- numpy - scipy - matplotlib - numpy-stl - nlopt - paths: - ./topopt/run.py - ./topopt/topopt.py - ./topopt/stl_utils.py - ./topopt/utils.py - ./topopt/example.json - ./topopt/fourier.py - ./topopt/bte.py - ./topopt/mapping.py

# HeatOpt: Fun with topology optimization for heat transport

Giuseppe Romano

Massachusetts Institute of Technology

The ability of a material to conduct heat depends on its geometry and chemical composition. In infinite systems, this property is described by the bulk thermal conductivity tensor, $$\kappa$$. In non-homogenous materials the effective thermal conductivity tensor, $$\kappa_{\mathrm{eff}}$$, is computed by solving Fourier's law and the steady-state continuity equation, \begin{equation}\label{eq:fourier} \nabla \cdot \left[\kappa_{\mathrm{bulk}} \nabla T(\mathbf{r}) \right] = 0. \end{equation} In practice, given a geometry, $$\kappa_{\mathrm{eff}}$$ is evaluated by solving Eq. \eqref{eq:fourier} for different directions of the applied temperature gradient. Once Eq. \eqref{eq:fourier} is discretized in space (here we use the finite-volume method), it can be casted into the linear equation $$\mathbf{A} \mathbf{x}= \mathbf{b}$$. Finally, the effective thermal conductivity is obtained by $$\kappa = \alpha \mathbf{b}^T \mathbf{A}^{-1}\mathbf{b}$$, with $$\alpha$$ a normalization constant. In this App, we are interested in inverse design, i.e. the identification of a material with a prescribed thermal conductivity, $$\tilde{\kappa}$$. To this end, we adopt a density-based topology optimization approach, where a material is described in terms of pixels and a fictitious density, $$\boldsymbol \rho$$. The optimization algorithm reads \begin{eqnarray}\label{eq:opt} \min_{\boldsymbol \rho} g(\boldsymbol \rho) \nonumber \\ 0\le \boldsymbol \rho \le 1 \end{eqnarray} Where the cost function is the Frobenius norm $$g(\boldsymbol \rho) = ||\kappa- \tilde{\kappa}||$$.

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App. 1 Specify the components of the prescribed thermal conductivity tensor. The structures are assumed to have four-fold symmetry. STL files are read by common 3D-printing software.
The optimization requires the sensitivity of $$g$$ with respect to $$\boldsymbol \rho$$, \begin{equation} \frac{\partial g }{\partial \mathbf{\rho} } = \frac{1}{g}\left[\Delta \kappa_{xx}\frac{\partial \Delta \kappa_{xx}}{\partial \boldsymbol \rho}+\Delta \kappa_{yy}\frac{\partial \Delta \kappa_{yy}}{\partial \boldsymbol \rho} \right], \end{equation} where $$\Delta \kappa_{ii} = \tilde{\kappa}_{ii} - \kappa_{ii}$$. To proceed with optimization, we thus need to evaluate $$\frac{\partial \kappa_{ii}}{\partial \boldsymbol \rho}$$. To this end, we use the adjoint method, which is derived by differentiating analytically the linear system mentioned above with respect to $$\boldsymbol \rho$$. As a result, the gradient can be computed by solving just an additional linear system, which is the adjoint of the original one. As detailed here, in our case the adjoint solution is simply the solution of the original one with minus sign. The resulting gradient is \begin{equation} \frac{d \kappa_{ii}}{d \boldsymbol \rho} = \frac{\partial \kappa_{ii}}{\partial \boldsymbol \rho} - \mathbf{x}^T\frac{\partial \mathbf{b}}{\partial \boldsymbol \rho} + \alpha \mathbf{x}^T\frac{\partial \mathbf{A}}{\partial\boldsymbol \rho} \mathbf{x}, \,\, i \in [x,y] \end{equation} where all the partial derivatives are computed analytically. As for the optimization algorithm, we choose the method of moving asymtptotes, implemented in the free open-source software NlOpt. To avoid checkerboard patterns and to enforce the final solution to be binary, we adopt filtering and projection. Specifically, we use a conic filter and a projection parameter (commonly known as $$\beta$$) that doubles every 30 iterations.