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MIT License
Copyright (c) 2022 LIYu
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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# Topology-Optimization-in-Julia
Julia Codes for Structural Topology Optimization Design
## Codes
A personal [Julia](https://epubs.siam.org/doi/10.1137/141000671) code is given mainly based on a compact and efficient Matlab implementation **top99neo** of compliance topology optimization (TO) for 2D continua[^1], which is a v3.0 version of the celebrated **top99** Matlab code developed by Sigmund[^2] and **top88** by its heir[^3].
Assemble just one half of the sysmetric stiffness matrix, thus substantial speedups are acheived.
`top_oc/` and `top_mma/` contain corresponding files related to the TO with OC and MMA algorithms, respectively.
Running codes could be tried out as:
```julia
include("./top99neo_mma.jl")
setup = SetUp() # problem setup
mat = Mat() # material property
disfeature = DiscretizationFeature(setup, mat) # model discretization
load = LoadsSupportsBCs(setup, disfeature) # boudary conditions
ini = Initialization(setup, disfeature, mat) # initial conditions
filter = Filter(setup) # filtering
xPhys, opt_hist, vf_hist, anim = Optimization(setup, mat, load, filter, ini, disfeature) # optimization process
gif(anim, "./res/des_hist.gif", fps=20) # design result visulization
```
## Results
A benchmark MBB example is presented. TO design results are saved in `./res/` folder and a evolution history is shown as below.
<!-- ![TO design evolution with MMA](./top_mma/res/des_hist.gif) -->
<div align=center>
<img src=./top_mma/res/des_hist.gif width="500">
👍 💯
</div>
## Packages
Run [the Julia REPL](https://docs.julialang.org/en/v1/stdlib/REPL/), enter `]` to bring up Julia's [package manager](https://docs.julialang.org/en/v1/stdlib/Pkg/),
and add the listed packages:
> julia> ]
>
> (@v1.7) pkg> add #pkg_name#
- Scientific computing
- LinearAlgebra
- SparseArrays
- Statistics : mean
- Image process
- ImageFiltering: imfilter
- Modelling
- [Gmsh](https://onlinelibrary.wiley.com/doi/10.1002/nme.2579)
- FEM
- [Gridap](https://joss.theoj.org/papers/10.21105/joss.02520)
- AD
- [ForwardDiff](https://arxiv.org/abs/1607.07892)
- [Zygote](https://arxiv.org/abs/1810.07951)
- Optimization
- [NLopt](https://github.com/stevengj/nlopt)
- Visualization
- Plots
## TODOs
- [x] [`top99neo.jl`](./top_oc/top99neo.jl)
top99neo.m rewritten in Julia
- [x] [`MMA.jl`](./top_mma/MMA.jl)
MMA algorithm (mmasub.m + subsolve.m) rewritten in Julia
- [x] [`top99neo_mma.jl`](./top_mma/top99neo_mma.jl)
2D code (top99neo + MMA) written in Julia
- [ ] `top99neo_AD.jl`
Sensitivity Analysis using Automatic Differentiation
- [ ] `top99neo_NLopt.jl`
Optimization solved with NLopt
- [ ] `top3D.jl`
3D code (top3D125 + MMA) written in Julia
- [ ] `top_flux.jl`
Combine TO with machine learning through [Flux](https://arxiv.org/abs/1811.01457)
## Acknowledgements
- [TopOpt Group](https://www.topopt.mek.dtu.dk/) 🇩🇰
Matlab codes for topology optimization
- v1.0 [**top99.m**](https://www.topopt.mek.dtu.dk/Apps-and-software/A-99-line-topology-optimization-code-written-in-MATLAB)
Educatianal TO enlightenment for every beginners
- v2.0 [**top88.m**](https://www.topopt.mek.dtu.dk/Apps-and-software/Efficient-topology-optimization-in-MATLAB)
Loop vectorization and memory preallocation
- v3.0 [**top99neo.m**](https://www.topopt.mek.dtu.dk/Apps-and-software/New-99-line-topology-optimization-code-written-in-MATLAB)
Half matrix assembly operation, filter implementation and volume-preserving density projection
- Prof. [Krister Svanberg](https://people.kth.se/~krille/) 🇸🇪
- Freely available [Matlab code](http://www.smoptit.se/) for CCSA/MMA[^4] and GCMMA
## Author ©️
📧 Please contact to liyu_npu@outlook.com
⚠️ Disclaimer: The author reserves all rights but does not guarantee that the code is free from errors. Furthermore, we shall not be liable in any event.
| Name | Info. | Hobby | Food |
| ----- | :--------: | :-----------: | :-------: |
| Yu Li | 🇨🇳 🎓 1️⃣9️⃣9️⃣0️⃣ ♑ | 🎧 🃏 🎮 🏀 🏊 🏃 🚴‍♂️ | 🍦 🦞 🍣 🌽 🍌 |
```bibtex
@misc{Yu2022,
author = {Yu Li},
title = {Topology Optimization in Julia},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/yuloveyet/Topology-Optimization-in-Julia}},
}
```
**References**
[^1]: Ferrari, F., & Sigmund, O. (2020). A new generation 99 line Matlab code for compliance topology optimization and its extension to 3D. Structural and Multidisciplinary Optimization, 62(4), 2211-2228.
[^2]:Sigmund, O. (2001). A 99 line topology optimization code written in Matlab. Structural and multidisciplinary optimization, 21(2), 120-127.
[^3]:Andreassen, E., Clausen, A., Schevenels, M., Lazarov, B. S., & Sigmund, O. (2011). Efficient topology optimization in MATLAB using 88 lines of code. Structural and Multidisciplinary Optimization, 43(1), 1-16.
[^4]: Svanberg, K. (2002). A class of globally convergent optimization methods based on conservative convex separable approximations. SIAM journal on optimization, 12(2), 555-573.

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########################################################################################################
### MMA-Julia (1.7.1)
########################################################################################################
### MODIFIED BY YULI 2022/5/4
########################################################################################################
export mmasub
# Loading modules
using LinearAlgebra
using SparseArrays
########################################################################################################
### MMA FUNCTIONS ###
########################################################################################################
# Function for the MMA sub problem
function mmasub(m::Int, n::Int, iter::Int, xval::Array{Float64}, xmin::Array{Float64},
xmax::Array{Float64}, xold1::Array{Float64}, xold2::Array{Float64}, f0val,
df0dx::Array{Float64}, df0dx2:: Array{Float64},
fval::Float64, dfdx::Array{Float64}, dfdx2::Array{Float64},
low::Array{Float64}, upp::Array{Float64}, a0::Float64,
a::Array{Float64}, c::Array{Float64}, d::Array{Float64})
# """
# This function mmasub performs one MMA-iteration, aimed at solving the nonlinear programming problem:
#
# Minimize f_0(x) + a_0*z + sum( c_i*y_i + 0.5*d_i*(y_i)^2 )
# subject to f_i(x) - a_i*z - y_i < = 0, i = 1, ..., m
# xmin_j < = x_j < = xmax_j, j = 1, ..., n
# z > = 0, y_i > = 0, i = 1, ..., m
# INPUT:
#
# m = The number of general constraints.
# n = The number of variables x_j.
# iter = Current iteration number ( =1 the first time mmasub is called).
# xval = Column vector with the current values of the variables x_j.
# xmin = Column vector with the lower bounds for the variables x_j.
# xmax = Column vector with the upper bounds for the variables x_j.
# xold1 = xval, one iteration ago (provided that iter>1).
# xold2 = xval, two iterations ago (provided that iter>2).
# f0val = The value of the objective function f_0 at xval.
# df0dx = Column vector with the derivatives of the objective function
# f_0 with respect to the variables x_j, calculated at xval.
# fval = Column vector with the values of the constraint functions f_i, calculated at xval.
# dfdx = (m x n)-matrix with the derivatives of the constraint functions
# f_i with respect to the variables x_j, calculated at xval.
# dfdx(i,j) = the derivative of f_i with respect to x_j.
# low = Column vector with the lower asymptotes from the previous
# iteration (provided that iter>1).
# upp = Column vector with the upper asymptotes from the previous
# iteration (provided that iter>1).
# a0 = The constants a_0 in the term a_0*z.
# a = Column vector with the constants a_i in the terms a_i*z.
# c = Column vector with the constants c_i in the terms c_i*y_i.
# d = Column vector with the constants d_i in the terms 0.5*d_i*(y_i)^2.
#
# OUTPUT:
#
# xmma = Column vector with the optimal values of the variables x_j
# in the current MMA subproblem.
# ymma = Column vector with the optimal values of the variables y_i
# in the current MMA subproblem.
# zmma = Scalar with the optimal value of the variable z
# in the current MMA subproblem.
# lam = Lagrange multipliers for the m general MMA constraints.
# xsi = Lagrange multipliers for the n constraints alfa_j - x_j < = 0.
# eta = Lagrange multipliers for the n constraints x_j - beta_j < = 0.
# mu = Lagrange multipliers for the m constraints -y_i < = 0.
# zet = Lagrange multiplier for the single constraint -z < = 0.
# s = Slack variables for the m general MMA constraints.
# low = Column vector with the lower asymptotes, calculated and used
# in the current MMA subproblem.
# upp = Column vector with the upper asymptotes, calculated and used
# in the current MMA subproblem.
# """
epsimin = sqrt(m + n) * 10^(-9)
feps = 0.000001
asyinit = 0.5
asyincr = 1.05
asydecr = 0.65
albefa = 0.1
een = ones(n, 1)
zeron = zeros(n, 1)
if iter <= 2
low = xval - asyinit .* (xmax - xmin)
upp = xval + asyinit .* (xmax - xmin)
else
zzz = (xval - xold1) .* (xold1 - xold2)
factor = copy(een)
factor[findall(zzz .> 0.0)] .= asyincr
factor[findall(zzz .< 0.0)] .= asydecr
low = xval - factor .* (xold1 - low)
upp = xval + factor .* (upp - xold1)
lowmin = xval - 10.0 .* (xmax - xmin)
lowmax = xval - 0.01 .* (xmax - xmin)
uppmin = xval + 0.01 .* (xmax - xmin)
uppmax = xval + 10.0 .* (xmax - xmin)
low = max.(low, lowmin)
low = min.(low, lowmax)
upp = min.(upp, uppmax)
upp = max.(upp, uppmin)
end
zzz = low + albefa .* (xval - low)
alfa = max.(zzz, xmin)
zzz = upp - albefa .* (upp - xval)
beta = min.(zzz, xmax)
ux1 = upp - xval
ux2 = ux1 .* ux1
ux3 = ux2 .* ux1
xl1 = xval - low
xl2 = xl1 .* xl1
xl3 = xl2 .* xl1
ul1 = upp - low
ulinv1 = een ./ ul1
uxinv1 = een ./ ux1
xlinv1 = een ./ xl1
uxinv3 = een ./ ux3
xlinv3 = een ./ xl3
diap = (ux3 .* xl1) ./ (2 * ul1)
diaq = (ux1 .* xl3) ./ (2 * ul1)
p0 = copy(zeron)
q0 = copy(zeron)
p0[findall(df0dx .> 0.0)] = df0dx[findall(df0dx .> 0.0)]
p0 = p0 + 0.001 .* abs.(df0dx) + feps * ulinv1
p0 = p0 .* ux2
# q0 = zeron
q0[findall(df0dx .< 0.0)] = -df0dx[findall(df0dx .< 0.0)]
q0 = q0 + 0.001 * abs.(df0dx) + feps * ulinv1
q0 = q0 .* xl2
dg0dx2 = 2 * (p0 ./ ux3 + q0 ./ xl3)
del0 = df0dx2 - dg0dx2
delpos0 = copy(zeron)
delpos0[findall(del0 .> 0.0)] = del0[findall(del0 .> 0.0)]
p0 = p0 + delpos0 .* diap
q0 = q0 + delpos0 .* diaq
P = spzeros(m, n)
P[findall(dfdx .> 0.0)] = dfdx[findall(dfdx .> 0.0)]
# P = P * diag(ux2);
# P = P * spdiagm(n, n, 0 => ux2)
P = P * sparse(Diagonal(ux2[:]))
Q = spzeros(m, n)
Q[findall(dfdx .< 0.0)] = -dfdx[findall(dfdx .< 0.0)]
# Q = Q * diag(xl2);
# Q = Q * spdiagm(n, n, 0 => xl2)
Q = Q * sparse(Diagonal(xl2[:]))
# dgdx2 = 2.0*(P*diag(uxinv3) + Q*diag(xlinv3));
# dgdx2 = P * spdiagm(n, n, 0 => uxinv3) + Q * spdiagm(n, n, 0 => xlinv3)
dgdx2 = P * sparse(Diagonal(uxinv3[:])) + Q * sparse(Diagonal(xlinv3[:]))
dgdx2 = 2.0 * dgdx2
del = dfdx2 - dgdx2
delpos = zeros(m, n)
delpos[findall(del .> 0.0)] = del[findall(del .> 0.0)]
# P = P + delpos*diag(diap);
# P = P + delpos * spdiagm(n, n, 0 => diap)
P = P + delpos * sparse(Diagonal(diap[:]))
# Q = Q + delpos*diag(diaq);
# Q = Q + delpos * spdiagm(n, n, 0 => diap)
Q = Q + delpos * sparse(Diagonal(diap[:]))
b = P * uxinv1 + Q * xlinv1 .- fval
# b = P * uxinv + Q * xlinv - fval
# Solving the subproblem by a primal-dual Newton method
xmma, ymma, zmma, lam, xsi, eta, mu, zet, s =
subsolv(m, n, epsimin, low, upp, alfa, beta, p0, q0, P, Q, a0, a, b, c, d)
# Return values
return xmma, ymma, zmma, lam, xsi, eta, mu, zet, s, low, upp
end
# Function for solving the subproblem (can be used for MMA and GCMMA)
function subsolv(m::Int, n::Int, epsimin::Float64, low::Array{Float64}, upp::Array{Float64},
alfa::Array{Float64}, beta::Array{Float64}, p0::Array{Float64}, q0::Array{Float64},
P::Array{Float64}, Q::Array{Float64}, a0::Float64, a::Array{Float64}, b::Array{Float64},
c::Array{Float64}, d::Array{Float64})
# """
# This function subsolv solves the MMA subproblem:
#
# minimize SUM[p0j/(uppj-xj) + q0j/(xj-lowj)] + a0*z + SUM[ci*yi + 0.5*di*(yi)^2],
#
# subject to SUM[pij/(uppj-xj) + qij/(xj-lowj)] - ai*z - yi < = bi,
# alfaj < = xj < = betaj, yi > = 0, z > = 0.
#
# Input : m, n, low, upp, alfa, beta, p0, q0, P, Q, a0, a, b, c, d.
# Output: xmma, ymma, zmma, slack variables and Lagrange multiplers.
# """
een = ones(Float64, n)
eem = ones(Float64, m)
epsi = 1.0
epsvecn = epsi .* een
epsvecm = epsi .* eem
x = 0.5 .* (alfa + beta)
y = copy(eem)
z = 1.0
lam = copy(eem)
xsi = een ./ (x - alfa)
xsi = max.(xsi, een)
eta = een ./ (beta - x)
eta = max.(eta, een)
mu = max.(eem, 0.5 .* c)
zet = 1.0
s = copy(eem)
itera = 0
while epsi > epsimin # Start while epsi>epsimin
epsvecn = epsi .* een
epsvecm = epsi .* eem
ux1 = upp - x
xl1 = x - low
ux2 = ux1 .* ux1
xl2 = xl1 .* xl1
uxinv1 = een ./ ux1
xlinv1 = een ./ xl1
plam = p0 + (P)' * lam
qlam = q0 + (Q)' * lam
gvec = P * uxinv1 + Q * xlinv1
dpsidx = (plam ./ ux2) - (qlam ./ xl2)
rex = dpsidx - xsi + eta
rey = c + d .* y - mu - lam
rez = a0 .- zet .- (a)' * lam
relam = gvec - a .* z - y + s - b
rexsi = xsi .* (x - alfa) - epsvecn
reeta = eta .* (beta - x) - epsvecn
remu = mu .* y - epsvecm
rezet = zet * z - epsi
res = lam .* s - epsvecm
residu1 = [rex' rey' rez]'
residu2 = [relam' rexsi' reeta' remu' rezet res']'
residu = [residu1' residu2']'
residunorm = norm(residu, 2)
residumax = maximum(abs.(residu))
ittt = 0
while (residumax > 0.9 * epsi) && (ittt < 100) # Start while (residumax>0.9*epsi) and (ittt<100)
ittt = ittt + 1
itera = itera + 1
if ittt == 100
println("max inner iter reached")
end
ux1 = upp - x
xl1 = x - low
ux2 = ux1 .* ux1
xl2 = xl1 .* xl1
ux3 = ux1 .* ux2
xl3 = xl1 .* xl2
uxinv1 = een ./ ux1
xlinv1 = een ./ xl1
uxinv2 = een ./ ux2
xlinv2 = een ./ xl2
plam = p0 + (P)' * lam
qlam = q0 + (Q)' * lam
gvec = P * uxinv1 + Q * xlinv1
# GG = P .* transpose(uxinv2) - Q .* transpose(xlinv2)
# GG = P .* spdiagm(n,n, 0 => uxinv2) - Q .* spdiagm(n, n, 0 => xlinv2)
GG = P * sparse(Diagonal(uxinv2[:])) - Q * sparse(Diagonal(xlinv2[:]))
dpsidx = (plam ./ ux2) - (qlam ./ xl2)
delx = dpsidx - epsvecn ./ (x - alfa) + epsvecn ./ (beta - x)
dely = c + d .* y - lam - epsvecm ./ y
delz = a0 .- transpose(a) * lam .- epsi / z
dellam = gvec - a .* z - y - b + epsvecm ./ lam
diagx = plam ./ ux3 + qlam ./ xl3
diagx = 2.0 .* diagx + xsi ./ (x - alfa) + eta ./ (beta - x)
diagxinv = een ./ diagx
diagy = d + mu ./ y
diagyinv = eem ./ diagy
diaglam = s ./ lam
diaglamyi = diaglam + diagyinv
if m < n # Start if m < n
blam = dellam .+ dely ./ diagy .- GG * (delx ./ diagx)
bb = [blam' delz]'
# Alam = spdiagm(0 => diaglamyi) + (GG .* transpose(diagxinv)) * transpose(GG)
# Alam = spdiagm(m, m, 0 => diaglamyi) + (GG .* spdiagm(n, n, 0 => diagxinv)) * transpose(GG)
Alam = sparse(Diagonal(diaglamyi[:])) + (GG * sparse(Diagonal(diagxinv[:]))) * (GG)'
AA = [Alam a
a' -zet/z]
solut = AA \ bb
dlam = solut[1:m]
dz = solut[m+1]
dx = -delx ./ diagx - ((GG)' * dlam) ./ diagx
else
diaglamyiinv = eem ./ diaglamyi
dellamyi = dellam + dely ./ diagy
# Axx = spdiagm(0 => diagx) + (transpose(GG) .* transpose(diaglamyiinv)) * GG
# Axx = spdiagm(n,n, 0 => diagx) + (transpose(GG) .* spdiagm(m, m, 0 => diaglamyiinv)) * GG
Axx = sparse(Diagonal(diagx[:])) + ((GG)' .* sparse(Diagonal(diaglamyiinv[:]))) * GG
azz = zet / z + (a)' * (a ./ diaglamyi)
axz = -(GG)' * (a ./ diaglamyi)
bx = delx + (GG)' * (dellamyi ./ diaglamyi)
bz = delz - (a)' * (dellamyi ./ diaglamyi)
# AAr1 = [Axx axz]
# AAr2 = [transpose(axz) azz]
# AA = [AAr1; AAr2]
AA = [Axx axz
axz' azz]
bb = [-bx' -bz]'
solut = AA \ bb
dx = solut[1:n]
dz = solut[n+1]
dlam = (GG * dx) ./ diaglamyi - dz .* (a ./ diaglamyi) + dellamyi ./ diaglamyi
end # End if m<n
dy = -dely ./ diagy + dlam ./ diagy
dxsi = -xsi .+ epsvecn ./ (x - alfa) - (xsi .* dx) ./ (x - alfa)
deta = -eta .+ epsvecn ./ (beta - x) + (eta .* dx) ./ (beta - x)
dmu = -mu .+ epsvecm ./ y - (mu .* dy) ./ y
dzet = -zet .+ epsi / z - zet * dz / z
ds = -s .+ epsvecm ./ lam - (s .* dlam) ./ lam
xx = [y' z lam' xsi' eta' mu' zet s']'
dxx = [dy' dz dlam' dxsi' deta' dmu' dzet ds']'
#
stepxx = -1.01 .* dxx ./ xx
stmxx = maximum(stepxx)
stepalfa = -1.01 .* dx ./ (x - alfa)
stmalfa = maximum(stepalfa)
stepbeta = 1.01 .* dx ./ (beta - x)
stmbeta = maximum(stepbeta)
stmalbe = max.(stmalfa, stmbeta)
stmalbexx = max.(stmalbe, stmxx)
stminv = max.(stmalbexx, 1.0)
steg = 1.0 / stminv
#
xold = copy(x)
yold = copy(y)
zold = copy(z)
lamold = copy(lam)
xsiold = copy(xsi)
etaold = copy(eta)
muold = copy(mu)
zetold = copy(zet)
sold = copy(s)
#
itto = 0
resinew = 2.0 * residunorm
# Start: while (resinew>residunorm) and (itto<50)
while (resinew > residunorm) && (itto < 50)
itto = itto + 1
x = xold + steg .* dx
y = yold + steg .* dy
z = zold + steg * dz
lam = lamold + steg .* dlam
xsi = xsiold + steg .* dxsi
eta = etaold + steg .* deta
mu = muold + steg .* dmu
zet = zetold + steg * dzet
s = sold + steg .* ds
ux1 = upp - x
xl1 = x - low
ux2 = ux1 .* ux1
xl2 = xl1 .* xl1
uxinv1 = een ./ ux1
xlinv1 = een ./ xl1
plam = p0 + (P)' * lam
qlam = q0 + (Q)' * lam
gvec = P * uxinv1 + Q * xlinv1
dpsidx = plam ./ ux2 - qlam ./ xl2
rex = dpsidx - xsi + eta
rey = c .+ d .* y - mu - lam
rez = a0 - zet .- (a)' * lam
relam = gvec - a .* z - y + s - b
rexsi = xsi .* (x - alfa) - epsvecn
reeta = eta .* (beta - x) - epsvecn
remu = mu .* y - epsvecm
rezet = zet * z - epsi
res = lam .* s - epsvecm
residu1 = [rex' rey' rez]'
residu2 = [relam' rexsi' reeta' remu' rezet res']'
residu = [residu1' residu2']'
resinew = norm(residu, 2)
steg = steg / 2.0
end # End: while (resinew>residunorm) and (itto<50)
residunorm = copy(resinew)
residumax = maximum(abs.(residu))
steg = 2.0 * steg
end # End: while (residumax>0.9*epsi) and (ittt<200)
epsi = 0.1 * epsi
end # End: while epsi>epsimin
xmma = copy(x)
ymma = copy(y)
zmma = copy(z)
lamma = lam
xsimma = xsi
etamma = eta
mumma = mu
zetmma = zet
smma = s
# Return values
return xmma, ymma, zmma, lamma, xsimma, etamma, mumma, zetmma, smma
end

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# module TopOpt99neo\n",
"\n",
"# include(\"FastConv.jl/src/FastConv.jl\")\n",
"# import .FastConv.fastconv\n",
"\n",
"using LinearAlgebra, SparseArrays\n",
"using Plots\n",
"# : heatmap, savefig, @animate\n",
"using ImageFiltering: imfilter\n",
"using Statistics: mean\n",
"\n",
"using BenchmarkTools\n",
"using Zygote\n",
"\n",
"BLAS.set_num_threads(1)\n",
"\n",
"# abstract type top99neo end\n",
"\n",
"include(\"utils.jl\")\n",
"include(\"MMA.jl\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mutable struct SetUp\n",
" nelx::Int\n",
" nely::Int\n",
"\n",
" eta::Float64\n",
" beta::Int\n",
" betaCnt::NTuple{4,Int}\n",
"\n",
" move::Float64\n",
" maxit::Int\n",
" maxchang::Float64\n",
"\n",
" pasS::Array{Int}\n",
" pasV::Array{Int}\n",
"\n",
" function SetUp()\n",
" nelx = 30\n",
" nely = 10\n",
" # volfrac = 0.3\n",
" maxit = 300\n",
" move = 0.1\n",
" beta = 2\n",
" eta = 0.5\n",
" maxchang = 1e-6\n",
" # penalCnt = {maxit+1, 3, 25, 0.25}\n",
" betaCnt = (1, 32, 10, 2) # continuation scheme on beta parCont = { istart,maxPar,steps,deltaPar }\n",
" \n",
" # elNrs = reshape(1:nely*nelx, nely, nelx)\n",
" # a1 = elNrs[Int.(nely/4:nely/2), Int.(nelx/4:nelx/2)]\n",
" # pasS, pasV = Array([]), a1[:]\n",
" pasS, pasV = Array([]), Array([])\n",
" \n",
" new(nelx, nely, eta, beta, betaCnt, move, maxit, maxchang, pasS, pasV)\n",
" end\n",
"\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"setup = SetUp()\n",
"# setup.nelx = 20\n",
"# setup.nely = 10\n",
"# setup.maxit = 100\n",
"# typeof(setup.nodeNrs)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"mutable struct Mat\n",
" E0::Float64\n",
" Emin::Float64\n",
" ν::Float64\n",
" penal::Float64\n",
" volfrac::Float64\n",
"\n",
" function Mat()\n",
" E0 = 1\n",
" Emin = 1e-9\n",
" ν = 0.3\n",
" penal = 3.0\n",
" volfrac = 0.3\n",
" new(E0, Emin, ν, penal, volfrac)\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mat = Mat()\n",
"# mat.volfrac"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mutable struct DiscretizationFeature\n",
" nEl::Int\n",
" nodeNrs::Array{Int,2}\n",
" nDof::Int\n",
" act\n",
"\n",
" cMat::Array{Int}\n",
" Iar::Array{Int}\n",
" Ke::Array{Float64,1}\n",
" Ke0::Array{Float64,2}\n",
" \n",
" function DiscretizationFeature(setup::SetUp, mat::Mat)\n",
" nelx = setup.nelx\n",
" nely = setup.nely\n",
" pasS, pasV = setup.pasS, setup.pasV\n",
" ν = mat.ν\n",
" \n",
" nEl = nely * nelx\n",
" nodeNrs = reshape(1:(1+nelx)*(1+nely), nely + 1, nelx + 1)\n",
" nDof = (nely + 1) * (nelx + 1) * 2\n",
" act = setdiff(collect(1:nEl), union(pasS, pasV))\n",
" \n",
" cVec = reshape(2 * nodeNrs[1:end-1, 1:end-1] .+ 1, nEl, 1)\n",
" # cMat = cVec + Int.([0 1 2 * nely .+ [2 3 0 1] -2 -1])\n",
" cMat = Int.(repeat(cVec, 1, 8) + repeat([0 1 2 * nely .+ [2 3 0 1] -2 -1], nelx * nely, 1))\n",
" # nDof = (nely + 1) * (nelx + 1) * 2\n",
" FuckRow = [1 2 3 4 5 6 7 8]\n",
" sI::Array{Int}, sII::Array{Int} = copy(FuckRow), fill(1, 1, 8)\n",
" for j in 2:8\n",
" sI = cat(sI, FuckRow[j:8]'; dims=2)\n",
" # sI = append!(sI, convert(Array{Int},[j:8]))\n",
" # sII = cat(2, sII, repmat(j, 1, 8 - j + 1))\n",
" sII = cat(sII, fill(j, 1, 8 - j + 1); dims=2)\n",
" end\n",
" iK::Array{Int,2}, jK::Array{Int,2} = cMat[:, sI][:, 1, :]', cMat[:, sII][:, 1, :]'\n",
" Iar = sort([iK[:] jK[:]]; dims=2, rev=true) # comma is a newline\n",
" # iK[:], jK[:] .= 0.0, 0.0\n",
" c1 = [12, 3, -6, -3, -6, -3, 0, 3, 12, 3, 0, -3, -6, -3, -6, 12, -3, 0, -3, -6, 3, 12, 3, -6, 3, -6, 12, 3, -6, -3, 12, 3, 0, 12, -3, 12]\n",
" c2 = [-4, 3, -2, 9, 2, -3, 4, -9, -4, -9, 4, -3, 2, 9, -2, -4, -3, 4, 9, 2, 3, -4, -9, -2, 3, 2, -4, 3, -2, 9, -4, -9, 4, -4, -3, -4]\n",
" Ke = 1 / (1 - ν^2) / 24 .* (c1 .+ ν .* c2) # half-KE vector\n",
" # full KE\n",
" # Ke0::Array{Float64} = zeros(8, 8)\n",
" # start_id, end_id = 1, 8\n",
" # for i in 1:8\n",
" # Ke0[i:8, i] = Ke[start_id:end_id]\n",
" # start_id, end_id = end_id + 1, 2 * end_id - start_id\n",
" # end\n",
" \n",
" Ke0::Array{Float64} = zeros(8, 8)\n",
" # Index::Array{Int} = [sI' sII']\n",
" # Ke0[sI, sII] = Ke\n",
" Index = findall(isequal(1), tril(ones(8, 8)))\n",
" Ke0[Index] = Ke'\n",
" # Ke0 = reshape(Ke0, 8, 8)\n",
" Ke0 = Ke0 + Ke0' - diagm(diag(Ke0))\n",
" new(nEl, nodeNrs, nDof, act, cMat, Iar, Ke, Ke0)\n",
" end\n",
"end\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"disfeature = DiscretizationFeature(setup, mat)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mutable struct LoadsSupportsBCs\n",
" # setup::SetUp\n",
" lcDof::Array{Int}\n",
" F\n",
" free::Array{Int}\n",
" fixed::Array{Int}\n",
"\n",
" function LoadsSupportsBCs(setup::SetUp, disfeature::DiscretizationFeature)\n",
" nelx = setup.nelx\n",
" nely = setup.nely\n",
" nodeNrs = disfeature.nodeNrs\n",
" nDof = disfeature.nDof\n",
" \n",
" load_position::Symbol = :half_MBB\n",
" if load_position == :half_MBB\n",
" load_nodey, load_nodex = 1, 1\n",
" # fixed = union([1:2:2*(nely+1)], 2 * nodeNrs[nely+1, nelx+1])\n",
" fixed = union(collect(1:2:2*(nely+1)), 2 * nodeNrs[end, end])\n",
" elseif load_position == :cantilever\n",
" load_nodey = nely + 1\n",
" load_nodex = nelx / 2 + 1\n",
" fixed = 1:2*(nely+1)\n",
" end\n",
" \n",
" F = spzeros(nDof)\n",
" \n",
" load_type::Symbol = :pin\n",
" if load_type == :pin # 1 point\n",
" lcDof = collect(2 * nodeNrs[load_nodey, load_nodex])\n",
" F[2, 1] = -1.0\n",
" elseif load_type == :points # 5 points\n",
" lcDof = collect(2 * nodeNrs[load_nodey, load_nodex], nodeNrs[load_nodey, load_nodex-1], nodeNrs[load_nodey, load_nodex-2], nodeNrs[load_nodey, load_nodex+1], nodeNrs[load_nodey, load_nodex+2])\n",
" F[lcDof', ones(length(lcDof'))] .= -1.0\n",
" elseif load_type == :line\n",
" lcDof = [2:2*(nely+1):nDof]\n",
" F = spzeros(nDof, 1)\n",
" # F = sparse(lcDof', ones(length(lcDof')), -1.0)\n",
" end\n",
" all = collect(1:nDof)\n",
" free = setdiff(all, fixed)\n",
" \n",
" new(lcDof, F, free, fixed)\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"load = LoadsSupportsBCs(setup, disfeature)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mutable struct Initialization \n",
" # setup::SetUp\n",
" x::Array{Float64}\n",
" xPhys::Array{Float64}\n",
" xOld::Array{Float64}\n",
" ch::Float64\n",
" loop::Int\n",
" # U::Array{Float64}\n",
" # dsK::Array{Float64}\n",
" # dV::Array{Float64}\n",
"\n",
" function Initialization(setup::SetUp, disfeature::DiscretizationFeature, mat::Mat)\n",
" pasV = setup.pasV\n",
" pasS = setup.pasS\n",
" act = disfeature.act\n",
" volfrac = mat.volfrac\n",
" nEl = disfeature.nEl\n",
" nDof = disfeature.nDof\n",
" # column vectors\n",
" x = zeros(nEl, 1)\n",
" # dV[act, 1] .= 1.0 / nEl / volfrac\n",
" x[act] .= volfrac\n",
" x[pasS] .= 1.0\n",
" xPhys, xOld, ch, loop = copy(x), ones(nEl, 1), 1.0, 0\n",
" # x̅ x̃\n",
" \n",
" new(x, xPhys, xOld, ch, loop)\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ini = Initialization(setup, disfeature, mat)\n",
"# typeof(ini.x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mutable struct Filter \n",
" rmin::Float64\n",
" ft::Int\n",
" h::Array{Float64}\n",
" Hs::Array{Float64}\n",
" dHs::Array{Float64}\n",
"\n",
" function Filter(setup::SetUp)\n",
" nelx = setup.nelx\n",
" nely = setup.nely\n",
" # bcF = setup.bcF\n",
" rmin = 6.5\n",
" ft = 3\n",
" dy, dx = meshgrid(-ceil(rmin)+1:ceil(rmin)-1, -ceil(rmin)+1:ceil(rmin)-1)\n",
" h = max.(0, rmin .- sqrt.(dx .^ 2 + dy .^ 2))\n",
" Hs = imfilter(ones(nely, nelx), h, \"symmetric\")\n",
" dHs = Hs\n",
" new(rmin, ft, h, Hs, dHs)\n",
" end\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"\n",
"\n",
"filter = Filter(setup)\n",
"# filter.Hs"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"FiniteElementAnalasys (generic function with 1 method)"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"function FiniteElementAnalasys(mat::Mat, disfeature::DiscretizationFeature, load::LoadsSupportsBCs, xPhys::Array{Float64})\n",
" nEl, nDof, Iar, Ke = disfeature.nEl, disfeature.nDof, disfeature.Iar, disfeature.Ke\n",
" act = disfeature.act\n",
" Emin, penal, E0 = mat.Emin, mat.penal, mat.E0\n",
" F, free = load.F, load.free\n",
"\n",
" sK = Emin .+ xPhys .^ penal .* (E0 - Emin)\n",
" sK = reshape(Ke[:] * sK', length(Ke) * nEl, 1)\n",
" K = sparse(Iar[:, 1], Iar[:, 2], vec(sK), nDof, nDof)\n",
" U = zeros(nDof)\n",
" #~\n",
" # U[free] = cholesky(Symmetric(K[free, free], :L), check=false) \\ F[free]\n",
" K0 = K + K' - diagm(diag(K))\n",
" U[free] = K0[free, free] \\ F[free]\n",
" #~\n",
" Obj = F' * U\n",
" Vf = mean(xPhys[act])\n",
" return U, Obj, Vf\n",
"end\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1-element Vector{Float64}:\n",
" 4558.123953305818"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"U, C, vf = FiniteElementAnalasys(mat, disfeature, load, xval)\n",
"C"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"function SensitivityAnalasys(setup::SetUp, filter::Filter, mat::Mat, disfeature::DiscretizationFeature, \n",
" U::Array{Float64}, xPhys::Array{Float64})\n",
" nelx, nely, act = setup.nelx, setup.nely, disfeature.act\n",
" dHs, h = filter.dHs, filter.h\n",
" E0, Emin, penal = mat.E0, mat.Emin, mat.penal\n",
" nEl, cMat, Ke0 = disfeature.nEl, disfeature.cMat, disfeature.Ke0\n",
" act = disfeature.act\n",
"\n",
" dsK, dV = zeros(nEl, 1), zeros(nEl, 1)\n",
" dV[act, 1] .= 1.0 / length(act)\n",
" dsK[act] = -penal * (E0 - Emin) .* xPhys[act] .^ (penal - 1)\n",
"\n",
" dc = dsK .* sum((U[cMat] * Ke0) .* U[cMat], dims=2)\n",
" dc = imfilter(reshape(dc, nely, nelx) ./ dHs, h, \"symmetric\")\n",
" dV0 = imfilter(reshape(dV, nely, nelx) ./ dHs, h, \"symmetric\")\n",
"\n",
" return reshape(dc, nEl, 1), reshape(dV0, nEl, 1)\n",
"end\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"function AutomaticDifferentiation(mat::Mat, disfeature::DiscretizationFeature, load::LoadsSupportsBCs, xPhys::Array{Float64})\n",
" # fea = FiniteElementAnalasys()\n",
" dc_AD = gradient(x -> FiniteElementAnalasys(mat, disfeature, load, x), xPhys)\n",
" # dv_AD = gradient(x)\n",
"\n",
" return dc_AD\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"xval = ini.x\n",
"dc_AD = AutomaticDifferentiation(mat, disfeature, load, xval)\n",
"typeof(dc_AD)\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"typeof(dc_AD)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"function Visualization(setup::SetUp, x::Array{Float64}, loop::Int)\n",
" nelx, nely = setup.nelx, setup.nely\n",
" # cmap = cgrad(:Blues_9, rev=false)\n",
" plot = heatmap(reshape(x, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400,nely/nelx*400) , legend=:none, display_type=:gui)\n",
" display(plot)\n",
" savefig(plot, \"./top/res_$loop.pdf\")\n",
" # PLOT FINAL DESIGN\n",
" # heatmap(1.0 .- x[end:-1:1, :], yaxis=false, xaxis=false, legend=:none,color=:greys, grid=false, border=nothing, aspect_ratio=:equal)\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"function Optimization(setup::SetUp, mat::Mat, load::LoadsSupportsBCs,\n",
" filter::Filter, ini::Initialization, disfeature::DiscretizationFeature)\n",
" ch, loop = ini.ch, ini.loop\n",
" x, xPhys, xOld = ini.x, ini.xPhys, ini.xOld\n",
"\n",
" maxit, maxchang = setup.maxit, setup.maxchang\n",
" nely, nelx = setup.nely, setup.nelx\n",
" eta, beta, betaCnt = setup.eta, setup.beta, setup.betaCnt\n",
" volfrac, penal, nEl = mat.volfrac, mat.penal, disfeature.nEl\n",
" act = disfeature.act\n",
"\n",
" Hs, h, ft = filter.Hs, filter.h, filter.ft\n",
"\n",
" opt_hist = []\n",
" vf_hist = []\n",
"\n",
" ### Initiation of MMA ###\n",
" xval = copy(x[act])\n",
" xold1 = copy(xval)\n",
" xold2 = copy(xold1)\n",
"\n",
" low = copy(xval)\n",
" upp = copy(low)\n",
" #########################\n",
"\n",
" anim = @animate while ch > maxchang && loop < maxit || beta < betaCnt[2]\n",
" @time begin\n",
" loop = loop + 1\n",
" # COMPUTE PHYSICAL DENSITY FIELD \n",
" xTilde = imfilter(reshape(x, nely, nelx), h, \"symmetric\") ./ Hs\n",
" xPhys[act] = copy(xTilde[act])\n",
" if ft > 1\n",
" f = (mean(prj(xPhys[act], eta, beta)) .- volfrac) * (ft == 3)\n",
" while abs(f) > maxchang\n",
" eta = eta - f / mean(deta(xPhys[:], eta, beta))\n",
" f = mean(prj(xPhys[act], eta, beta)) - volfrac\n",
" end\n",
" filter.dHs = Hs ./ reshape(dprj(xTilde, eta, beta), nely, nelx)\n",
" xPhys = prj(xPhys, eta, beta)\n",
" end\n",
" ch = norm(xPhys - xOld) ./ sqrt(nEl)\n",
" xOld = copy(xPhys)\n",
" #~ SETUP AND SOLVE EQUILIBRIUM EQUATIONS\n",
" U, C, Vf = FiniteElementAnalasys(mat, disfeature, load, xPhys)\n",
" push!(opt_hist, C)\n",
" push!(vf_hist, Vf)\n",
" #~ COMPUTE SENSITIVITIES\n",
" dc, dV0 = SensitivityAnalasys(setup, filter, mat, disfeature, U, xPhys)\n",
" #~ MMA iteration\n",
" xmma, low, upp = MMAupdate(xval, low, upp, xold1, xold2, C, Vf, dc, dV0, loop, Mat().volfrac)\n",
" # Some vectors are updated:\n",
" xold2 = copy(xold1)\n",
" xold1 = copy(xval)\n",
" xval = copy(xmma)\n",
"\n",
" x[act] = xval\n",
"\n",
" #~ CONTINUATION\n",
" beta = cnt(beta, betaCnt, loop, ch, maxchang)\n",
"\n",
" heatmap(reshape(xPhys, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none)\n",
"\n",
" end\n",
" if mod(loop, 10) == 0\n",
" println(\"It.: $loop C.: $C Vf.: $Vf ch.: $ch, p.: $penal beta.:$beta eta.: $eta \")\n",
" Visualization(setup, xPhys, loop)\n",
" end\n",
" end\n",
" gif(anim, \"./top/top_hist.gif\", fps=8)\n",
" return xPhys, opt_hist, vf_hist, anim\n",
"end\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# @time xPhys, opt_hist, vf_hist, loop = Optimization(setup, mat, load, filter, ini, disfeature)\n",
"Optimization(setup, mat, load, filter, ini, disfeature)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"function MMAupdate(xval::Array{Float64}, low::Array{Float64}, upp::Array{Float64}, \n",
" xold1::Array{Float64}, xold2::Array{Float64},\n",
" Obj::Float64, Vf::Float64, dc::Array{Float64}, dV0::Array{Float64}, loop::Int, volfrac::Float64)\n",
" move = 0.1\n",
" ### Initiation of MMA ###\n",
" m = 1\n",
" # active design variable\n",
" n = length(act)\n",
" onen = ones(n, 1)\n",
" onem = ones(m, 1)\n",
" zeron = zeros(n, 1)\n",
" zerom = zeros(m, 1)\n",
" a_mma = zerom\n",
" c_mma = 1.0e3 * onem\n",
" d_mma = zerom\n",
" a0 = 1.0\n",
" # column vector\n",
" # xval = xval\n",
" xmin = max.(xval .- move, zeron)\n",
" xmax = min.(xval .+ move, onen)\n",
"\n",
" # low = low\n",
" # upp = upp\n",
" # objective function \n",
" f0val = Obj\n",
" df0dx = dc[act]\n",
" df0dx2 = 0.0 * df0dx\n",
" # constraint function\n",
" fval = Vf / volfrac - 1.0 # column vector\n",
" dfdx = reshape(dV0[act], 1, length(act)) ./ volfrac # (m * n)\n",
" dfdx2 = 0.0 * dfdx\n",
"\n",
" # The MMA subproblem is solved at the point xval:\n",
" xmma, ymma, zmma, lam, xsi, eta, mu, zet, s, low, upp =\n",
" mmasub(m, n, loop, xval, xmin, xmax, xold1, xold2,\n",
" f0val, df0dx, df0dx2, fval, dfdx, dfdx2, low, upp, a0, a_mma, c_mma, d_mma)\n",
" \n",
" return xmma, low, upp\n",
" # xmma =\n",
" # mmasub(m, n, loop, xval, xmin, xmax, xold1, xold2,\n",
" # f0val, df0dx, df0dx2, fval, dfdx, dfdx2, low, upp, a0, a_mma, c_mma, d_mma)\n",
" # return xmma\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"include(\"MMA.jl\")\n",
"### Initiation of MMA ###\n",
"x = ones(disfeature.nEl, 1) * 0.5\n",
"act = disfeature.act\n",
"xval = copy(x[act])\n",
"xold1 = copy(xval)\n",
"xold2 = copy(xold1)\n",
"\n",
"low = copy(xval)\n",
"upp = copy(low)\n",
"\n",
"xval, low, upp = MMAupdate(xval, low, upp, xold1, xold2, 1.0, 0.3, x[act],\n",
" reshape(x[act], 1, length(act)) , 1, Mat().volfrac)\n",
"# ux2 = MMAupdate(xval, low, upp, xold1, xold2, 1.0, 0.3, x[act],\n",
"# reshape(x[act], 1, length(act)) , 1, Mat().volfrac)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"size(xval)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"using NLopt\n",
"\n",
"function myfunc(x::Vector, grad::Vector)\n",
" if length(grad) > 0\n",
" grad[1] = 0\n",
" grad[2] = 0.5/sqrt(x[2])\n",
" end\n",
" return sqrt(x[2])\n",
"end\n",
"\n",
"function myconstraint(x::Vector, grad::Vector, a, b)\n",
" if length(grad) > 0\n",
" grad[1] = 3a * (a*x[1] + b)^2\n",
" grad[2] = -1\n",
" end\n",
" (a*x[1] + b)^3 - x[2]\n",
"end\n",
"\n",
"opt = Opt(:LD_MMA, 2)\n",
"opt.lower_bounds = [-Inf, 0.]\n",
"opt.xtol_rel = 1e-4\n",
"\n",
"opt.min_objective = myfunc\n",
"inequality_constraint!(opt, (x,g) -> myconstraint(x,g,2,0), 1e-8)\n",
"inequality_constraint!(opt, (x,g) -> myconstraint(x,g,-1,1), 1e-8)\n",
"\n",
"(minf,minx,ret) = optimize(opt, [1.234, 5.678])\n",
"numevals = opt.numevals # the number of function evaluations\n",
"println(\"got $minf at $minx after $numevals iterations (returned $ret)\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"using NLopt\n",
"function update_NLopt(act, xval, low, upp, C, Vf, dc, dV0)\n",
" alg = \":LD_MMA\"\n",
" n = length(act)\n",
" opt = Opt(alg, n)\n",
" opt.lower_bounds = low\n",
" opt.upper_bounds = upp\n",
"\n",
" opt.min_objective = ObjectiveFunction\n",
"\n",
" inequality_constraint!(opt, (x, g) -> VolumeConstraint(x, g;), 1e-8)\n",
"\n",
" return\n",
"end\n",
"\n",
"function ObjectiveFunction(x, grad; act, obj, dobj)\n",
" if length(grad) > 0\n",
" grad[:] = dobj[act]\n",
" end\n",
" return obj\n",
"end\n",
"\n",
"function VolumeConstraint(x, g; )\n",
" if length(grad) > 0\n",
" grad[:] = dV0\n",
" end\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"f(x::Matrix, y) = sum(sin, x) + prod(tan, x) * sum(sqrt, x)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"using Zygote\n",
"grad = gradient(y -> f(y), rand(3))\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"typeof(grad)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.7.2",
"language": "julia",
"name": "julia-1.7"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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# __precompile__()
# module TopOpt99neo_MMA
using LinearAlgebra, SparseArrays
using Plots
# : heatmap, savefig, @animate
using ImageFiltering: imfilter
using Statistics: mean
using BenchmarkTools
BLAS.set_num_threads(1)
# abstract type top99neo end
include("utils.jl")
include("MMA.jl")
export SetUp, Mat, DiscretizationFeature, LoadsSupportsBCs, Initialization, Filter
export Optimization, Visualization
mutable struct SetUp
nelx:: Int
nely:: Int
eta:: Float64
beta:: Int
penalCnt:: Array
betaCnt:: Array
move:: Float64
maxit:: Int
maxchang:: Float64
pasS:: Array{Int}
pasV:: Array{Int}
function SetUp()
nelx = 120
nely = 40
# volfrac = 0.3
maxit = 500
move = 0.1
beta = 2
eta = 0.5
maxchang = 1e-6
penalCnt = [1, 3, 20, 0.2]
betaCnt = [1, 32, 10, 2]
# continuation scheme on beta parCont = { istart,maxPar,steps,deltaPar }
# elNrs = reshape(1:nely*nelx, nely, nelx)
# a1 = elNrs[Int.(nely/4:nely/2), Int.(nelx/4:nelx/2)]
# pasS, pasV = Array([]), a1[:]
pasS, pasV = Array([]), Array([])
new(nelx, nely, eta, beta, penalCnt, betaCnt, move, maxit, maxchang, pasS, pasV)
end
end
mutable struct Mat
E0:: Float64
Emin:: Float64
ν:: Float64
penal:: Float64
volfrac:: Float64
function Mat()
E0 = 1.0
Emin = 1e-9 * E0
ν = 0.3
penal = 3.0
volfrac = 0.3
new(E0, Emin, ν, penal, volfrac)
end
end
struct DiscretizationFeature
nEl:: Int
nodeNrs:: Array{Int,2}
nDof:: Int
act
cMat:: Array{Int}
Iar:: Array{Int}
Ke:: Array{Float64,1}
Ke0:: Array{Float64,2}
function DiscretizationFeature(setup::SetUp, mat::Mat)
nelx = setup.nelx
nely = setup.nely
pasS, pasV = setup.pasS, setup.pasV
ν = mat.ν
nEl = nely * nelx
nodeNrs = reshape(1:(1+nelx)*(1+nely), nely + 1, nelx + 1)
nDof = (nely + 1) * (nelx + 1) * 2
act = setdiff(collect(1:nEl), union(pasS, pasV))
cVec = reshape(2 * nodeNrs[1:end-1, 1:end-1] .+ 1, nEl, 1)
cMat = Int.(repeat(cVec, 1, 8) + repeat([0 1 2 * nely .+ [2 3 0 1] -2 -1], nelx * nely, 1))
FuckRow = [1 2 3 4 5 6 7 8]
sI::Array{Int}, sII::Array{Int} = copy(FuckRow), fill(1, 1, 8)
@inbounds for j in 2: 8
sI = cat(sI, FuckRow[j:8]'; dims=2)
# sII = cat(2, sII, repmat(j, 1, 8 - j + 1))
sII = cat(sII, fill(j, 1, 8 - j + 1); dims=2)
end
iK::Array{Int,2}, jK::Array{Int,2} = cMat[:, sI][:, 1, :]', cMat[:, sII][:, 1, :]'
Iar = sort([iK[:] jK[:]]; dims=2, rev=true) # comma is a newline
# iK[:], jK[:] .= 0.0, 0.0
c1 = [12, 3, -6, -3, -6, -3, 0, 3, 12, 3, 0, -3, -6, -3, -6, 12, -3, 0, -3, -6, 3, 12, 3, -6, 3, -6, 12, 3, -6, -3, 12, 3, 0, 12, -3, 12]
c2 = [-4, 3, -2, 9, 2, -3, 4, -9, -4, -9, 4, -3, 2, 9, -2, -4, -3, 4, 9, 2, 3, -4, -9, -2, 3, 2, -4, 3, -2, 9, -4, -9, 4, -4, -3, -4]
Ke = 1 / (1 - ν^2) / 24 .* (c1 .+ ν .* c2) # half-KE vector
# full KE
# Ke0::Array{Float64} = zeros(8, 8)
# start_id, end_id = 1, 8
# for i in 1: 8
# Ke0[i:8, i] = Ke[start_id:end_id]
# start_id, end_id = end_id + 1, 2 * end_id - start_id
# end
Ke0::Array{Float64} = zeros(8, 8)
# Index::Array{Int} = [sI' sII']
# Ke0[sI, sII] = Ke
Index = findall(isequal(1), tril(ones(8, 8)))
Ke0[Index] = Ke'
# Ke0 = reshape(Ke0, 8, 8)
Ke0 = Ke0 + Ke0' - diagm(diag(Ke0))
# diag return the diag of a matrix
# diagm build a matirx with a diag
new(nEl, nodeNrs, nDof, act, cMat, Iar, Ke, Ke0)
end
end
mutable struct LoadsSupportsBCs
lcDof:: Array{Int}
F
free:: Array{Int}
fixed:: Array{Int}
function LoadsSupportsBCs(setup::SetUp, disfeature::DiscretizationFeature)
nelx = setup.nelx
nely = setup.nely
nodeNrs = disfeature.nodeNrs
nDof = disfeature.nDof
load_position::Symbol = :half_MBB
if load_position == :half_MBB
load_nodey , load_nodex = 1, 1
# fixed = union([1:2:2*(nely+1)], 2 * nodeNrs[nely+1, nelx+1])
fixed = union(collect(1:2:2*(nely+1)), 2 * nodeNrs[end, end])
elseif load_position == :cantilever
load_nodey = nely + 1
load_nodex = nelx / 2 + 1
fixed = 1:2*(nely+1)
end
F = spzeros(nDof)
load_type::Symbol = :pin
if load_type == :pin # 1 point
lcDof = collect(2 * nodeNrs[load_nodey, load_nodex])
F[2] = -1.0
elseif load_type == :points # 5 points
# lcDof = collect(2 * nodeNrs[load_nodey, load_nodex], nodeNrs[load_nodey, load_nodex-1], nodeNrs[load_nodey, load_nodex-2], nodeNrs[load_nodey, load_nodex+1], nodeNrs[load_nodey, load_nodex+2])
F[lcDof'] .= -1.0
elseif load_type == :line
lcDof = [2:2*(nely+1):nDof]
F = spzeros(nDof, 1)
F[lcDof'] .= -1.0
# F = sparse(lcDof', ones(length(lcDof')), -1.0)
end
all = collect(1:nDof)
free = setdiff(all, fixed)
new(lcDof, F, free, fixed)
end
end
struct Initialization
x:: Array{Float64}
xPhys:: Array{Float64}
xOld:: Array{Float64}
ch:: Float64
loop:: Int
function Initialization(setup::SetUp, disfeature::DiscretizationFeature, mat::Mat)
pasV = setup.pasV
pasS = setup.pasS
act = disfeature.act
volfrac = mat.volfrac
nEl = disfeature.nEl
# nDof = disfeature.nDof
# column vectors
x = zeros(nEl, 1)
x[act] .= volfrac
x[pasS] .= 1.0
xPhys, xOld, ch, loop = copy(x), ones(nEl, 1), 1.0, 0
# x̅ x̃
new(x, xPhys, xOld, ch, loop)
end
end
mutable struct Filter
rmin:: Float64
ft:: Int
h:: Array{Float64}
Hs:: Array{Float64}
dHs:: Array{Float64}
function Filter(setup::SetUp)
nelx = setup.nelx
nely = setup.nely
# volfrac = mat.volfrac
# bcF = setup.bcF
rmin = 6.5
ft = 3 # 1 density 2 projection 3 volume preserving
dy, dx = meshgrid(-ceil(rmin)+1:ceil(rmin)-1, -ceil(rmin)+1:ceil(rmin)-1)
h = max.(0, rmin .- sqrt.(dx .^ 2 + dy .^ 2))
Hs = imfilter(ones(nely, nelx), h, "symmetric")
dHs = Hs
new(rmin, ft, h, Hs, dHs)
end
end
function FiniteElementAnalasys(mat::Mat, disfeature::DiscretizationFeature, load::LoadsSupportsBCs, xPhys::Array{Float64})
nEl , nDof, Iar, Ke = disfeature.nEl, disfeature.nDof, disfeature.Iar, disfeature.Ke
act = disfeature.act
Emin, penal, E0 = mat.Emin, mat.penal, mat.E0
F , free = load.F, load.free
# Interplotation model
sK = Emin .+ xPhys .^ penal .* (E0 - Emin)
sK = reshape(Ke[:] * sK', length(Ke) * nEl, 1)
K = sparse(Iar[:, 1], Iar[:, 2], vec(sK), nDof, nDof)
U = zeros(nDof)
# U[free] = cholesky(Symmetric(K[free, free], :L), check=false) \ F[free
U[free] = cholesky(Symmetric(K, :L)[free, free], check=false) \ F[free]
Obj = F' * U
Vf = sum(xPhys[act])/length(act)
return U, Obj, Vf
end
function SensitivityAnalasys(setup::SetUp, filter::Filter, mat::Mat, disfeature::DiscretizationFeature, U::Array{Float64}, xPhys::Array{Float64})
nelx, nely, act = setup.nelx, setup.nely, disfeature.act
dHs , h = filter.dHs, filter.h
E0 , Emin, penal = mat.E0, mat.Emin, mat.penal
nEl , cMat, Ke0 = disfeature.nEl, disfeature.cMat, disfeature.Ke0
act = disfeature.act
dsK, dV = zeros(nEl), zeros(nEl)
dV[act] .= 1.0/length(act)
dsK[act] = -penal * (E0 - Emin) .* xPhys[act] .^ (penal - 1)
dc = dsK .* sum((U[cMat] * Ke0) .* U[cMat], dims=2)
dc = imfilter(reshape(dc, nely, nelx) ./ dHs, h, "symmetric")
dV0 = imfilter(reshape(dV, nely, nelx) ./ dHs, h, "symmetric")
return reshape(dc, nEl, 1), reshape(dV0, nEl, 1)
end
function MMAupdate(act, xval::Array{Float64}, low::Array{Float64}, upp::Array{Float64},
xold1:: Array{Float64}, xold2:: Array{Float64},
Obj, Vf:: Float64,
dc:: Array{Float64}, dV0:: Array{Float64}, loop:: Int, volfrac:: Float64)
move = 0.1
### Initiation of MMA ###
m = 1
# active design variable
n = length(act)
onen = ones(n, 1)
onem = ones(m, 1)
zeron = zeros(n, 1)
zerom = zeros(m, 1)
a_mma = zerom
c_mma = 1.0e3 * onem
d_mma = zerom
a0 = 1.0
# column vector
# xval = xval
xmin = max.(xval .- move, zeron)
xmax = min.(xval .+ move, onen)
# low = low
# upp = upp
# objective function
f0val = Obj
df0dx = dc[act]
df0dx2 = 0.0 * df0dx
# constraint function
fval = Vf / volfrac - 1.0 # column vector
dfdx = reshape(dV0[act], 1, length(act)) ./ volfrac # (m * n)
dfdx2 = 0.0 * dfdx
# The MMA subproblem is solved at the point xval:
xmma, ymma, zmma, lam, xsi, eta, mu, zet, s, low, upp =
mmasub(m, n, loop, xval, xmin, xmax, xold1, xold2,
f0val, df0dx, df0dx2, fval, dfdx, dfdx2, low, upp, a0, a_mma, c_mma, d_mma)
return xmma, low, upp
end
function Visualization(setup::SetUp, x::Array{Float64}, loop::Int)
nelx, nely = setup.nelx, setup.nely
# cmap = cgrad(:Blues_9, rev=false)
plot = heatmap(reshape(x, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none, display_type=:gui)
# display(plot)
# if mod(loop, 10) = = 0
# savefig(plot, "./res/res_$loop.pdf")
savefig(plot, "./res/design_$loop.png")
# end
# PLOT FINAL DESIGN
# heatmap(1.0 .- x[end:-1:1, :], yaxis=false, xaxis=false, legend=:none,color=:greys, grid=false, border=nothing, aspect_ratio=:equal)
nothing
end
function Optimization(setup::SetUp, mat::Mat, load::LoadsSupportsBCs, filter::Filter, ini::Initialization, disfeature::DiscretizationFeature)
ch, loop = ini.ch, ini.loop
x , xPhys, xOld = ini.x, ini.xPhys, ini.xOld
maxit, maxchang = setup.maxit, setup.maxchang
nely, nelx = setup.nely, setup.nelx
eta, beta, penalCnt, betaCnt = setup.eta, setup.beta, setup.penalCnt, setup.betaCnt
volfrac, penal, nEl = mat.volfrac, mat.penal, disfeature.nEl
act = disfeature.act
Hs, h, ft = filter.Hs, filter.h, filter.ft
opt_hist = []
vf_hist = []
### Initiation of MMA ###
xval = copy(x[act])
xold1 = copy(xval)
xold2 = copy(xold1)
low = copy(xval)
upp = copy(low)
#########################
loop_beta = 0
anim = @animate while ch > maxchang && loop < maxit || beta < betaCnt[2]
@time begin
loop = loop + 1
# COMPUTE PHYSICAL DENSITY FIELD
xTilde = imfilter(reshape(x, nely, nelx), h, "symmetric") ./ Hs
xPhys[act] = copy(xTilde[act])
if ft > 1
f = (mean(prj(xPhys[act], eta, beta)) .- volfrac) * (ft == 3)
while abs(f) > maxchang
eta = eta - f / mean(deta(xPhys[:], eta, beta))
f = mean(prj(xPhys[act], eta, beta)) - volfrac
end
filter.dHs = Hs ./ reshape(dprj(xTilde, eta, beta), nely, nelx)
xPhys = prj(xPhys, eta, beta)
end
ch = norm(xPhys - xOld) ./ sqrt(nEl)
xOld = copy(xPhys)
#~ SETUP AND SOLVE EQUILIBRIUM EQUATIONS
U, C, Vf = FiniteElementAnalasys(mat, disfeature, load, xPhys)
push!(opt_hist, C)
push!(vf_hist, Vf)
#~ COMPUTE SENSITIVITIES
dc, dV0 = SensitivityAnalasys(setup, filter, mat, disfeature, U, xPhys)
#~ MMA iteration
xmma, low, upp = MMAupdate(act, xval, low, upp, xold1, xold2, C, Vf, dc, dV0, loop, Mat().volfrac)
# Some vectors are updated:
xold2 = copy(xold1)
xold1 = copy(xval)
xval = copy(xmma)
x[act] = xval
#~ CONTINUATION
# if mat.penal == 1.0
mat.penal = cnt(mat.penal, penalCnt,loop, ch, maxchang)
# end
if mat.penal < 3.0
beta = 2.0
else
loop_beta = loop_beta + 1
beta = cnt(beta, betaCnt, loop_beta, ch, maxchang)
# restart mma
end
heatmap(reshape(xPhys, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none)
end
if mod(loop, 20) == 0
println("It.: $loop C.: $C Vf.: $Vf ch.: $ch, p.: $(mat.penal) beta.:$beta eta.: $eta ")
Visualization(setup, xPhys, loop)
end
end
# gif(anim, "./top/cantilever.gif", fps=8)
return xPhys, opt_hist, vf_hist, anim
end
# end

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# export loads!, bcs!, passive_domain!
export prj, deta, dprj, cnt
export meshgrid
export Visualization
function prj(v::Array{Float64}, eta::Float64, beta)
return (tanh(beta * eta) .+ tanh.(beta * (v[:] .- eta))) ./ (tanh(beta * eta) + tanh(beta * (1 - eta)))
end
function deta(v::Array{Float64}, eta::Float64, beta)
return -beta * csch(beta) .* sech.(beta * (v[:] .- eta)) .^ 2 .* sinh.(v[:] * beta) .* sinh.((1 .- v[:]) * beta)
end
function dprj(v::Array{Float64}, eta::Float64, beta)
return beta * (1 .- tanh.(beta * (v .- eta)) .^ 2) ./ (tanh(beta * eta) + tanh(beta * (1 - eta)))
end
function cnt(v, vCnt::Array, l::Int, ch::Float64, maxchang::Float64)
return v + (l >= vCnt[1]) * (v < vCnt[2]) * (mod(l, vCnt[3]) == 0 || ch <= maxchang) * vCnt[4]
end
"""
meshgrid(vx)
Computes an (x,y)-grid from the vectors (vx,vx).
For more information, see the MATLAB documentation.
"""
meshgrid(v::AbstractVector) = meshgrid(v, v)
"""
meshgrid(vx,vy)
Computes an (x,y)-grid from the vectors (vx,vy).
For more information, see the MATLAB documentation.
"""
function meshgrid(vx::AbstractVector{T}, vy::AbstractVector{T}) where {T}
m, n = length(vy), length(vx)
vx = reshape(vx, 1, n)
vy = reshape(vy, m, 1)
(repeat(vx, m, 1), repeat(vy, 1, n))
end
"""
meshgrid(vx,vy,vz)
Computes an (x,y,z)-grid from the vectors (vx,vy,vz).
For more information, see the MATLAB documentation.
"""
function meshgrid(vx::AbstractVector{T}, vy::AbstractVector{T},
vz::AbstractVector{T}) where {T}
m, n, o = length(vy), length(vx), length(vz)
vx = reshape(vx, 1, n, 1)
vy = reshape(vy, m, 1, 1)
vz = reshape(vz, 1, 1, o)
om = ones(Int, m)
on = ones(Int, n)
oo = ones(Int, o)
(vx[om, :, oo], vy[:, on, oo], vz[om, on, :])
end

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"module test\n",
"export main\n",
"include(\"/Users/yu/TopCodes/Julia/test/top99neo.jl\")\n",
"# include(joinpath(@__DIR__, \"top99neo.jl\"))\n",
"using .TopOpt99neo\n",
"function main()\n",
" setup = SetUp()\n",
" mat = Mat()\n",
" disfeature = DiscretizationFeature(setup, mat)\n",
" load = LoadsSupportsBCs(setup, disfeature)\n",
" ini = Initialization(setup, disfeature, mat)\n",
" filter = Filter(setup)\n",
" xPhys, opt_hist, vf_hist, anim = Optimization(setup, mat, load, filter, ini, disfeature)\n",
" return xPhys, opt_hist, vf_hist, anim\n",
"end\n",
"\n",
"end"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"using .test\n",
"\n",
"main()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"using Plots\n",
"gif(anim, \"./top/top_hist.gif\", fps=8)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Julia 1.7.0",
"language": "julia",
"name": "julia-1.7"
},
"language_info": {
"file_extension": ".jl",
"mimetype": "application/julia",
"name": "julia",
"version": "1.7.0"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}

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__precompile__()
module TopOpt99neo
using LinearAlgebra, SparseArrays
using Plots
# : heatmap, savefig, @animate
using ImageFiltering: imfilter
using Statistics: mean
using BenchmarkTools
BLAS.set_num_threads(1)
abstract type top99neo end
include("utils.jl")
export SetUp, Mat, DiscretizationFeature, LoadsSupportsBCs, Initialization, Filter
export Optimization, Visualization
mutable struct SetUp <: top99neo
nelx::Int
nely::Int
eta::Float64
beta::Int
betaCnt::NTuple{4,Int}
move::Float64
maxit::Int
maxchang::Float64
pasS::Array{Int}
pasV::Array{Int}
function SetUp()
nelx = 150
nely = 50
# volfrac = 0.3
maxit = 300
move = 0.1
beta = 2
eta = 0.5
maxchang = 1e-6
# penalCnt = {maxit+1, 3, 25, 0.25}
betaCnt = (2, 32, 10, 2) # continuation scheme on beta parCont = { istart,maxPar,steps,deltaPar }
pasS, pasV = Array([]), Array([])
new(nelx, nely, eta, beta, betaCnt, move, maxit, maxchang, pasS, pasV)
end
end
mutable struct Mat <: top99neo
E0::Float64
Emin::Float64
ν::Float64
penal::Float64
volfrac::Float64
function Mat()
E0 = 1
Emin = 1e-9
ν = 0.3
penal = 3.0
volfrac = 0.3
new(E0, Emin, ν, penal, volfrac)
end
end
struct DiscretizationFeature <: top99neo
nEl::Int
nodeNrs::Array{Int,2}
nDof::Int
act
cMat::Array{Int}
Iar::Array{Int}
Ke::Array{Float64,1}
Ke0::Array{Float64,2}
function DiscretizationFeature(setup::SetUp, mat::Mat)
nelx = setup.nelx
nely = setup.nely
pasS, pasV = setup.pasS, setup.pasV
ν = mat.ν
nEl = nely * nelx
nodeNrs = reshape(1:(1+nelx)*(1+nely), nely + 1, nelx + 1)
nDof = (nely + 1) * (nelx + 1) * 2
act = setdiff(collect(1:nEl), union(pasS, pasV))
cVec = reshape(2 * nodeNrs[1:end-1, 1:end-1] .+ 1, nEl, 1)
cMat = Int.(repeat(cVec, 1, 8) + repeat([0 1 2 * nely .+ [2 3 0 1] -2 -1], nelx * nely, 1))
FuckRow = [1 2 3 4 5 6 7 8]
sI::Array{Int}, sII::Array{Int} = copy(FuckRow), fill(1, 1, 8)
@inbounds for j in 2:8
sI = cat(sI, FuckRow[j:8]'; dims=2)
# sII = cat(2, sII, repmat(j, 1, 8 - j + 1))
sII = cat(sII, fill(j, 1, 8 - j + 1); dims=2)
end
iK::Array{Int,2}, jK::Array{Int,2} = cMat[:, sI][:, 1, :]', cMat[:, sII][:, 1, :]'
Iar = sort([iK[:] jK[:]]; dims=2, rev=true) # comma is a newline
# iK, jK .= 0 , 0
c1 = [12, 3, -6, -3, -6, -3, 0, 3, 12, 3, 0, -3, -6, -3, -6, 12, -3, 0, -3, -6, 3, 12, 3, -6, 3, -6, 12, 3, -6, -3, 12, 3, 0, 12, -3, 12]
c2 = [-4, 3, -2, 9, 2, -3, 4, -9, -4, -9, 4, -3, 2, 9, -2, -4, -3, 4, 9, 2, 3, -4, -9, -2, 3, 2, -4, 3, -2, 9, -4, -9, 4, -4, -3, -4]
Ke = 1 / (1 - ν^2) / 24 .* (c1 .+ ν .* c2) # half-KE vector
# full KE
# Ke0::Array{Float64} = zeros(8, 8)
# start_id, end_id = 1, 8
# for i in 1:8
# Ke0[i:8, i] = Ke[start_id:end_id]
# start_id, end_id = end_id + 1, 2 * end_id - start_id
# end
Ke0::Array{Float64} = zeros(8, 8)
# Index::Array{Int} = [sI' sII']
# Ke0[sI, sII] = Ke
Index = findall(isequal(1), tril(ones(8, 8)))
Ke0[Index] = Ke'
# Ke0 = reshape(Ke0, 8, 8)
Ke0 = Ke0 + Ke0' - diagm(diag(Ke0))
new(nEl, nodeNrs, nDof, act, cMat, Iar, Ke, Ke0)
end
end
mutable struct LoadsSupportsBCs <: top99neo
lcDof::Array{Int}
F
free::Array{Int}
fixed::Array{Int}
function LoadsSupportsBCs(setup::SetUp, disfeature::DiscretizationFeature)
nelx = setup.nelx
nely = setup.nely
nodeNrs = disfeature.nodeNrs
nDof = disfeature.nDof
load_position::Symbol = :half_MBB
if load_position == :half_MBB
load_nodey, load_nodex = 1, 1
# fixed = union([1:2:2*(nely+1)], 2 * nodeNrs[nely+1, nelx+1])
fixed = union(collect(1:2:2*(nely+1)), 2 * nodeNrs[end, end])
elseif load_position == :cantilever
load_nodey = nely + 1
load_nodex = nelx / 2 + 1
fixed = 1:2*(nely+1)
end
F = spzeros(nDof)
load_type::Symbol = :pin
if load_type == :pin # 1 point
lcDof = collect(2 * nodeNrs[load_nodey, load_nodex])
F[2] = -1.0
elseif load_type == :points # 5 points
# lcDof = collect(2 * nodeNrs[load_nodey, load_nodex], nodeNrs[load_nodey, load_nodex-1], nodeNrs[load_nodey, load_nodex-2], nodeNrs[load_nodey, load_nodex+1], nodeNrs[load_nodey, load_nodex+2])
F[lcDof'] .= -1.0
elseif load_type == :line
lcDof = [2:2*(nely+1):nDof]
F = spzeros(nDof, 1)
F[lcDof'] .= -1.0
# F = sparse(lcDof', ones(length(lcDof')), -1.0)
end
all = collect(1:nDof)
free = setdiff(all, fixed)
new(lcDof, F, free, fixed)
end
end
struct Initialization <: top99neo
x::Array{Float64}
xPhys::Array{Float64}
xOld::Array{Float64}
ch::Float64
loop::Int
function Initialization(setup::SetUp, disfeature::DiscretizationFeature, mat::Mat)
pasV = setup.pasV
pasS = setup.pasS
act = disfeature.act
volfrac = mat.volfrac
nEl = disfeature.nEl
# nDof = disfeature.nDof
# column vectors
x = zeros(nEl, 1)
# x[act] .= (volfrac * (nEl - length(pasV)) - length(pasS)) / length(act)
x[act] .= volfrac
x[pasS] .= 1.0
xPhys, xOld, ch, loop = copy(x), ones(nEl, 1), 1.0, 0
# x̅ x̃
new(x, xPhys, xOld, ch, loop)
end
end
mutable struct Filter <: top99neo
rmin::Float64
ft::Int
h::Array{Float64}
Hs::Array{Float64}
dHs::Array{Float64}
function Filter(setup::SetUp)
nelx = setup.nelx
nely = setup.nely
# volfrac = mat.volfrac
# bcF = setup.bcF
rmin = nely / 20
ft = 3 # 1 density 2 projection 3 volume preserving
dy, dx = meshgrid(-ceil(rmin)+1:ceil(rmin)-1, -ceil(rmin)+1:ceil(rmin)-1)
h = max.(0, rmin .- sqrt.(dx .^ 2 + dy .^ 2))
Hs = imfilter(ones(nely, nelx), h, "symmetric")
dHs = Hs
new(rmin, ft, h, Hs, dHs)
end
end
function FiniteElementAnalasys(mat::Mat, disfeature::DiscretizationFeature, load::LoadsSupportsBCs, xPhys::Array{Float64})
nEl, nDof, Iar, Ke = disfeature.nEl, disfeature.nDof, disfeature.Iar, disfeature.Ke
Emin, penal, E0 = mat.Emin, mat.penal, mat.E0
F, free = load.F, load.free
sK = Emin .+ xPhys .^ penal .* (E0 - Emin)
sK = reshape(Ke[:] * sK', length(Ke) * nEl, 1)
K::SparseMatrixCSC = sparse(Iar[:, 1], Iar[:, 2], vec(sK), nDof, nDof)
U = zeros(nDof)
# U[free] = cholesky(Symmetric(K[free, free], :L), check=false) \ F[free
U[free] = cholesky(Symmetric(K, :L)[free, free], check=false) \ F[free]
Obj = F' * U
Vf = mean(xPhys)
return U, Obj, Vf
end
function SensitivityAnalasys(setup::SetUp, filter::Filter, mat::Mat, disfeature::DiscretizationFeature, U::Array{Float64}, xPhys::Array{Float64})
nelx, nely, act = setup.nelx, setup.nely, disfeature.act
dHs, h = filter.dHs, filter.h
E0, Emin, volfrac, penal = mat.E0, mat.Emin, mat.volfrac, mat.penal
nEl, cMat, Ke0 = disfeature.nEl, disfeature.cMat, disfeature.Ke0
dsK, dV = zeros(nEl, 1), zeros(nEl, 1)
#! active volume fraction in the design domain
# fval = sum(xPhys[act])./length( act )./volfrac.-1.0
dV[act] .= 1.0 / length(act) / volfrac
dsK[act] = -penal * (E0 - Emin) .* xPhys[act] .^ (penal - 1)
dc = dsK .* sum((U[cMat] * Ke0) .* U[cMat], dims=2)
dc = imfilter(reshape(dc, nely, nelx) ./ dHs, h, "symmetric")
dV0 = imfilter(reshape(dV, nely, nelx) ./ dHs, h, "symmetric")
return dc, dV0
end
# UPDATE DESIGN VARIABLES AND APPLY CONTINUATION
function OCupdate(x::Array{Float64}, setup::SetUp, mat::Mat, disfeature::DiscretizationFeature, dc::Array{Float64}, dV0::Array{Float64})
act, move, volfrac = disfeature.act, setup.move, mat.volfrac
xT = zeros(length(act), 1)
xT = x[act]
xU, xL = xT .+ move, xT .- move
ocP = xT .* real.(sqrt.(-dc[act] ./ dV0[act]))
l = [0 mean(ocP) / volfrac]
while (l[2] - l[1]) / (l[2] + l[1]) > 1e-4
lmid = 0.5 * (l[1] + l[2])
x[act] = max.(max.(min.(min.(ocP ./ lmid, xU), 1.0), xL), 0.0)
if mean(x) > volfrac
l[1] = lmid
else
l[2] = lmid
end
end
return x
end
function Visualization(setup::SetUp, x::Array{Float64}, loop::Int)
nelx, nely = setup.nelx, setup.nely
# cmap = cgrad(:Blues_9, rev=false)
plot = heatmap(reshape(x, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none, display_type=:gui)
# display(plot)
# if mod(loop, 10) == 0
savefig(plot, "./top/res_$loop.pdf")
# end
# PLOT FINAL DESIGN
# heatmap(1.0 .- x[end:-1:1, :], yaxis=false, xaxis=false, legend=:none,color=:greys, grid=false, border=nothing, aspect_ratio=:equal)
nothing
end
function Optimization(setup::SetUp, mat::Mat, load::LoadsSupportsBCs, filter::Filter, ini::Initialization, disfeature::DiscretizationFeature)
ch, loop = ini.ch, ini.loop
x, xPhys, xOld = ini.x, ini.xPhys, ini.xOld
maxit, maxchang = setup.maxit, setup.maxchang
nely, nelx = setup.nely, setup.nelx
eta, beta, betaCnt = setup.eta, setup.beta, setup.betaCnt
volfrac, penal, nEl = mat.volfrac, mat.penal, disfeature.nEl
act = disfeature.act
Hs, h, ft = filter.Hs, filter.h, filter.ft
opt_hist = []
vf_hist = []
anim = @animate while ch > maxchang && loop < maxit || beta < betaCnt[2]
@time begin
loop = loop + 1
# COMPUTE PHYSICAL DENSITY FIELD
xTilde = imfilter(reshape(x, nely, nelx), h, "symmetric") ./ Hs
xPhys[act] = copy(xTilde[act])
if ft > 1
f = (mean(prj(xPhys, eta, beta)) .- volfrac) * (ft == 3)
while abs(f) > maxchang
eta = eta - f / mean(deta(xPhys[:], eta, beta))
f = mean(prj(xPhys, eta, beta)) - volfrac
end
filter.dHs = Hs ./ reshape(dprj(xTilde, eta, beta), nely, nelx)
xPhys = prj(xPhys, eta, beta)
end
ch = norm(xPhys - xOld) ./ sqrt(nEl)
xOld = copy(xPhys)
# SETUP AND SOLVE EQUILIBRIUM EQUATIONS
U, C, Vf = FiniteElementAnalasys(mat, disfeature, load, xPhys)
push!(opt_hist, C)
push!(vf_hist, Vf)
# COMPUTE SENSITIVITIES
dc, dV0 = SensitivityAnalasys(setup, filter, mat, disfeature, U, xPhys)
# OC iteration
x = OCupdate(x, setup, mat, disfeature, dc, dV0)
# CONTINUATION
beta = cnt(beta, betaCnt, loop, ch, maxchang)
heatmap(reshape(xPhys, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none)
end
if mod(loop, 10) == 0
println("It.: $loop C.: $C Vf.: $Vf ch.: $ch, p.: $penal beta.:$beta eta.: $eta ")
Visualization(setup, xPhys, loop)
end
end
return xPhys, opt_hist, vf_hist, anim
end
end

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top_oc/utils.jl Executable file
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# export loads!, bcs!, passive_domain!
export prj, deta, dprj, cnt
export meshgrid
export Visualization
function prj(v::Array{Float64}, eta::Float64, beta::Int)
return (tanh(beta * eta) .+ tanh.(beta * (v[:] .- eta))) ./ (tanh(beta * eta) + tanh(beta * (1 - eta)))
end
function deta(v::Array{Float64}, eta::Float64, beta::Int)
return -beta * csch(beta) .* sech.(beta * (v[:] .- eta)) .^ 2 .* sinh.(v[:] * beta) .* sinh.((1 .- v[:]) * beta)
end
function dprj(v::Array{Float64}, eta::Float64, beta::Int)
return beta * (1 .- tanh.(beta * (v .- eta)) .^ 2) ./ (tanh(beta * eta) + tanh(beta * (1 - eta)))
end
function cnt(v::Int, vCnt::NTuple{4,Int}, l::Int, ch::Float64, maxchang::Float64)
return v + (l >= vCnt[1]) * (v < vCnt[2]) * (mod(l, vCnt[3]) == 0 || ch <= maxchang) * vCnt[4]
end
"""
meshgrid(vx)
Computes an (x,y)-grid from the vectors (vx,vx).
For more information, see the MATLAB documentation.
"""
meshgrid(v::AbstractVector) = meshgrid(v, v)
"""
meshgrid(vx,vy)
Computes an (x,y)-grid from the vectors (vx,vy).
For more information, see the MATLAB documentation.
"""
function meshgrid(vx::AbstractVector{T}, vy::AbstractVector{T}) where {T}
m, n = length(vy), length(vx)
vx = reshape(vx, 1, n)
vy = reshape(vy, m, 1)
(repeat(vx, m, 1), repeat(vy, 1, n))
end
"""
meshgrid(vx,vy,vz)
Computes an (x,y,z)-grid from the vectors (vx,vy,vz).
For more information, see the MATLAB documentation.
"""
function meshgrid(vx::AbstractVector{T}, vy::AbstractVector{T},
vz::AbstractVector{T}) where {T}
m, n, o = length(vy), length(vx), length(vz)
vx = reshape(vx, 1, n, 1)
vy = reshape(vy, m, 1, 1)
vz = reshape(vz, 1, 1, o)
om = ones(Int, m)
on = ones(Int, n)
oo = ones(Int, o)
(vx[om, :, oo], vy[:, on, oo], vz[om, on, :])
end
function Visualization(setup::SetUp, x::Array{Float64}, loop::Int)
nelx, nely = setup.nelx, setup.nely
# cmap = cgrad(:Blues_9, rev=false)
plot = heatmap(reshape(x, nely, nelx), c=:Blues_9, aspect_ratio=:equal, yflip=true, grid=false, axis=:off, tick=false, colorbar=false, border=nothing, dpi=300, size=(400, nely / nelx * 400), legend=:none, display_type=:gui)
# display(plot)
# if mod(loop, 10) == 0
savefig(plot, "./top_mma/res_$loop.pdf")
# end
# PLOT FINAL DESIGN
# heatmap(1.0 .- x[end:-1:1, :], yaxis=false, xaxis=false, legend=:none,color=:greys, grid=false, border=nothing, aspect_ratio=:equal)
nothing
end