Multiscale topology optimization with explicit de-homogenization for graded pin-fin heat sink design

Abstract

This paper is focused on a homogenization-based multiscale topology optimization (TO) framework with explicit de-homogenization for the design of graded pin-fin heat sinks subject to conjugate heat transfer. The proposed approach enables the design of diverse pin-fin geometries, and facilitates practical implementation through direct computer-aided design (CAD) model generation using explicit geometry feature representation. The proposed framework consists of three steps. Surrogate models for effective thermal conductivity and fluid permeability are first constructed using numerical homogenization and artificial neural networks. These models are then incorporated with cell rotations and macroscopic design variables to build the material property model, which is subsequently used to define and solve the TO problem. Finally, explicit de-homogenization is employed to restore optimized pin-fin microstructures using CAD feature representation, allowing direct generation of manufacturable CAD models. The effectiveness of the proposed framework is validated through a quantitative comparison of three benchmark heat sink designs using full three-dimensional (3D) numerical simulations. This comparison confirms that a multiscale heat sink design outperforms conventional (uniform fin, size-optimized fin, and macro-topology optimized fin) approaches by achieving a balanced configuration through effective flow redistribution. In addition, the practical applicability of the framework is established through additional examples including a Pareto front investigation for trade-off analysis and the direct generation of a 3D CAD model for additive manufacturing (AM). © 2026 Elsevier Ltd.