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Topology optimization of thermal problems in a nonsmooth variational setting: closed-form optimality criteria

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Abstract

This paper extends the nonsmooth Relaxed Variational Approach (RVA) to topology optimization, proposed by the authors in a preceding work, to the solution of thermal optimization problems. First, the RVA topology optimization method is briefly discussed and, then, it is applied to a set of representative problems in which the thermal compliance, the deviation of the heat flux from a given field and the average temperature are minimized. For each optimization problem, the relaxed topological derivative and the corresponding adjoint equations are presented. This set of expressions are then discretized in the context of the finite element method and used in the optimization algorithm to update the characteristic function. Finally, some representative (3D) thermal topology optimization examples are presented to asses the performance of the proposed method and the Relaxed Variational Approach solutions are compared with the ones obtained with the level set method in terms of the cost function, the topology design and the computational cost.

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Notes

  1. The magnitude of the heat source changes according to the material of the point.

  2. Based on a bi-material (soft/hard) approximation, or ersatz approach.

  3. Albeit the name design domain. is commonly used in topology optimization for \(\varOmega \), in this work distinction is made of the analysis domain, the whole domain considered in the analysis, and the design domain, the subset of \(\varOmega \) where the topology is going to be optimized (therefore changed from an initial layout). The reason is that, in some of the considered problems, a certain part of \(\varOmega \) is endowed with a fixed, predetermined, topology thus not being properly part of the design domain.

  4. \(\overline{(\cdot )}\) denotes the closure of the open domain \((\cdot )\).

  5. The characteristic function, \({\chi }\), is considered as the design variable in the topology optimization problem.

  6. Henceforth, the subindex \({\psi }\) of the characteristic function, \({\chi }_{{\psi }}\), will be omitted.

  7. Thus, the single-material and the bi-material formulations converge asymptotically as \(\beta \rightarrow 0\).

  8. The present Cutting&Bisection algorithm is only intended for single constrained topology optimization problems. Furthermore, along this paper, only equality, pseudo-time evolving volume constraints are considered.

  9. The pseudo-energy, \(\xi {(\mathbf{x },\chi )}\), has normally dimensions of energy.

  10. From now on, superscript \((\cdot )^{(h_e)}\) refers to results obtained from approximations via finite element calculations of typical mesh-size \(h_e\).

  11. Shifting and normalization operations in terms of \(\varDelta _{shift}\) and \(\varDelta _{norm}\) (standing, respectively, for the minimum value and the range of \(\xi \) at \(t=0\)) are introduced for the purposes of providing algorithmic time consistency to the problem at \(t=0\). It can be proven that those operations do not alter the problem solution.

  12. \({{{{\varvec{\kappa }}}}}=\kappa \)\( {\mathbf { I}}\) for isotropic conductive materials.

  13. The solution \({\chi }\), resulting from the optimization process, must lie in the subset of admissible solutions, \({\mathscr {U}}_{ad}\), corresponding to the tackled single-material (state) thermal problem (i.e. for \(\beta \rightarrow 0\)). Then, the subset is defined as \({\mathscr {U}}_{ad} = \{ {\chi } \; / \; \varOmega ^+({\chi })\subset \varOmega , \; \partial _{\theta }\varOmega \cap \partial \varOmega ^+{(\chi )}\ne \emptyset , \; \partial _q\varOmega \subset \partial \varOmega ^+{(\chi )}, \; \partial _h\varOmega \subset \partial \varOmega ^+{(\chi )}\}\).

  14. The utopia point \({{{\mathcal {J}}}}_i^\circ \) defined as \({{{\mathcal {J}}}}_i^\circ = \min _{\chi } {{{\mathcal {J}}}}_i({\chi }) \quad \forall {\chi } \in {\mathscr {U}}_{ad}\) is an unattainable optimal point and it may be prohibitively expensive to compute. In these cases, an approximation is used.

  15. The maximum objective function value corresponds either to the maximum value that minimizes the other objective functions, \({{{\mathcal {J}}}}_i^{max}=\max _j {{{\mathcal {J}}}}_i({\chi }_j^*) \quad j\ne i\), or the absolute maximum of \({{{\mathcal {J}}}}_i({\chi })\).

  16. The exponential parameters \(m_i\) are set on the basis of the authors’ experience.

  17. Removing an octant of the total domain as well as the hard material for a better visualization of the topology.

  18. The isotherms for the homogeneous case are vertical, equally spaced, isolines from \(\overline{\theta }_h\) to \(\overline{\theta }_c\).

  19. The comparison is done in terms of the number of iterations, instead of the computational time, as the computational cost per iteration is almost equivalent for the two approaches. Additionally, the number of iterations remains independent of the platform.

  20. The Cutting & Bisection algorithm in Sect. 4 is then replaced by the standard Augmented Lagrangian update, see Eq. (69)c. At convergence, the volume constraint is fulfilled at he prescribed tolerance.

  21. Voigt’s vector/matrix notation is used in what follows.

  22. From now on, the sub-index \(\theta \) of \(\mathbf{N}_{\theta }\) shall be omitted.

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Acknowledgements

This research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Proof of Concept Grant agreement n 874481) through the project “Computational design and prototyping of acoustic metamaterials for target ambient noise reduction” (METACOUSTIC). The authors also acknowledge financial support from the Spanish Ministry of Economy and Competitiveness, through the research grant DPI2017-85521-P for the project “Computational design of Acoustic and Mechanical Metamaterials” (METAMAT) and through the “Severo Ochoa Programme for Centres of Excellence in R&D” (CEX2018-000797-S). D. Yago acknowledges the support received from the Spanish Ministry of Education through the FPU program for PhD grants.

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Appendices

Appendix A: Finite element discretization

The finite element method (FEM) is used to discretize and solve the state-equation (18) and the required adjoint problems. The temperature field in \(\varOmega \) is approximated via \(C_0\) shape functions as followsFootnote 21:

$$\begin{aligned} \varvec{\theta }_{{\chi }}(\mathbf{x })\equiv {\mathbf {N}}_{\theta }(\mathbf{x }){\hat{\varvec{\theta }}}_{\chi } \end{aligned}$$
(A.1)

where \({\mathbf {N}}_{\theta }(\mathbf{x })\) is the, temperature, shape-function matrix and \({{\hat{\varvec{\theta }}}}_{\chi }\) corresponds to the nodal temperature vector. Equivalently, the gradient of \(\varvec{\theta }_{{\chi }}(\mathbf{x })\) is expressed as

$$\begin{aligned} {\varvec{\nabla } \theta }_{{\chi }}(\mathbf{x })\equiv {\mathbf {B}}(\mathbf{x }){{\hat{\varvec{\theta }}}}_{\chi } \end{aligned}$$
(A.2)

where \({\mathbf {B}}(\mathbf{x })\) denotes the gradient matrix. Then, introducing expressions (A.1) and (A.2) into the Fourier’s law, the heat flux, \(\mathbf{{q}}_{\chi }(\mathbf{x })\), can be written as

$$\begin{aligned} \varvec{q}_{{\chi }}(\mathbf{x })\equiv -{{{\varvec{\kappa }}}}_{{\chi }}(\mathbf{x })\ {\mathbf {B}}(\mathbf{x }){{\hat{\varvec{\theta }}}}_{\chi } { \, . }\end{aligned}$$
(A.3)

Finally, the state Eq. (18), once the previous expressions are replaced, yields to

$$\begin{aligned} {\mathbb {K}}_{{\chi }}{{\hat{\varvec{\theta }}}}_{{\chi }}=\mathbf {f} \end{aligned}$$
(A.4)

with

$$\begin{aligned} \left\{ \begin{aligned}&\begin{aligned} {\mathbb {K}}_{{\chi }}=&\int _{\varOmega }{\mathbf {B}}^T(\mathbf{x })\ {{{\varvec{\kappa }}}}_{{\chi }}(\mathbf{x })\ {\mathbf {B}}(\mathbf{x })\,d{\varOmega }\\&-\int _{\partial _h\varOmega }\mathbf {N}_{\theta }^T(\mathbf{x })h\mathbf {N}_{\theta }(\mathbf{x }){ \, d\varGamma }\end{aligned} \\&\begin{aligned} \mathbf {f}=&\int _{\varOmega } \mathbf {N}_{\theta }^T(\mathbf{x }){r}_{{\chi }}(\mathbf{x }){ \, d\varOmega }\\&-\int _{\partial _{q}\varOmega }\mathbf {N}_{\theta } ^T(\mathbf{x }){\overline{q}}(\mathbf{x }){ \, d\varGamma }\\&-\int _{\partial _h\varOmega }\mathbf {N}_{\theta }^T(\mathbf{x })h{\theta _{amb}(\mathbf{x })}{ \, d\varGamma }\end{aligned} \end{aligned} \right. { \, , }\end{aligned}$$
(A.5)

where \(\mathbb {K}_{{\chi }}\) and \(\mathbf {f}\) stand for the stiffness matrix and the external forces vector, respectively.Footnote 22

A Laplacian smoothing is used to smooth the topology, control the filament size and avoid checkerboard patterns. The smooth discrimination function, \({\psi }_\tau \), corresponds to the solution of

$$\begin{aligned} \left\{ \begin{aligned}&{\psi }_{\tau }(\mathbf{x })-\epsilon ^2\varDelta _{{\mathbf{x}}}{\psi }_{\tau }(\mathbf{x })={\psi }(\mathbf{x })&\quad in \; \varOmega \\&\nabla _{{\mathbf{x}}}{\psi }_{\tau }(\mathbf{x })\cdot {\mathbf {n}}={0}&\quad on \; \partial \varOmega \end{aligned} \right. { \, , }\end{aligned}$$
(A.6)

where, \(\varDelta _{{\mathbf{x}}}({{\mathbf{x}}},n)\) and \(\nabla _{{\mathbf{x}}}({{\mathbf{x}}},n)\) stand for the Laplacian and gradient operators, respectively, and \({\mathbf {n}}\) is the outwards normal to the boundary of the analysis domain, \(\partial \varOmega \). The FE discretization of Eq. (A.6), considering \({\psi }_\tau (\mathbf{x })={\mathbf {N}}(\mathbf{x }){\hat{\varvec{\psi }}_\tau }\), leads to the following system

$$\begin{aligned} \hat{\varvec{\psi }}_\tau ={\tilde{\mathbb {G}}}^{-1}{\mathbf{f}}({\psi }) \end{aligned}$$
(A.7)

with

$$\begin{aligned} \left\{ \begin{aligned}&\tilde{\mathbb {G}}=\tilde{\mathbb {M}}+{\epsilon ^{2}}\tilde{\mathbb {K}} \quad \rightarrow \\&\quad \rightarrow \quad \left\{ \begin{aligned}&\tilde{\mathbb {M}}=\int _{\varOmega }{\mathbf{N}}^T(\mathbf{x }){\mathbf {N}}(\mathbf{x }){ \, d\varOmega }\;\\&\tilde{\mathbb {K}}=\int _{\varOmega }\nabla {\mathbf{N}}^T(\mathbf{x }){\nabla {\mathbf{N}}(\mathbf{x }){ \, d\varOmega }}\;\\ \end{aligned} \right.&(a) \\&{\mathbf{f}}({\psi })=\int _\varOmega {{\mathbf {N}}^T(\mathbf{x }){\psi }(\mathbf{x }){ \, d\varOmega }}&(b) \end{aligned} \right. \end{aligned}$$
(A.8)

where \({\mathbf {N}}(\mathbf {x})\) stands for the standard interpolation matrix and \({\varvec{\hat{{\psi }}}}_\tau \) is the vector of nodal values of the field \({\psi }_\tau (\mathbf{x })\).

Appendix B: Thermal compliance minimization: cost function derivative

The topological sensitivity of the thermal compliance optimization problem (Eq. 24) is computed in detail in this section via the adjoint method and the Relaxed Topological Derivative (RTD). Let first rephrase the objective function, \({\mathcal {J}}^{(h_e)}({\chi })\), to incorporate the state Eq. (A.4)

$$\begin{aligned} \overline{\mathcal {J}}^{(h_e)}({\chi })=\frac{1}{2} \mathbf {f}^{T}{{\hat{\varvec{\theta }}}}_{{\chi }}- {\hat{\mathbf {w}}}^T \underbrace{\left( {\mathbb {K}}_{{\chi }}{{\hat{\varvec{\theta }}}}_{{\chi }}-\mathbf {f}\right) } _{{=\mathbf {0}}} { \, , }\end{aligned}$$
(B.1)

where \({\hat{\mathbf {w}}}\) corresponds to the solution of the adjoint state problem, as aforementioned. Computing the RTD of Eq. (B.1) and reordering terms, one arrives to

$$\begin{aligned} \begin{aligned}&\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}= \left( \frac{1}{2}\mathbf {f}^{T}-{\hat{\mathbf {w}}}^T{\mathbb {K}}_{{\chi }}\right) \dfrac{\delta {{\hat{\varvec{\theta }}}}_{{\chi }}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\\&\quad + \left( \frac{1}{2}\dfrac{\delta \mathbf {f}^T _{{\chi }}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}{{\hat{\varvec{\theta }}}}_{{\chi }}-{\hat{\mathbf {w}}}^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{{\hat{\varvec{\theta }}}}_{{\chi }} + {\hat{\mathbf {w}}}^T\dfrac{\delta \mathbf {f}_{{\chi }}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\right) { \, . }\end{aligned} \end{aligned}$$
(B.2)

Substituting \({\hat{\mathbf {w}}}\equiv {\dfrac{1}{2}}{{\hat{\varvec{\theta }}}}_{{\chi }}\) in Eq. (B.2), and considering the state Eq. (A.4), the expression can be simplified to

$$\begin{aligned} \begin{aligned} \dfrac{\delta \overline{\mathcal {J}}^{(h_e)}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}&=\frac{1}{2}\underbrace{(\mathbf {f}^{T}-{{\hat{\varvec{\theta }}}}_{{\chi }}^T{\mathbb {K}}_{{\chi }})} _{{=\mathbf {0}}}\dfrac{\delta {{\hat{\varvec{\theta }}}}_{{\chi }}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\\&\quad +\left( \dfrac{\delta \mathbf {f}^T _{{\chi }}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}{{\hat{\varvec{\theta }}}}_{{\chi }}-{{\hat{\varvec{\theta }}}}_{{\chi }}^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{{\hat{\varvec{\theta }}}}_{{\chi }}\right) \\&=\left[ \dfrac{\delta \mathbf {f}^T _{{\chi }}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}{{\hat{\varvec{\theta }}}}_{{\chi }}- {\hat{\varvec{\theta }}}_{{\chi }}^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}(\mathbf{x }){\hat{\varvec{\theta }}}_{{\chi }}\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}} { \, . }\end{aligned} \end{aligned}$$
(B.3)

Then, considering Eqs. (14)–(17) and replacing the corresponding terms into Eq. (B.3), the Relaxed Topological Derivative of Eq. (B.1) can be expressed as

$$\begin{aligned} \begin{aligned}&\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}=\dfrac{\partial {{r}_{{\chi }}}}{\partial {\chi }}{(\hat{\mathbf{x }})}{\mathbf {N}}{(\hat{\mathbf{x }})}{{\hat{\varvec{\theta }}}}_{{\chi }} \varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\\&\qquad -{{\hat{\varvec{\theta }}}}^T_{{\chi }}{\mathbf {B}}^T{(\hat{\mathbf{x }})}\dfrac{\partial {{{\varvec{\kappa }}}_{{\chi }}}}{\partial {\chi }}{(\hat{\mathbf{x }})}{\mathbf {B}}{(\hat{\mathbf{x }})}{{\hat{\varvec{\theta }}}}_{{\chi }}\varDelta {\chi }_{\kappa }{(\hat{\mathbf{x }})}\\&\quad =\left[ \dfrac{\partial {{r}_{{\chi }}}}{\partial {\chi }}{\mathbf {N}}(\mathbf{x }){{\hat{\varvec{\theta }}}}_{{\chi }} \right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\\&\qquad -\left[ \varvec{\nabla }\theta ^T_{{\chi }}(\mathbf{x })\dfrac{\partial {{\varvec{\kappa }}}_{{\chi }}}{\partial {{\chi }}}\varvec{\nabla }\theta _{{\chi }}(\mathbf{x })\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}\varDelta {\chi }_{\kappa }{(\hat{\mathbf{x }})}\\&\quad =\left[ m_{{r}}{{\chi }}^{m_{{r}}-1}(\mathbf{x }){r}(\mathbf{x }){\mathbf {N}}(\mathbf{x }){{\hat{\varvec{\theta }}}}_{{\chi }} \right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\\&\qquad -\left[ m_{\kappa }{{\chi }}^{m_{\kappa }-1}(\mathbf{x })\varvec{\nabla }\theta ^T_{{\chi }}(\mathbf{x }){{\varvec{\kappa }}}(\mathbf{x })\varvec{\nabla }\theta _{{\chi }}(\mathbf{x })\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}{{\varDelta {\chi }_{\kappa }}({\hat{{\mathbf{x}}}}) } { \, , }\end{aligned} \end{aligned}$$
(B.4)

which is then written in terms of energy densities, to recover Eq. (28), as

$$\begin{aligned} \begin{aligned} \dfrac{\delta {\overline{\mathcal {J}}^{(h_e)}(\theta _{{\chi }})}}{\delta {\chi }}{(\hat{\mathbf{x }})}&=m_{{r}}\left( {{\chi }}_{{r}}{(\hat{\mathbf{x }})}\right) ^{m_{{r}}-1}{\overline{{\mathcal {U}}}_{{r}}}{(\hat{\mathbf{x }})}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\\&\quad -2m_{\kappa } \left( {\chi }_{\kappa }{(\hat{\mathbf{x }})}\right) ^{m_{\kappa }-1}{\overline{{\mathcal {U}}}}{(\hat{\mathbf{x }})}{{\varDelta {\chi }}_{\kappa }({\hat{{\mathbf{x}}}}) } { \, , }\end{aligned} \end{aligned}$$
(B.5)

where \(\overline{{\mathcal {U}}}{(\hat{\mathbf{x }})}\) is the nominal heat conduction energy density and \({\overline{{\mathcal {U}}}_{{r}}}{(\hat{\mathbf{x }})}\) is the nominal heat source energy density, as described in Eq. (29).

Appendix C: Thermal cloaking via heat flux manipulation: cost function derivative

This section describes step-by-step the topological sensitivity computation of the thermal cloaking optimization problem (34), mimicking the procedure explained in “Appendix B”. Let us then define the extended cost function, \(\overline{{\mathcal {J}}}^{(h_e)}({\chi })\), i.e.

$$\begin{aligned} \begin{aligned} \overline{{\mathcal {J}}}^{(h_e)}({\chi })&=\Bigg (\underbrace{\int _{\varOmega } {1_{\varOmega _c}(\mathbf{x })}\left| \mathbf{q}_{{\chi }}\left( {{\mathbf{x}}},{\theta }_{\chi }^{(1)}\right) -{\overline{\mathbf{q}}}(\mathbf{x })\right| ^2 { \, d\varOmega }}_{E\left( {\chi },{\theta }_{\chi }^{(1)}\right) }\Bigg )^\frac{1}{2} \\&\quad -\hat{\mathbf{w}}^T\underbrace{\left( {\mathbb {K}}_{{\chi }}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)}-\mathbf {f}^{(1)}\right) } _{{=\mathbf {0}}} { \, , }\end{aligned} \end{aligned}$$
(C.1)

which is subsequently derived through the RTD, yielding to

$$\begin{aligned} \begin{aligned} \dfrac{\delta \overline{\mathcal {J}}^{(h_e)}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}=&\frac{1}{2}\dfrac{1}{{\mathcal {J}}^{(h_e)}({\chi })}\dfrac{\delta E({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}-{\hat{\mathbf {w}}}^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)} \\&-{\hat{\mathbf {w}}}^T{\mathbb {K}}_{{\chi }}\dfrac{\delta {\hat{\varvec{\theta }}}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}+ {\hat{\mathbf {w}}}^T\dfrac{\delta \mathbf {f}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\end{aligned} \end{aligned}$$
(C.2)

where

$$\begin{aligned} \left\{ \begin{aligned}&\dfrac{\delta E({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}= \left[ 2 \ { {1_{\varOmega _c}(\mathbf{x })}\left( \mathbf{q}_{{\chi }}\left( {{\mathbf{x}}},{\theta }_{\chi }^{(1)}\right) -{\overline{\mathbf{q}}}(\mathbf{x })\right) \dfrac{\delta \mathbf{q}_{\chi }({\chi })}{\delta {\chi }}(\mathbf{x })} \right] _{\mathbf{x }={\hat{\mathbf{x }}}} { \, , }\\&\dfrac{\delta \mathbf{q}_{\chi }({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}= -\dfrac{\delta {{{\varvec{\kappa }}}}_{{\chi }}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}{\varvec{\nabla }\theta }_{{\chi }}^{(1)}{(\hat{\mathbf{x }})}-{{{\varvec{\kappa }}}}_{{\chi }}\nabla \dfrac{\delta {\varvec{\theta }}_{{\chi }} ^{(1)}}{\delta {\chi }}{(\hat{\mathbf{x }})}{ \, . }\end{aligned} \right. \end{aligned}$$
(C.3)

Introducing expressions (C.3) into Eq. (C.2), and manipulating the terms, we obtain

$$\begin{aligned}&\frac{\delta \overline{\mathcal {J}}^{(h_e)}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}\nonumber \\&\quad = { \underbrace{\scriptstyle {\left( -{\hat{\mathbf {w}}}^T{\mathbb {K}}_{{\chi }}-{\mathbf{C_1}}\left( {\chi },{\hat{\mathbf{x }}},{\theta }_{\chi }^{(1)}\right) {{{\varvec{\kappa }}}}_{{\chi }}\nabla \right) }}_{\mathbf{=0}}} \frac{\delta {{\hat{\varvec{\theta }}}}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\nonumber \\&\qquad -{\mathbf{C_1}}\left( {\chi },{\hat{\mathbf{x }}},{\theta }_{\chi }^{(1)}\right) \dfrac{\delta {{{\varvec{\kappa }}}}_{{\chi }}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}{\varvec{\nabla }\theta }^{(1)}_{{\chi }}{(\hat{\mathbf{x }})}\nonumber \\&\qquad -{\hat{\mathbf {w}}}^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)} +{\hat{\mathbf {w}}}^T\dfrac{\delta \mathbf {f}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}{ \, , }\end{aligned}$$
(C.4)

with

$$\begin{aligned} {\mathbf{C_1}}\left( {\chi },{\hat{\mathbf{x }}},{\theta }_{\chi }^{(1)}\right) = \dfrac{{1_{\varOmega _c}{(\hat{\mathbf{x }})}} \left( \mathbf{q}_{{\chi }}\left( {\hat{\mathbf{x }}},{\theta }_{\chi }^{(1)}\right) -{\overline{\mathbf{q}}}{(\hat{\mathbf{x }})}\right) }{{\mathcal {J}}^{(h_e)}({\chi })} { \, . }\end{aligned}$$
(C.5)

Now, the adjoint problem of Eq.  (C.4) is solved for \({{\hat{\mathbf{w}}}}\equiv {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\), leading to

$$\begin{aligned}&\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}= -{\mathbf{C_1}}\left( {\chi },{\hat{\mathbf{x }}},{\theta }_{\chi }^{(1)}\right) \dfrac{\delta {{{\varvec{\kappa }}}}_{{\chi }}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}{\varvec{\nabla }\theta }_{{\chi }}^{(1)}{(\hat{\mathbf{x }})}\nonumber \\&\quad -\left( {{\hat{\varvec{\theta }}}}_{{\chi }}^{(2)}\right) ^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{{\hat{\varvec{\theta }}}}_{{\chi }}^{(1)} + \left( {{\hat{\varvec{\theta }}}}_{{\chi }}^{(2)}\right) ^T\dfrac{\delta \mathbf {f}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}{ \, . }\end{aligned}$$
(C.6)

After applying the RTD to the corresponding terms, Eq. (C.6) reads as

$$\begin{aligned}&\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}=\left[ \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbf {N}}^T(\mathbf{x })\dfrac{\partial {{r}_{{\chi }}}}{\partial {\chi }}(\mathbf{x })\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}} \varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\nonumber \\&\quad -\left[ \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(1)}\right) ^T{\mathbf {B}}^T(\mathbf{x })\dfrac{\partial {{{\varvec{\kappa }}}_{{\chi }}}}{\partial {\chi }}(\mathbf{x }){\mathbf {B}}(\mathbf{x }){\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}\varDelta {\chi }_{\kappa }{(\hat{\mathbf{x }})}\nonumber \\&\quad -\left[ {\mathbf{C_1}}\left( {\chi },{{\mathbf{x}}},{\theta }_{\chi }^{(1)}\right) \dfrac{\partial {{{\varvec{\kappa }}}}_{{\chi }}}{\partial {\chi }}(\mathbf{x }){\mathbf {B}}(\mathbf{x }){\hat{\varvec{\theta }}}_{{\chi }}^{(1)}\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}\varDelta {\chi }_{\kappa }{(\hat{\mathbf{x }})}{ \, . }\end{aligned}$$
(C.7)

Subsequently, relations (14) and (15) are considered in Eq. (C.7), which yields to

$$\begin{aligned}&\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}=\left[ m_{{r}}{{\chi }}^{m_{{r}}-1}\left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbf {N}}^T(\mathbf{x }){r}(\mathbf{x })\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\nonumber \\&\quad -\left[ m_{\kappa }{{\chi }}^{m_{\kappa }-1}\left( \varvec{\nabla }\theta ^{(1)}_{{\chi }}\right) ^T(\mathbf{x }){{\varvec{\kappa }}}(\mathbf{x })\varvec{\nabla }\theta _{{\chi }}^{(2)}(\mathbf{x })\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}{{\varDelta {\chi }_{\kappa }}({\hat{{\mathbf{x}}}}) } \nonumber \\&\quad -\left[ m_{\kappa }{{\chi }}^{m_{\kappa }-1}{\mathbf{C_1}}\left( {\chi },{{\mathbf{x}}},{\theta }_{\chi }^{(1)}\right) {{{\varvec{\kappa }}}}(\mathbf{x }){\mathbf {B}}(\mathbf{x }){\hat{\varvec{\theta }}}_{{\chi }}^{(1)}\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}} \varDelta {\chi }_{\kappa }{(\hat{\mathbf{x }})}{ \, . }\end{aligned}$$
(C.8)

Finally, Eq. (C.8) can be reformulated, in terms of pseudo-energies, as

$$\begin{aligned} \dfrac{\delta \overline{\mathcal {J}}^{(h_e)}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}&= m_{{r}}\left( {{\chi }}_{{r}}{(\hat{\mathbf{x }})}\right) ^{m_{{r}}-1}{\overline{{\mathcal {U}}}_{{r}}}{(\hat{\mathbf{x }})}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\nonumber \\&-2m_{\kappa } \left( {\chi }_{\kappa }{(\hat{\mathbf{x }})}\right) ^{m_{\kappa }-1}{\overline{{\mathcal {U}}}}_{1-2}{(\hat{\mathbf{x }})}{{\varDelta {\chi }}_{\kappa }({\hat{{\mathbf{x}}}}) } \nonumber \\&-m_{\kappa } \left( {\chi }_{\kappa }{(\hat{\mathbf{x }})}\right) ^{m_{\kappa }-1}{\overline{{\mathcal {U}}}}_\mathbf{q}{(\hat{\mathbf{x }})}{{\varDelta {\chi }}_{\kappa }({\hat{{\mathbf{x}}}}) } { \, , }\end{aligned}$$
(C.9)

where \(\overline{{\mathcal {U}}}_{1-2}{(\hat{\mathbf{x }})}\) is the nominal heat conduction energy density, \({\overline{{\mathcal {U}}}_{{r}}}{(\hat{\mathbf{x }})}\) is the nominal heat source energy density and \({\overline{{\mathcal {U}}}}_\mathbf{q}{(\hat{\mathbf{x }})}\) corresponds to the nominal heat flux energy density, as defined in Eq. (43).

Appendix D: Average temperature minimization: cost function derivative

Let us now proceed with the computation of the topological sensitivity of the average temperature minimization problem (50). As before, let \(\overline{\mathcal {J}}^{(h_e)}_{\text {av}}({\chi })\) be the extended cost function, considering the state equation through the Lagrange multiplier vector, \({\hat{\mathbf {w}}}\), defined as

$$\begin{aligned} \overline{\mathcal {J}}^{(h_e)}_{\text {av}}({\chi })=C_2 \mathbf{1}_{\partial _c\varOmega }^T{\hat{\varvec{\theta }}}_{\chi }^{(1)}-\hat{\mathbf{w}}^T\underbrace{\left( {\mathbb {K}}_{{\chi }}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)}-\mathbf {f}^{(1)}\right) } _{{=\mathbf {0}}} { \, , }\end{aligned}$$
(D.1)

where \(C_2=\left( \int _{\partial _c\varOmega } { \, d\varGamma }\right) ^{-1}\).

Applying the RTD to Eq. (D.1) and reordering its terms, one obtains

$$\begin{aligned} \begin{aligned} \dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {av}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}&= \left( -{\hat{\mathbf {w}}}^T{\mathbb {K}}_{{\chi }}+C_2 \mathbf{1}_{\partial _c\varOmega }^T \right) \dfrac{\delta {{\hat{\varvec{\theta }}}}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\\ -&{\hat{\mathbf {w}}}^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)} +{\hat{\mathbf {w}}}^T\dfrac{\delta \mathbf {f}_{{\chi }} ^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}{ \, , }\end{aligned} \end{aligned}$$
(D.2)

which is then simplified by choosing \(\hat{\mathbf{w}}\equiv -C_2 {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\), yielding to

$$\begin{aligned} \begin{aligned} \dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {av}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}&= C_2 \underbrace{\left( \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbb {K}}_{{\chi }}+\mathbf{1}_{\partial _c\varOmega }^T \right) }_{\displaystyle \mathbf{=0}}\dfrac{\delta {{\hat{\varvec{\theta }}}}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\\&\quad \ +C_2 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)} \\&\quad \ -C_2 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T\dfrac{\delta \mathbf {f}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\\&=C_2 \Bigg (\left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)} \\&\quad \ - \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T\dfrac{\delta \mathbf {f}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\Bigg ) { \, . }\end{aligned} \end{aligned}$$
(D.3)

Equation (D.3) is finally discretized using the expressions in Sect. 1, which then reads as

$$\begin{aligned}&\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {av}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}={ C_2 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbf {B}}^T{(\hat{\mathbf{x }})}\dfrac{\partial {{{\varvec{\kappa }}}_{{\chi }}}}{\partial {\chi }}{(\hat{\mathbf{x }})}{\mathbf {B}}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)}\varDelta {\chi }_{\kappa }{(\hat{\mathbf{x }})}}\nonumber \\&\qquad -C_2 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbf {N}}^T{(\hat{\mathbf{x }})}\dfrac{\partial {{r}_{{\chi }}}}{\partial {\chi }}{(\hat{\mathbf{x }})}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\nonumber \\&\quad = {C_2 \left[ m_{\kappa }{{\chi }}^{m_{\kappa }-1}(\mathbf{x })\left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbf {B}}^T(\mathbf{x }){{\varvec{\kappa }}}(\mathbf{x }){\mathbf {B}}(\mathbf{x }){\hat{\varvec{\theta }}}_{{\chi }}^{(1)}\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}{{\varDelta {\chi }_{\kappa }}({\hat{{\mathbf{x}}}}) }} \nonumber \\&\qquad -C_2 \left[ m_{{r}}{{\chi }}^{m_{{r}}-1}(\mathbf{x })\left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbf {N}}^T(\mathbf{x }){r}(\mathbf{x })\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}} \varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}{ \, . }\end{aligned}$$
(D.4)

The Relaxed Topological Derivative of the cost function (50) can be finally expressed in terms of energy densities as

$$\begin{aligned} \begin{aligned} \dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {av}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}&=2 C_2 m_{\kappa } \left( {\chi }_{\kappa }{(\hat{\mathbf{x }})}\right) ^{m_{\kappa }-1}{\overline{{\mathcal {U}}}}_{1-2}{(\hat{\mathbf{x }})}{{\varDelta {\chi }}_{\kappa }({\hat{{\mathbf{x}}}}) } \\&\quad -C_2 m_{{r}}\left( {{\chi }}_{{r}}{(\hat{\mathbf{x }})}\right) ^{m_{{r}}-1}{\overline{{\mathcal {U}}}_{{r}-2}}{(\hat{\mathbf{x }})}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}{ \, , }\end{aligned} \end{aligned}$$
(D.5)

where \(\overline{{\mathcal {U}}}_{1-2}{(\hat{\mathbf{x }})}\) and \({\overline{{\mathcal {U}}}_{{r}-2}}{(\hat{\mathbf{x }})}\) are, respectively, the nominal heat conduction energy density and the nominal heat source energy density, both defined in Eq. (54).

Appendix E: Temperature variance minimization: cost function derivation

Let us now address the corresponding RTD computation of the cost function for the minimization of the temperature variance (Eq. 56), starting by defining the extended cost function as

$$\begin{aligned} \begin{aligned} \overline{\mathcal {J}}^{(h_e)}_{\text {vr}}({\chi })&=C_3 \left( {\mathcal T}_{\chi }\left( \theta _{\chi }^{(1)}\right) \right) ^T \mathbb {M}_{\partial _c\varOmega } {\mathcal T}_{\chi }\left( \theta _{\chi }^{(1)}\right) \\&\quad -\hat{\mathbf{w}}^T\underbrace{\left( {\mathbb {K}}_{{\chi }}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)}-\mathbf {f}^{(1)}\right) }_{{=\mathbf {0}}} { \, , }\end{aligned} \end{aligned}$$
(E.1)

where \({\mathcal T}_{\chi }\left( \theta _{\chi }^{(1)}\right) \) and \(\mathbb {M}_{\partial _c\varOmega }\) are respectively defined as

$$\begin{aligned}&{\mathcal T}_{\chi }\left( \theta _{\chi }^{(1)}\right) = {\hat{\varvec{\theta }}}_{\chi }^{(1)}-{\mathbb {I}}\,{\mathcal {J}}^{(h_e)}_{\text {av}}\left( \theta _{\chi }^{(1)}\right) { \, , }\\&\mathbb {M}_{\partial _c\varOmega } = \int _{\partial \varOmega } \mathbf{N}^T(\mathbf{x }){1}_{\partial _c\varOmega } (\mathbf{x })\mathbf{N}(\mathbf{x }){ \, d\varGamma }{ \, . }\end{aligned}$$

Applying the RTD to Eq. (E.1) and rearranging the expression, one arrives to

$$\begin{aligned}&\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {vr}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}\nonumber \\&\quad = {\overbrace{\left( -{\hat{\mathbf {w}}}^T{\mathbb {K}}_{{\chi }}+2 C_{3} \left( {\mathcal T}_{\chi }\left( \theta _{\chi }^{(1)}\right) \right) ^T \mathbb {M}_{\partial _c\varOmega } \right) }^{\displaystyle \mathbf{=0}}}\dfrac{\delta {{\hat{\varvec{\theta }}}}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\nonumber \\&\qquad -2 C_3 \left( {\mathcal T}_{\chi }\left( \theta _{\chi }^{(1)}\right) \right) ^T \mathbb {M}_{\partial _c\varOmega } {\mathbb {I}} \dfrac{\delta {\mathcal {J}}^{(h_e)}_{\text {av}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}\nonumber \\&\qquad -{\hat{\mathbf {w}}}^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)} +{\hat{\mathbf {w}}}^T\dfrac{\delta \mathbf {f}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}{ \, . }\end{aligned}$$
(E.2)

Then, the adjoint state equation can be readily identified from Eq. (E.2) and solved for \({{\hat{\mathbf{w}}}} \equiv -{C_{3}}{\hat{\varvec{\theta }}}_{{\chi }}^{(3)}\), resulting in

$$\begin{aligned} \begin{aligned} \dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {vr}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}&= -2 C_3 \left( {\mathcal T}_{\chi }\left( \theta _{\chi }^{(1)}\right) \right) ^T \mathbb {M}_{\partial _c\varOmega } {\mathbb {I}} \dfrac{\delta {\mathcal {J}}^{(h_e)}_{\text {av}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}\\&\quad +C_3 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(3)}\right) ^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)} \\&\quad -C_3\left( {\hat{\varvec{\theta }}}_{{\chi }}^{(3)}\right) ^T\dfrac{\delta \mathbf {f}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}{ \, , }\end{aligned} \end{aligned}$$
(E.3)

which can be, after inserting the RTD of \({\mathcal {J}}^{(h_e)}_{\text {av}}({\chi })\) (D.3), expressed as

$$\begin{aligned} \begin{aligned}&\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {vr}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}=-2 C_3 \left( {\mathcal T}_{\chi }\left( \theta _{\chi }^{(1)}\right) \right) ^T \mathbb {M}_{\partial _c\varOmega } {\mathbb {I}} \\&\quad \,\Bigg (C_2 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)} -C_2 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T\dfrac{\delta \mathbf {f}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}\Bigg ) \\&\quad +\,C_3 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(3)}\right) ^T\dfrac{\delta {\mathbb {K}}_{{\chi }}}{\delta {\chi }}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)} -C_3 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(3)}\right) ^T\dfrac{\delta \mathbf {f}_{{\chi }}^{(1)}}{\delta {{\chi }}}{(\hat{\mathbf{x }})}{ \, . }\end{aligned} \end{aligned}$$
(E.4)

Replacing the RTD of the stiffness matrix and the force vector into Eq. (E.4), one arrives to

$$\begin{aligned} \begin{aligned}&\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {vr}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}=-2 C_3 {\mathcal A}\left( \theta _{\chi }^{(1)}\right) \\&\Bigg ( C_2 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbf {B}}^T{(\hat{\mathbf{x }})}\dfrac{\partial {{{\varvec{\kappa }}}_{{\chi }}}}{\partial {\chi }}{(\hat{\mathbf{x }})}{\mathbf {B}}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)}\varDelta {\chi }_{\kappa }{(\hat{\mathbf{x }})}\\&\quad -C_2 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbf {N}}^T{(\hat{\mathbf{x }})}\dfrac{\partial {{r}_{{\chi }}}}{\partial {\chi }}{(\hat{\mathbf{x }})}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\Bigg )\\&\quad +C_3 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(3)}\right) ^T{\mathbf {B}}^T{(\hat{\mathbf{x }})}\dfrac{\partial {{{\varvec{\kappa }}}_{{\chi }}}}{\partial {\chi }}{(\hat{\mathbf{x }})}{\mathbf {B}}{(\hat{\mathbf{x }})}{\hat{\varvec{\theta }}}_{{\chi }}^{(1)}\varDelta {\chi }_{\kappa }{(\hat{\mathbf{x }})}\\&\quad -C_3 \left( {\hat{\varvec{\theta }}}_{{\chi }}^{(3)}\right) ^T{\mathbf {N}}^T{(\hat{\mathbf{x }})}\dfrac{\partial {{r}_{{\chi }}}}{\partial {\chi }}{(\hat{\mathbf{x }})}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}{ \, , }\end{aligned} \end{aligned}$$
(E.5)

where \({\mathcal A}\left( \theta _{\chi }^{(1)}\right) \) is equal to \(\left( {\mathcal T}_{\chi }\left( \theta _{\chi }^{(1)}\right) \right) ^T \mathbb {M}_{\partial _c\varOmega } {\mathbb {I}}\). Now we introduce the definition of the conductivity and the heat source with respect to the topology (Eqs. (14) and (15)) into expression (E.5), yielding to

$$\begin{aligned}&\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {vr}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}=-2 C_3 {\mathcal A}\left( \theta _{\chi }^{(1)}\right) \nonumber \\&\quad \, \Bigg (C_2 \left[ m_{\kappa }{{\chi }}^{m_{\kappa }-1}\left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbf {B}}^T(\mathbf{x }){{\varvec{\kappa }}}(\mathbf{x }){\mathbf {B}}(\mathbf{x }){\hat{\varvec{\theta }}}_{{\chi }}^{(1)}\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}{{\varDelta {\chi }_{\kappa }}({\hat{{\mathbf{x}}}}) }\nonumber \\&\quad -C_2 \left[ m_{{r}}{{\chi }}^{m_{{r}}-1}\left( {\hat{\varvec{\theta }}}_{{\chi }}^{(2)}\right) ^T{\mathbf {N}}^T(\mathbf{x }){r}(\mathbf{x })\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\Bigg )\nonumber \\&\quad +C_3 \left[ m_{\kappa }{{\chi }}^{m_{\kappa }-1}\left( {\hat{\varvec{\theta }}}_{{\chi }}^{(3)}\right) ^T{\mathbf {B}}^T(\mathbf{x }){{\varvec{\kappa }}}(\mathbf{x }){\mathbf {B}}(\mathbf{x }){\hat{\varvec{\theta }}}_{{\chi }}^{(1)}\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}{{\varDelta {\chi }_{\kappa }}({\hat{{\mathbf{x}}}}) } \nonumber \\&\quad -C_3 \left[ m_{{r}}{{\chi }}^{m_{{r}}-1}\left( {\hat{\varvec{\theta }}}_{{\chi }}^{(3)}\right) ^T{\mathbf {N}}^T(\mathbf{x }){r}(\mathbf{x })\right] _{{\mathbf {x}}=\hat{{\mathbf {x}}}}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}{ \, . }\end{aligned}$$
(E.6)

Finally, the sensitivity \(\dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {vr}}({\chi })}{\delta {\chi }}\) at point \(\hat{{\mathbf{x}}}\) can be written as a sum of actual energies, which yields to

$$\begin{aligned} \dfrac{\delta \overline{\mathcal {J}}^{(h_e)}_{\text {vr}}({\chi })}{\delta {\chi }}{(\hat{\mathbf{x }})}&= -4C_3C_2m_{\kappa } \left( {\chi }_{\kappa }{(\hat{\mathbf{x }})}\right) ^{m_{\kappa }-1}{\overline{{\mathcal {U}}}}_{1-2}{(\hat{\mathbf{x }})}{{\varDelta {\chi }}_{\kappa }({\hat{{\mathbf{x}}}}) }\nonumber \\&\quad +C_3C_2m_{{r}}\left( {{\chi }}_{{r}}{(\hat{\mathbf{x }})}\right) ^{m_{{r}}-1}{\overline{{\mathcal {U}}}_{{r}-2}}{(\hat{\mathbf{x }})}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}\nonumber \\&\quad +2C_3m_{\kappa } \left( {\chi }_{\kappa }{(\hat{\mathbf{x }})}\right) ^{m_{\kappa }-1}{\overline{{\mathcal {U}}}}_{1-3}{(\hat{\mathbf{x }})}{{\varDelta {\chi }}_{\kappa }({\hat{{\mathbf{x}}}}) }\nonumber \\&\quad -C_3m_{{r}}\left( {{\chi }}_{{r}}{(\hat{\mathbf{x }})}\right) ^{m_{{r}}-1}{\overline{{\mathcal {U}}}_{{r}-3}}{(\hat{\mathbf{x }})}\varDelta {\chi }_{_{{r}}}{(\hat{\mathbf{x }})}{ \, , }\end{aligned}$$
(E.7)

where \(\overline{{\mathcal {U}}}_{i-j}{(\hat{\mathbf{x }})}\) is the nominal heat conduction energy density for i-th and j-th temperature fields (\(i,j=\{1,2,3\}\)) and \({\overline{{\mathcal {U}}}_{{r}-k}}{(\hat{\mathbf{x }})}\) corresponds to the nominal heat source energy density for the k-th temperature field \((k=\{1,2,3\})\).

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Yago, D., Cante, J., Lloberas-Valls, O. et al. Topology optimization of thermal problems in a nonsmooth variational setting: closed-form optimality criteria. Comput Mech 66, 259–286 (2020). https://doi.org/10.1007/s00466-020-01850-0

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