Abstract
Additive manufacturing (AM) has an affinity with topology optimization to think of various designs with complex structures. Hence, this paper aims to optimize the design of a lattice-structured heat sink, which can be manufactured by AM. The design objectives are to maximize the thermal performance of convective heat transfer in natural convection simulated by computational fluid dynamics (CFD) and to minimize the material cost required for AM process at the same time. The lattice structure is represented as a node/edge system via graph theory with a moderate number of design variables. Bayesian optimization, which employs the non-dominated sorting genetic algorithm II and the Kriging surrogate model, is conducted to search for better designs with the minimum CFD cost. The present topology optimization successfully finds better lattice-structured heat sink designs than a reference fin-structured design regarding thermal performance and material cost. Also, several optimized lattice-structured designs outperform reference pin-fin-structured designs regarding thermal performance though the pin-fin structure is still advantageous for a material cost-oriented design. This paper also discusses the flow mechanism observed in the heat sink to explain how the optimized heat sink structure satisfies the competing design objectives simultaneously.
Highlights
In recent years, additive manufacturing (AM) using a threedimensional printer has attracted attention and has been utilized in various fields such as mechanical engineering, aerospace engineering, biomechanical medical engineering, and architecture (Gardan and Schneider 2015; Gao et al 2015)
This paper aims to perform multi-objective topology optimization of a heat sink designed in natural convection, which considers a balance between thermal performance improvement and material cost reduction
This paper considers a lattice-structured heat sink parameterized with a moderate number of design variables based on graph theory (Bender and Williamson 2010)
Summary
Additive manufacturing (AM) using a threedimensional printer has attracted attention and has been utilized in various fields such as mechanical engineering, aerospace engineering, biomechanical medical engineering, and architecture (Gardan and Schneider 2015; Gao et al 2015). 2. It is effective to save materials since they are not processed by removal or deformation as in the conventional methods. 3. It is unnecessary to change the tools for modeling since the structure is modeled by hardening material powder (e.g., resin and metal) with a heat source. This paper wishes to take AM process’s advantages to design a heat sink, which transfers thermal energy from higher-temperature devices (e.g., CPU and GPU) to lower-temperature fluid mediums (e.g., air and liquid coolant). The AM process is expected to sophisticate the heat sink structure design and keep up with further development in high-performance computing. The AM process has an affinity with topology optimization, which has been gaining popularity in the recent field of engineering design, as reviewed in Liu et al (2018).
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