GitHub

Topology optimization

Keywords: Topology optimization, weak and strong form, non-linear problem, Laplacian, grid topology

Figure 1: Optimization of the bending beam. Evolution of the density for fixed total mass.

Tip

This example is also available as a Jupyter notebook: topology_optimization.ipynb.

Introduction

Topology optimization is the task of finding structures that are mechanically ideal. In this example we cover the bending beam, where we specify a load, boundary conditions and the total mass. Then, our objective is to find the most suitable geometry within the design space minimizing the compliance (i.e. the inverse stiffness) of the structure. We shortly introduce our simplified model for regular meshes. A detailed derivation of the method and advanced techniques can be found in [15] and [16].

We start by introducing the local, elementwise density $\chi \in [\chi_{\text{min}}, 1]$ of the material, where we choose $\chi_{\text{min}}$ slightly above zero to prevent numerical instabilities. Here, $\chi = \chi_{\text{min}}$ means void and $\chi=1$ means bulk material. Then, we use a SIMP ansatz (solid isotropic material with penalization) for the stiffness tensor $C(\chi) = \chi^p C_0$, where $C_0$ is the stiffness of the bulk material. The SIMP exponent $p>1$ ensures that the model prefers the density values void and bulk before the intermediate values. The variational formulation then yields the modified Gibbs energy

\[G = \int_{\Omega} \frac{1}{2} \chi^p \varepsilon : C : \varepsilon \; \text{d}V - \int_{\Omega} \boldsymbol{f} \cdot \boldsymbol{u} \; \text{d}V - \int_{\partial\Omega} \boldsymbol{t} \cdot \boldsymbol{u} \; \text{d}A.\]

Furthermore, we receive the evolution equation of the density and the additional Neumann boundary condition in the strong form

\[p_\chi + \eta \dot{\chi} + \lambda + \gamma - \beta \nabla^2 \chi \ni 0 \quad \forall \textbf{x} \in \Omega,\]

\[\beta \nabla \chi \cdot \textbf{n} = 0 \quad \forall \textbf{x} \in \partial \Omega,\]

with the thermodynamic driving force

\[p_\chi = \frac{1}{2} p \chi^{p-1} \varepsilon : C : \varepsilon.\]

We obtain the mechanical displacement field by applying the Finite Element Method to the weak form of the Gibbs energy using Ferrite. In contrast, we use the evolution equation (i.e. the strong form) to calculate the value of the density field $\chi$. The advantage of this "split" approach is the very high computation speed. The evolution equation consists of the driving force, the damping parameter $\eta$, the regularization parameter $\beta$ times the Laplacian, which is necessary to avoid numerical issues like mesh dependence or checkerboarding, and the constraint parameters $\lambda$, to keep the mass constant, and $\gamma$, to avoid leaving the set $[\chi_{\text{min}}, 1]$. By including gradient regularization, it becomes necessary to calculate the Laplacian. The Finite Difference Method for square meshes with the edge length $\Delta h$ approximates the Laplacian as follows:

\[\nabla^2 \chi_p = \frac{1}{(\Delta h)^2} (\chi_n + \chi_s + \chi_w + \chi_e - 4 \chi_p)\]

Here, the indices refer to the different cardinal directions. Boundary element do not have neighbors in each direction. However, we can calculate the central difference to fulfill Neumann boundary condition. For example, if the element is on the left boundary, we have to fulfill

\[\nabla \chi_p \cdot \textbf{n} = \frac{1}{\Delta h} (\chi_w - \chi_e) = 0\]

from which follows $\chi_w = \chi_e$. Thus for boundary elements we can replace the value for the missing neighbor by the value of the opposite neighbor. In order to find the corresponding neighbor elements, we will make use of Ferrites grid topology funcionalities.

Commented Program

We now solve the problem in Ferrite. What follows is a program spliced with comments. The full program, without comments, can be found in the next section.

First we load all necessary packages.

using Ferrite, SparseArrays, LinearAlgebra, Tensors, Printf

Next, we create a simple square grid of the size 2x1. We apply a fixed Dirichlet boundary condition to the left facet set, called clamped. On the right facet, we create a small set traction, where we will later apply a force in negative y-direction.

function create_grid(n)
    corners = [
        Vec{2}((0.0, 0.0)),
        Vec{2}((2.0, 0.0)),
        Vec{2}((2.0, 1.0)),
        Vec{2}((0.0, 1.0)),
    ]
    grid = generate_grid(Quadrilateral, (2 * n, n), corners)

    # node-/facesets for boundary conditions
    addnodeset!(grid, "clamped", x -> x[1] ≈ 0.0)
    addfacetset!(grid, "traction", x -> x[1] ≈ 2.0 && norm(x[2] - 0.5) <= 0.05)
    return grid
end

Next, we create the FE values, the DofHandler and the Dirichlet boundary condition.

function create_values()
    # quadrature rules
    qr = QuadratureRule{RefQuadrilateral}(2)
    facet_qr = FacetQuadratureRule{RefQuadrilateral}(2)

    # cell and facetvalues for u
    ip = Lagrange{RefQuadrilateral, 1}()^2
    cellvalues = CellValues(qr, ip)
    facetvalues = FacetValues(facet_qr, ip)

    return cellvalues, facetvalues
end

function create_dofhandler(grid)
    dh = DofHandler(grid)
    add!(dh, :u, Lagrange{RefQuadrilateral, 1}()^2) # displacement
    close!(dh)
    return dh
end

function create_bc(dh)
    dbc = ConstraintHandler(dh)
    add!(dbc, Dirichlet(:u, getnodeset(dh.grid, "clamped"), (x, t) -> zero(Vec{2}), [1, 2]))
    close!(dbc)
    t = 0.0
    update!(dbc, t)
    return dbc
end

Now, we define a struct to store all necessary material parameters (stiffness tensor of the bulk material and the parameters for topology optimization) and add a constructor to the struct to initialize it by using the common material parameters Young's modulus and Poisson number.

struct MaterialParameters{T, S <: SymmetricTensor{4, 2, T}}
    C::S
    χ_min::T
    p::T
    β::T
    η::T
end

function MaterialParameters(E, ν, χ_min, p, β, η)
    δ(i, j) = i == j ? 1.0 : 0.0 # helper function

    G = E / 2(1 + ν) # =μ
    λ = E * ν / (1 - ν^2) # correction for plane stress included

    C = SymmetricTensor{4, 2}((i, j, k, l) -> λ * δ(i, j) * δ(k, l) + G * (δ(i, k) * δ(j, l) + δ(i, l) * δ(j, k)))
    return MaterialParameters(C, χ_min, p, β, η)
end

To store the density and the strain required to calculate the driving forces, we create the struct MaterialState. We add a constructor to initialize the struct. The function update_material_states! updates the density values once we calculated the new values.

mutable struct MaterialState{T, S <: AbstractArray{SymmetricTensor{2, 2, T, 3}, 1}}
    χ::T # density
    ε::S # strain in each quadrature point
end

function MaterialState(ρ, n_qp)
    return MaterialState(ρ, Array{SymmetricTensor{2, 2, Float64, 3}, 1}(undef, n_qp))
end

function update_material_states!(χn1, states, dh)
    for (element, state) in zip(CellIterator(dh), states)
        state.χ = χn1[cellid(element)]
    end
    return
end

Next, we define a function to calculate the driving forces for all elements. For this purpose, we iterate through all elements and calculate the average strain in each element. Then, we compute the driving force from the formula introduced at the beginning. We create a second function to collect the density in each element.

function compute_driving_forces(states, mp, dh, χn)
    pΨ = zeros(length(states))
    for (element, state) in zip(CellIterator(dh), states)
        i = cellid(element)
        ε = sum(state.ε) / length(state.ε) # average element strain
        pΨ[i] = 1 / 2 * mp.p * χn[i]^(mp.p - 1) * (ε ⊡ mp.C ⊡ ε)
    end
    return pΨ
end

function compute_densities(states, dh)
    χn = zeros(length(states))
    for (element, state) in zip(CellIterator(dh), states)
        i = cellid(element)
        χn[i] = state.χ
    end
    return χn
end

For the Laplacian we need some neighboorhood information which is constant throughout the analysis so we compute it once and cache it. We iterate through each facet of each element, obtaining the neighboring element by using the getneighborhood function. For boundary facets, the function call will return an empty object. In that case we use the dictionary to instead find the opposite facet, as discussed in the introduction.

function cache_neighborhood(dh, topology)
    nbgs = Vector{Vector{Int}}(undef, getncells(dh.grid))
    _nfacets = nfacets(dh.grid.cells[1])
    opp = Dict(1 => 3, 2 => 4, 3 => 1, 4 => 2)

    for element in CellIterator(dh)
        nbg = zeros(Int, _nfacets)
        i = cellid(element)
        for j in 1:_nfacets
            nbg_cellid = getneighborhood(topology, dh.grid, FacetIndex(i, j))
            if !isempty(nbg_cellid)
                nbg[j] = first(nbg_cellid)[1] # assuming only one facet neighbor per cell
            else # boundary facet
                nbg[j] = first(getneighborhood(topology, dh.grid, FacetIndex(i, opp[j])))[1]
            end
        end

        nbgs[i] = nbg
    end

    return nbgs
end

Now we calculate the Laplacian using the previously cached neighboorhood information.

function approximate_laplacian(nbgs, χn, Δh)
    ∇²χ = zeros(length(nbgs))
    for i in 1:length(nbgs)
        nbg = nbgs[i]
        ∇²χ[i] = (χn[nbg[1]] + χn[nbg[2]] + χn[nbg[3]] + χn[nbg[4]] - 4 * χn[i]) / (Δh^2)
    end

    return ∇²χ
end

For the iterative computation of the solution, a function is needed to update the densities in each element. To ensure that the mass is kept constant, we have to calculate the constraint parameter $\lambda$, which we do via the bisection method. We repeat the calculation until the difference between the average density (calculated from the element-wise trial densities) and the target density nearly vanishes. By using the extremal values of $\Delta \chi$ as the starting interval, we guarantee that the method converges eventually.

function compute_χn1(χn, Δχ, ρ, ηs, χ_min)
    n_el = length(χn)

    χ_trial = zeros(n_el)
    ρ_trial = 0.0

    λ_lower = minimum(Δχ) - ηs
    λ_upper = maximum(Δχ) + ηs
    λ_trial = 0.0

    while abs(ρ - ρ_trial) > 1.0e-7
        for i in 1:n_el
            Δχt = 1 / ηs * (Δχ[i] - λ_trial)
            χ_trial[i] = max(χ_min, min(1.0, χn[i] + Δχt))
        end

        ρ_trial = 0.0
        for i in 1:n_el
            ρ_trial += χ_trial[i] / n_el
        end

        if ρ_trial > ρ
            λ_lower = λ_trial
        elseif ρ_trial < ρ
            λ_upper = λ_trial
        end
        λ_trial = 1 / 2 * (λ_upper + λ_lower)
    end

    return χ_trial
end

Lastly, we use the following helper function to compute the average driving force, which is later used to normalize the driving forces. This makes the used material parameters and numerical parameters independent of the problem.

function compute_average_driving_force(mp, pΨ, χn)
    n = length(pΨ)
    w = zeros(n)

    for i in 1:n
        w[i] = (χn[i] - mp.χ_min) * (1 - χn[i])
    end

    p_Ω = sum(w .* pΨ) / sum(w) # average driving force

    return p_Ω
end

Finally, we put everything together to update the density. The loop ensures the stability of the updated solution.

function update_density(dh, states, mp, ρ, neighboorhoods, Δh)
    n_j = Int(ceil(6 * mp.β / (mp.η * Δh^2))) # iterations needed for stability
    χn = compute_densities(states, dh) # old density field
    χn1 = zeros(length(χn))

    for j in 1:n_j
        ∇²χ = approximate_laplacian(neighboorhoods, χn, Δh) # Laplacian
        pΨ = compute_driving_forces(states, mp, dh, χn) # driving forces
        p_Ω = compute_average_driving_force(mp, pΨ, χn) # average driving force

        Δχ = pΨ / p_Ω + mp.β * ∇²χ

        χn1 = compute_χn1(χn, Δχ, ρ, mp.η, mp.χ_min)

        if j < n_j
            χn[:] = χn1[:]
        end
    end

    return χn1
end

Now, we move on to the Finite Element part of the program. We use the following function to assemble our linear system.

function doassemble!(cellvalues::CellValues, facetvalues::FacetValues, K::SparseMatrixCSC, grid::Grid, dh::DofHandler, mp::MaterialParameters, u, states)
    r = zeros(ndofs(dh))
    assembler = start_assemble(K, r)
    nu = getnbasefunctions(cellvalues)

    re = zeros(nu) # local residual vector
    Ke = zeros(nu, nu) # local stiffness matrix

    for (element, state) in zip(CellIterator(dh), states)
        fill!(Ke, 0)
        fill!(re, 0)

        eldofs = celldofs(element)
        ue = u[eldofs]

        elmt!(Ke, re, element, cellvalues, facetvalues, grid, mp, ue, state)
        assemble!(assembler, celldofs(element), Ke, re)
    end

    return K, r
end

The element routine is used to calculate the elementwise stiffness matrix and the residual. In contrast to a purely elastomechanic problem, for topology optimization we additionally use our material state to receive the density value of the element and to store the strain at each quadrature point.

function elmt!(Ke, re, element, cellvalues, facetvalues, grid, mp, ue, state)
    n_basefuncs = getnbasefunctions(cellvalues)
    reinit!(cellvalues, element)
    χ = state.χ

    # We only assemble lower half triangle of the stiffness matrix and then symmetrize it.
    @inbounds for q_point in 1:getnquadpoints(cellvalues)
        dΩ = getdetJdV(cellvalues, q_point)
        state.ε[q_point] = function_symmetric_gradient(cellvalues, q_point, ue)

        for i in 1:n_basefuncs
            δεi = shape_symmetric_gradient(cellvalues, q_point, i)
            for j in 1:i
                δεj = shape_symmetric_gradient(cellvalues, q_point, j)
                Ke[i, j] += (χ)^(mp.p) * (δεi ⊡ mp.C ⊡ δεj) * dΩ
            end
            re[i] += (-δεi ⊡ ((χ)^(mp.p) * mp.C ⊡ state.ε[q_point])) * dΩ
        end
    end

    symmetrize_lower!(Ke)

    @inbounds for facet in 1:nfacets(getcells(grid, cellid(element)))
        if (cellid(element), facet) ∈ getfacetset(grid, "traction")
            reinit!(facetvalues, element, facet)
            t = Vec((0.0, -1.0)) # force pointing downwards
            for q_point in 1:getnquadpoints(facetvalues)
                dΓ = getdetJdV(facetvalues, q_point)
                for i in 1:n_basefuncs
                    δu = shape_value(facetvalues, q_point, i)
                    re[i] += (δu ⋅ t) * dΓ
                end
            end
        end
    end
    return
end

function symmetrize_lower!(K)
    for i in 1:size(K, 1)
        for j in (i + 1):size(K, 1)
            K[i, j] = K[j, i]
        end
    end
    return
end

We put everything together in the main function. Here the user may choose the radius parameter, which is related to the regularization parameter as $\beta = ra^2$, the starting density, the number of elements in vertical direction and finally the name of the output. Additionally, the user may choose whether only the final design (default) or every iteration step is saved.

First, we compute the material parameters and create the grid, DofHandler, boundary condition and FE values. Then we prepare the iterative Newton-Raphson method by pre-allocating all important vectors. Furthermore, we create material states for each element and construct the topology of the grid.

During each iteration step, first we solve our FE problem in the Newton-Raphson loop. With the solution of the elastomechanic problem, we check for convergence of our topology design. The criteria has to be fulfilled twice in a row to avoid oscillations. If no convergence is reached yet, we update our design and prepare the next iteration step. Finally, we output the results in paraview and calculate the relative stiffness of the final design, i.e. how much how the stiffness increased compared to the starting point.

function topopt(ra, ρ, n, filename; output = :false)
    # material
    mp = MaterialParameters(210.0e3, 0.3, 1.0e-3, 3.0, ra^2, 15.0)

    # grid, dofhandler, boundary condition
    grid = create_grid(n)
    dh = create_dofhandler(grid)
    Δh = 1 / n # element edge length
    dbc = create_bc(dh)

    # cellvalues
    cellvalues, facetvalues = create_values()

    # Pre-allocate solution vectors, etc.
    n_dofs = ndofs(dh) # total number of dofs
    u = zeros(n_dofs) # solution vector
    un = zeros(n_dofs) # previous solution vector

    Δu = zeros(n_dofs)  # previous displacement correction
    ΔΔu = zeros(n_dofs) # new displacement correction

    # create material states
    states = [MaterialState(ρ, getnquadpoints(cellvalues)) for _ in 1:getncells(dh.grid)]

    χ = zeros(getncells(dh.grid))

    r = zeros(n_dofs) # residual
    K = allocate_matrix(dh) # stiffness matrix

    i_max = 300 ## maximum number of iteration steps
    tol = 1.0e-4
    compliance = 0.0
    compliance_0 = 0.0
    compliance_n = 0.0
    conv = :false

    topology = ExclusiveTopology(grid)
    neighboorhoods = cache_neighborhood(dh, topology)

    # Newton-Raphson loop
    NEWTON_TOL = 1.0e-8
    print("\n Starting Newton iterations\n")

    for it in 1:i_max
        apply_zero!(u, dbc)
        newton_itr = -1

        while true
            newton_itr += 1

            if newton_itr > 10
                error("Reached maximum Newton iterations, aborting")
                break
            end

            # current guess
            u .= un .+ Δu
            K, r = doassemble!(cellvalues, facetvalues, K, grid, dh, mp, u, states)
            norm_r = norm(r[Ferrite.free_dofs(dbc)])

            if (norm_r) < NEWTON_TOL
                break
            end

            apply_zero!(K, r, dbc)
            ΔΔu = Symmetric(K) \ r

            apply_zero!(ΔΔu, dbc)
            Δu .+= ΔΔu
        end # of loop while NR-Iteration

        # calculate compliance
        compliance = 1 / 2 * u' * K * u

        if it == 1
            compliance_0 = compliance
        end

        # check convergence criterium (twice!)
        if abs(compliance - compliance_n) / compliance < tol
            if conv
                println("Converged at iteration number: ", it)
                break
            else
                conv = :true
            end
        else
            conv = :false
        end

        # update density
        χ = update_density(dh, states, mp, ρ, neighboorhoods, Δh)

        # update old displacement, density and compliance
        un .= u
        Δu .= 0.0
        update_material_states!(χ, states, dh)
        compliance_n = compliance

        # output during calculation
        if output
            i = @sprintf("%3.3i", it)
            filename_it = string(filename, "_", i)

            VTKGridFile(filename_it, grid) do vtk
                write_cell_data(vtk, χ, "density")
            end
        end
    end

    # export converged results
    if !output
        VTKGridFile(filename, grid) do vtk
            write_cell_data(vtk, χ, "density")
        end
    end
    @printf "Rel. stiffness: %.4f \n" compliance^(-1) / compliance_0^(-1)

    return
end

Lastly, we call our main function and compare the results. To create the complete output with all iteration steps, it is possible to set the output parameter to true.

grid, χ =topopt(0.02, 0.5, 60, "small_radius"; output=:false);

@time topopt(0.03, 0.5, 60, "large_radius"; output = :false);
#topopt(0.02, 0.5, 60, "topopt_animation"; output=:true); # can be used to create animations

 Starting Newton iterations
Converged at iteration number: 65
Rel. stiffness: 4.8466
  4.835852 seconds (2.23 M allocations: 2.005 GiB, 10.08% gc time, 1.93% compilation time)

We observe, that the stiffness for the lower value of $ra$ is higher, but also requires more iterations until convergence and finer structures to be manufactured, as can be seen in Figure 2:

Figure 2: Optimization results of the bending beam for smaller (left) and larger (right) value of the regularization parameter $\beta$.

To prove mesh independence, the user could vary the mesh resolution and compare the results.

References

[15]
[16]

Plain program

Here follows a version of the program without any comments. The file is also available here: topology_optimization.jl.

using Ferrite, SparseArrays, LinearAlgebra, Tensors, Printf

function create_grid(n)
    corners = [
        Vec{2}((0.0, 0.0)),
        Vec{2}((2.0, 0.0)),
        Vec{2}((2.0, 1.0)),
        Vec{2}((0.0, 1.0)),
    ]
    grid = generate_grid(Quadrilateral, (2 * n, n), corners)

    # node-/facesets for boundary conditions
    addnodeset!(grid, "clamped", x -> x[1] ≈ 0.0)
    addfacetset!(grid, "traction", x -> x[1] ≈ 2.0 && norm(x[2] - 0.5) <= 0.05)
    return grid
end

function create_values()
    # quadrature rules
    qr = QuadratureRule{RefQuadrilateral}(2)
    facet_qr = FacetQuadratureRule{RefQuadrilateral}(2)

    # cell and facetvalues for u
    ip = Lagrange{RefQuadrilateral, 1}()^2
    cellvalues = CellValues(qr, ip)
    facetvalues = FacetValues(facet_qr, ip)

    return cellvalues, facetvalues
end

function create_dofhandler(grid)
    dh = DofHandler(grid)
    add!(dh, :u, Lagrange{RefQuadrilateral, 1}()^2) # displacement
    close!(dh)
    return dh
end

function create_bc(dh)
    dbc = ConstraintHandler(dh)
    add!(dbc, Dirichlet(:u, getnodeset(dh.grid, "clamped"), (x, t) -> zero(Vec{2}), [1, 2]))
    close!(dbc)
    t = 0.0
    update!(dbc, t)
    return dbc
end

struct MaterialParameters{T, S <: SymmetricTensor{4, 2, T}}
    C::S
    χ_min::T
    p::T
    β::T
    η::T
end

function MaterialParameters(E, ν, χ_min, p, β, η)
    δ(i, j) = i == j ? 1.0 : 0.0 # helper function

    G = E / 2(1 + ν) # =μ
    λ = E * ν / (1 - ν^2) # correction for plane stress included

    C = SymmetricTensor{4, 2}((i, j, k, l) -> λ * δ(i, j) * δ(k, l) + G * (δ(i, k) * δ(j, l) + δ(i, l) * δ(j, k)))
    return MaterialParameters(C, χ_min, p, β, η)
end

mutable struct MaterialState{T, S <: AbstractArray{SymmetricTensor{2, 2, T, 3}, 1}}
    χ::T # density
    ε::S # strain in each quadrature point
end

function MaterialState(ρ, n_qp)
    return MaterialState(ρ, Array{SymmetricTensor{2, 2, Float64, 3}, 1}(undef, n_qp))
end

function update_material_states!(χn1, states, dh)
    for (element, state) in zip(CellIterator(dh), states)
        state.χ = χn1[cellid(element)]
    end
    return
end

function compute_driving_forces(states, mp, dh, χn)
    pΨ = zeros(length(states))
    for (element, state) in zip(CellIterator(dh), states)
        i = cellid(element)
        ε = sum(state.ε) / length(state.ε) # average element strain
        pΨ[i] = 1 / 2 * mp.p * χn[i]^(mp.p - 1) * (ε ⊡ mp.C ⊡ ε)
    end
    return pΨ
end

function compute_densities(states, dh)
    χn = zeros(length(states))
    for (element, state) in zip(CellIterator(dh), states)
        i = cellid(element)
        χn[i] = state.χ
    end
    return χn
end

function cache_neighborhood(dh, topology)
    nbgs = Vector{Vector{Int}}(undef, getncells(dh.grid))
    _nfacets = nfacets(dh.grid.cells[1])
    opp = Dict(1 => 3, 2 => 4, 3 => 1, 4 => 2)

    for element in CellIterator(dh)
        nbg = zeros(Int, _nfacets)
        i = cellid(element)
        for j in 1:_nfacets
            nbg_cellid = getneighborhood(topology, dh.grid, FacetIndex(i, j))
            if !isempty(nbg_cellid)
                nbg[j] = first(nbg_cellid)[1] # assuming only one facet neighbor per cell
            else # boundary facet
                nbg[j] = first(getneighborhood(topology, dh.grid, FacetIndex(i, opp[j])))[1]
            end
        end

        nbgs[i] = nbg
    end

    return nbgs
end

function approximate_laplacian(nbgs, χn, Δh)
    ∇²χ = zeros(length(nbgs))
    for i in 1:length(nbgs)
        nbg = nbgs[i]
        ∇²χ[i] = (χn[nbg[1]] + χn[nbg[2]] + χn[nbg[3]] + χn[nbg[4]] - 4 * χn[i]) / (Δh^2)
    end

    return ∇²χ
end

function compute_χn1(χn, Δχ, ρ, ηs, χ_min)
    n_el = length(χn)

    χ_trial = zeros(n_el)
    ρ_trial = 0.0

    λ_lower = minimum(Δχ) - ηs
    λ_upper = maximum(Δχ) + ηs
    λ_trial = 0.0

    while abs(ρ - ρ_trial) > 1.0e-7
        for i in 1:n_el
            Δχt = 1 / ηs * (Δχ[i] - λ_trial)
            χ_trial[i] = max(χ_min, min(1.0, χn[i] + Δχt))
        end

        ρ_trial = 0.0
        for i in 1:n_el
            ρ_trial += χ_trial[i] / n_el
        end

        if ρ_trial > ρ
            λ_lower = λ_trial
        elseif ρ_trial < ρ
            λ_upper = λ_trial
        end
        λ_trial = 1 / 2 * (λ_upper + λ_lower)
    end

    return χ_trial
end

function compute_average_driving_force(mp, pΨ, χn)
    n = length(pΨ)
    w = zeros(n)

    for i in 1:n
        w[i] = (χn[i] - mp.χ_min) * (1 - χn[i])
    end

    p_Ω = sum(w .* pΨ) / sum(w) # average driving force

    return p_Ω
end

function update_density(dh, states, mp, ρ, neighboorhoods, Δh)
    n_j = Int(ceil(6 * mp.β / (mp.η * Δh^2))) # iterations needed for stability
    χn = compute_densities(states, dh) # old density field
    χn1 = zeros(length(χn))

    for j in 1:n_j
        ∇²χ = approximate_laplacian(neighboorhoods, χn, Δh) # Laplacian
        pΨ = compute_driving_forces(states, mp, dh, χn) # driving forces
        p_Ω = compute_average_driving_force(mp, pΨ, χn) # average driving force

        Δχ = pΨ / p_Ω + mp.β * ∇²χ

        χn1 = compute_χn1(χn, Δχ, ρ, mp.η, mp.χ_min)

        if j < n_j
            χn[:] = χn1[:]
        end
    end

    return χn1
end

function doassemble!(cellvalues::CellValues, facetvalues::FacetValues, K::SparseMatrixCSC, grid::Grid, dh::DofHandler, mp::MaterialParameters, u, states)
    r = zeros(ndofs(dh))
    assembler = start_assemble(K, r)
    nu = getnbasefunctions(cellvalues)

    re = zeros(nu) # local residual vector
    Ke = zeros(nu, nu) # local stiffness matrix

    for (element, state) in zip(CellIterator(dh), states)
        fill!(Ke, 0)
        fill!(re, 0)

        eldofs = celldofs(element)
        ue = u[eldofs]

        elmt!(Ke, re, element, cellvalues, facetvalues, grid, mp, ue, state)
        assemble!(assembler, celldofs(element), Ke, re)
    end

    return K, r
end

function elmt!(Ke, re, element, cellvalues, facetvalues, grid, mp, ue, state)
    n_basefuncs = getnbasefunctions(cellvalues)
    reinit!(cellvalues, element)
    χ = state.χ

    # We only assemble lower half triangle of the stiffness matrix and then symmetrize it.
    @inbounds for q_point in 1:getnquadpoints(cellvalues)
        dΩ = getdetJdV(cellvalues, q_point)
        state.ε[q_point] = function_symmetric_gradient(cellvalues, q_point, ue)

        for i in 1:n_basefuncs
            δεi = shape_symmetric_gradient(cellvalues, q_point, i)
            for j in 1:i
                δεj = shape_symmetric_gradient(cellvalues, q_point, j)
                Ke[i, j] += (χ)^(mp.p) * (δεi ⊡ mp.C ⊡ δεj) * dΩ
            end
            re[i] += (-δεi ⊡ ((χ)^(mp.p) * mp.C ⊡ state.ε[q_point])) * dΩ
        end
    end

    symmetrize_lower!(Ke)

    @inbounds for facet in 1:nfacets(getcells(grid, cellid(element)))
        if (cellid(element), facet) ∈ getfacetset(grid, "traction")
            reinit!(facetvalues, element, facet)
            t = Vec((0.0, -1.0)) # force pointing downwards
            for q_point in 1:getnquadpoints(facetvalues)
                dΓ = getdetJdV(facetvalues, q_point)
                for i in 1:n_basefuncs
                    δu = shape_value(facetvalues, q_point, i)
                    re[i] += (δu ⋅ t) * dΓ
                end
            end
        end
    end
    return
end

function symmetrize_lower!(K)
    for i in 1:size(K, 1)
        for j in (i + 1):size(K, 1)
            K[i, j] = K[j, i]
        end
    end
    return
end

function topopt(ra, ρ, n, filename; output = :false)
    # material
    mp = MaterialParameters(210.0e3, 0.3, 1.0e-3, 3.0, ra^2, 15.0)

    # grid, dofhandler, boundary condition
    grid = create_grid(n)
    dh = create_dofhandler(grid)
    Δh = 1 / n # element edge length
    dbc = create_bc(dh)

    # cellvalues
    cellvalues, facetvalues = create_values()

    # Pre-allocate solution vectors, etc.
    n_dofs = ndofs(dh) # total number of dofs
    u = zeros(n_dofs) # solution vector
    un = zeros(n_dofs) # previous solution vector

    Δu = zeros(n_dofs)  # previous displacement correction
    ΔΔu = zeros(n_dofs) # new displacement correction

    # create material states
    states = [MaterialState(ρ, getnquadpoints(cellvalues)) for _ in 1:getncells(dh.grid)]

    χ = zeros(getncells(dh.grid))

    r = zeros(n_dofs) # residual
    K = allocate_matrix(dh) # stiffness matrix

    i_max = 300 ## maximum number of iteration steps
    tol = 1.0e-4
    compliance = 0.0
    compliance_0 = 0.0
    compliance_n = 0.0
    conv = :false

    topology = ExclusiveTopology(grid)
    neighboorhoods = cache_neighborhood(dh, topology)

    # Newton-Raphson loop
    NEWTON_TOL = 1.0e-8
    print("\n Starting Newton iterations\n")

    for it in 1:i_max
        apply_zero!(u, dbc)
        newton_itr = -1

        while true
            newton_itr += 1

            if newton_itr > 10
                error("Reached maximum Newton iterations, aborting")
                break
            end

            # current guess
            u .= un .+ Δu
            K, r = doassemble!(cellvalues, facetvalues, K, grid, dh, mp, u, states)
            norm_r = norm(r[Ferrite.free_dofs(dbc)])

            if (norm_r) < NEWTON_TOL
                break
            end

            apply_zero!(K, r, dbc)
            ΔΔu = Symmetric(K) \ r

            apply_zero!(ΔΔu, dbc)
            Δu .+= ΔΔu
        end # of loop while NR-Iteration

        # calculate compliance
        compliance = 1 / 2 * u' * K * u

        if it == 1
            compliance_0 = compliance
        end

        # check convergence criterium (twice!)
        if abs(compliance - compliance_n) / compliance < tol
            if conv
                println("Converged at iteration number: ", it)
                break
            else
                conv = :true
            end
        else
            conv = :false
        end

        # update density
        χ = update_density(dh, states, mp, ρ, neighboorhoods, Δh)

        # update old displacement, density and compliance
        un .= u
        Δu .= 0.0
        update_material_states!(χ, states, dh)
        compliance_n = compliance

        # output during calculation
        if output
            i = @sprintf("%3.3i", it)
            filename_it = string(filename, "_", i)

            VTKGridFile(filename_it, grid) do vtk
                write_cell_data(vtk, χ, "density")
            end
        end
    end

    # export converged results
    if !output
        VTKGridFile(filename, grid) do vtk
            write_cell_data(vtk, χ, "density")
        end
    end
    @printf "Rel. stiffness: %.4f \n" compliance^(-1) / compliance_0^(-1)

    return
end

@time topopt(0.03, 0.5, 60, "large_radius"; output = :false);
#topopt(0.02, 0.5, 60, "topopt_animation"; output=:true); # can be used to create animations

This page was generated using Literate.jl.