to try linearization *after* assembly, would it work?\n",
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"- Verify calculations using some well known FEM software.\n",
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"\n",
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"\n",
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"Author: Jukka Aho\n",
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"\n",
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"Email: <jukka.aho@kapsi.fi>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0x00000c95556709c2"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"tic()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Dict{Any,Any} with 0 entries"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"type Node\n",
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" coords\n",
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"end\n",
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"type Element\n",
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" node_ids\n",
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"end\n",
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"elements = Dict()\n",
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"nodes = Dict()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Element([5,6,3,2])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"ndim = 2\n",
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"nnodes = 6\n",
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"\n",
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"nodes[1] = Node([0, 1, 0])\n",
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"nodes[2] = Node([5, 1, 0])\n",
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"nodes[3] = Node([10, 1, 0])\n",
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"nodes[4] = Node([0, 0, 0])\n",
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"nodes[5] = Node([5, 0, 0])\n",
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"nodes[6] = Node([10, 0, 0])\n",
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"elements[1] = Element([4, 5, 2, 1])\n",
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"elements[2] = Element([5, 6, 3, 2])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(36.0,24.0)"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Partial derivatives of bilinear Lagrange polynomials\n",
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"dNdξ(ξ) = [[-(1-ξ[2])/4.0 -(1-ξ[1])/4.0],\n",
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" [ (1-ξ[2])/4.0 -(1+ξ[1])/4.0],\n",
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" [ (1+ξ[2])/4.0 (1+ξ[1])/4.0],\n",
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" [-(1+ξ[2])/4.0 (1-ξ[1])/4.0]] \n",
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"\n",
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"E = 90\n",
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"ν = 0.25\n",
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"μ = E/(2*(1+ν))\n",
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"λ = E*ν/((1+ν)*(1-2*ν))\n",
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"λ = 2*λ*μ/(λ + 2*μ)\n",
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"μ, λ"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"calculate_internal_energy! (generic function with 2 methods)"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"function calculate_internal_energy!(X, u, Wint, dNdξ, λ, μ, dim=2)\n",
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" \"\"\"Calculate internal energy for a single element.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" X : array [dim x nodes]\n",
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" u : array [dim x nodes]\n",
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" dNdξ : shape function derivatives\n",
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" λ : float\n",
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" μ : float\n",
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" dim : integer, optinal\n",
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"\n",
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" Returns\n",
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" -------\n",
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" Nothing, this is inplace function\n",
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" \n",
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" \"\"\"\n",
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" I = eye(dim)\n",
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" \n",
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" function J(ξ)\n",
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" Jᵀ = X*dNdξ(ξ)\n",
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" ∇N = inv(Jᵀ)*dNdξ(ξ)'\n",
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" ∇u = u*∇N'\n",
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" F = I + ∇u # Deformation gradient\n",
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" E = 1/2*(∇u' + ∇u + ∇u'*∇u) # Green-Lagrange strain tensor\n",
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" P = λ*trace(E)*I + 2*μ*E # PK1 stress tensor\n",
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" S = F*P # PK2 stress tensor\n",
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" return S*∇N*det(Jᵀ)\n",
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" end\n",
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"\n",
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" a = 1/sqrt(3)\n",
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" ipoints = [[-a -a], [a -a], [a a], [-a a]]\n",
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" iweights = [1 1 1 1]\n",
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"\n",
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" for m = 1:length(iweights)\n",
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" w = iweights[m]\n",
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" ξ = ipoints[m, :]\n",
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" Wint[:,:] += w*J(ξ)\n",
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" end\n",
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"\n",
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"end"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2x4 Array{Float64,2}:\n",
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" -60.6667 -1.33333 133.333 -71.3333\n",
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" -24.0 -8.0 35.0 -3.0 "
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"Wint = zeros(2, 4)\n",
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"X = [0 0; 1 0; 1 1; 0 1]'\n",
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"u = [0 0; 0 0; 1 0; 0 0]'\n",
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"calculate_internal_energy!(X, u, Wint, dNdξ, λ, μ)\n",
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"Wint"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"assemble! (generic function with 1 method)"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"function assemble!(u, R)\n",
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" \"\"\" Assemble global residual vector R = T - F\n",
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" \"\"\"\n",
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" R[:] = 0.0\n",
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" u = reshape(u, ndim, nnodes)\n",
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" R = reshape(R, ndim, nnodes)\n",
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" \n",
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" Xe = zeros(ndim, 4)\n",
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" Winte = zeros(ndim, 4)\n",
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" \n",
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" # Internal forces, T\n",
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" for i=1:length(elements) # loop through elements\n",
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" Xe[:,:] = 0.0 # FIXME: how to efficiently empty array?\n",
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" el = elements[i]\n",
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" nids = el.node_ids\n",
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" for i=1:length(nids) # loop through nodes\n",
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" Xe[:,i] = nodes[nids[i]].coords[1:2]\n",
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" end\n",
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" Winte[:,:] = 0.0\n",
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" calculate_internal_energy!(Xe, u[:,nids], Winte, dNdξ, λ, μ)\n",
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" R[:,nids] += Winte\n",
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" end\n",
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"\n",
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" # External forces, F\n",
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" # T - F = T + (-F)\n",
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" R[2, 3] += 2 # Force to the tip of härveli\n",
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"\n",
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" u = reshape(u, ndim*nnodes)\n",
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" R = reshape(R, ndim*nnodes)\n",
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"end"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1x12 Array{Float64,2}:\n",
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" 0.0 0.0 34.8853 16.2 140.715 78.6 … -64.1013 -36.0 -111.499 -56.8"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"R = zeros(ndim*nnodes)\n",
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"u = [0 0; 0 0; 1 0; 0 0; 0 0; 0 0]'\n",
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"#u = [0 0; 0 0; 1 0; 0 0]'\n",
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"assemble!(reshape(u, ndim*nnodes), R)\n",
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"R'"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We should use ReverseDiffSparse because of it's sparse matrix support"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# FIXME: I don't know how to get these working!\n",
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"#using ReverseDiffSparse\n",
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"#using ForwardDiff\n",
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"#Kt! = forwarddiff_jacobian!(assemble!, Float64, fadtype=:dual, n=12, m=12)\n",
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"#Kt!(reshape(u, 12), Kt)\n",
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"#Kt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"Lin (generic function with 2 methods)"
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# So we go to plan B. FIXME: change this to analytical version\n",
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"\n",
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"function Lin(f!, h=1.0e-6)\n",
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"\n",
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" function D!(x, J)\n",
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" J[:,:] = 0\n",
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" N = length(x)\n",
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" Δx = zeros(N)\n",
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" y = zeros(N)\n",
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" Δy = zeros(N)\n",
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" f!(x, y) # Evaluate function f in x and store results to y\n",
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" for i=1:N\n",
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" Δx[:] = 0.0\n",
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" Δx[i] += h\n",
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" f!(x+Δx, Δy) # Evaluate function f in x+Δx and store results to Δy\n",
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" J[i, :] = (Δy-y) / h\n",
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" end\n",
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" end\n",
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"\n",
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" return D!\n",
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"\n",
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"end"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Handling homogeneous Dirichlet conditions, using elimination.\n",
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"\n",
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"**INFO**: We could try something like this: http://www.code-aster.org/V2/doc/default/en/man_r/r3/r3.03.01.pdf\n",
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"\n",
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"Here's an idea how to make a very simply elimination"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2x6 Array{Int64,2}:\n",
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" 1 0 0 1 0 0\n",
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" 1 0 0 1 0 0"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"fixed_dofs = integer(zeros(ndim, nnodes))\n",
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"fixed_dofs[:,1] = fixed_dofs[:,4] = 1\n",
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"fixed_dofs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"2x6 Array{Int64,2}:\n",
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" 0 1 1 0 1 1\n",
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" 0 1 1 0 1 1"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"free_dofs = integer(ones(ndim, nnodes)) - fixed_dofs\n",
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"free_dofs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"1x8 Array{Int64,2}:\n",
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" 3 4 5 6 9 10 11 12"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"free_dofs = find(free_dofs)\n",
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"free_dofs'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Starting Newton iterations\n",
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"Iteration 1, norm = 12.031651257897487\n",
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"Iteration 2, norm = 3.141275068747564\n",
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"Iteration 3, norm = 1.1530769747882408\n",
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"Iteration 4, norm = 0.2665784114781298\n",
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"Iteration 5, norm = 0.035146969229970175\n",
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"Iteration 6, norm = 0.003125584242156337\n",
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"Iteration 7, norm = 2.227371173806294e-6\n",
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"Iteration 8, norm = 2.1397676611257455e-10\n",
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"Converged.\n"
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]
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},
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{
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"data": {
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||||
"text/plain": [
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||||
"2x6 Array{Float64,2}:\n",
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||||
" 0.0 -0.106192 -1.69115 0.0 -0.761252 -2.48604\n",
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||||
" 0.0 -2.10509 -6.00728 0.0 -1.89221 -5.5914 "
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]
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},
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"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"function solve!(u, free_dofs; max_iterations=10, eps=1.0e-7)\n",
|
||||
" ∇R = Lin(assemble!)\n",
|
||||
" Kt = zeros(ndim*nnodes, ndim*nnodes)\n",
|
||||
" R = zeros(size(u))\n",
|
||||
" println(\"Starting Newton iterations\")\n",
|
||||
" for i=1:max_iterations\n",
|
||||
" print(\"Iteration \",i, \", \")\n",
|
||||
" R[:] = 0.0\n",
|
||||
" assemble!(u, R) # Calculate internal energy in nodes and store results to R\n",
|
||||
" ∇R(u, Kt) # Linearize residual in u and save result to Kt\n",
|
||||
" # Solve !\n",
|
||||
" du = Kt[free_dofs, free_dofs] \\ -R[free_dofs]\n",
|
||||
" u[free_dofs] += du\n",
|
||||
" println(\"norm = \",norm(du))\n",
|
||||
" if norm(du) < eps\n",
|
||||
" println(\"Converged.\")\n",
|
||||
" break\n",
|
||||
" end\n",
|
||||
" end\n",
|
||||
" return u\n",
|
||||
"end\n",
|
||||
"\n",
|
||||
"u = zeros(ndim*nnodes)\n",
|
||||
"solve!(u, free_dofs)\n",
|
||||
"u = reshape(u, ndim, nnodes)\n",
|
||||
"u"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"elapsed time: 4.541729719 seconds\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"4.541729719"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"toc()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Julia 0.3.8",
|
||||
"language": "julia",
|
||||
"name": "julia-0.3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "julia",
|
||||
"version": "0.3.8"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
Reference in New Issue
Block a user