Obtaining iteration-wise samples using TopOpt

[Shantanu21285]

Greetings,

I was wondering if someone could help me out with this. I wish to obtain iteration wise samples of the 3D sample i am optimizing. Let;s say if it takes 100 iterations to converge to the optimized figure, i want all the 3D sample figures at every iteration.

I checked the github but i believe this functionality is not there. Below is my code, could you tell how do i do this?

using TopOpt
using Makie, GeometryBasics, GLMakie # For visualization
using Images
using FileIO

Material properties

E = 1.0 # Young's modulus
v = 0.3 # Poisson's ratio

Force applied

f = -1.0; # Downward force magnitude

Number of elements in each dimension

nels = (30, 10, 10) # Adjust based on desired resolution and computational resources

Define the problem, assuming a cantilever fixed at one end with a point load at the other

problem = PointLoadCantilever(Val{:Linear}, nels, (1.0, 1.0, 1.0), E, v, f)

Optimization settings

V = 0.3 # Volume fraction
xmin = 1e-6 # Minimum density
rmin = 2.0 # Density filter radius

Penalty for intermediate density values

penalty = TopOpt.PowerPenalty(3.0)

Finite element solver setup

solver = FEASolver(Direct, problem; xmin=xmin, penalty=penalty)

Compliance objective

comp = TopOpt.Compliance(solver)
filter = DensityFilter(solver; rmin=rmin)
obj = x -> comp(filter(PseudoDensities(x)))

Volume constraint

volfrac = TopOpt.Volume(solver)
constr = x -> volfrac(filter(PseudoDensities(x))) - V

Initial design (volume fraction as initial guess for all elements)

x0 = fill(V, length(solver.vars))

Define optimization model

model = Model(obj)
addvar!(model, zeros(length(x0)), ones(length(x0)))
add_ineq_constraint!(model, constr)

Optimization algorithm

alg = MMA87()
convcriteria = Nonconvex.KKTCriteria()
options = MMAOptions(; maxiter=3000, tol=Nonconvex.Tolerance(; x=1e-3, f=1e-3, kkt=0.001), convcriteria)

Create a list to store iteration figures

iteration_figures = []

Optimization loop to collect iteration figures

function optimization_loop()
r = optimize(model, alg, x0; options)
push!(iteration_figures, GeometryBasics.Mesh(problem, r.minimizer))
println("Iteration $(length(iteration_figures)): Objective value = $(obj(r.minimizer))")
return r
end