Evolutionary design of conductive pathways using a generative autoencoder
To enhance performance and energy efficiency in compact electronic systems, effective thermal management through miniaturized heat sinks is essential. This study introduces a generative design framework for optimizing bi-material heat sink architectures suitable for space-constrained electronics. Co...
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| Vydáno v: | International communications in heat and mass transfer Ročník 166; s. 109098 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.08.2025
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| ISSN: | 0735-1933 |
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| Abstract | To enhance performance and energy efficiency in compact electronic systems, effective thermal management through miniaturized heat sinks is essential. This study introduces a generative design framework for optimizing bi-material heat sink architectures suitable for space-constrained electronics. Conventional thermal optimization methods often rely on gradient-based techniques or parametric geometric shapes, which limit adaptability and design resolution. In contrast, our approach integrates topology optimization, constructal theory, and bio-inspired principles via a generative model that couples a variational autoencoder with evolutionary algorithms. Trained on a diverse dataset of microleaf structures, constructal patterns, and synthetic conductive networks, the model addresses the area-to-point conduction challenge, a common scenario where heat must be efficiently transferred from a broad source area to a narrow sink. Compared to standard methods, the proposed design pipeline reduces average device temperatures by 10% and peak hotspot temperatures by up to 1%. Additionally, it achieves a 13% increase in Pareto front hypervolume, indicating superior trade-offs among conflicting thermal objectives. By enabling smooth feature transitions and adaptive geometries through latent space interpolation, the method supports modular and scalable design of unit cells and full assemblies. A demonstrative case assembles these cells into a manufacturable three-dimensional heat sink tailored for spatially varying thermal loads. The methodology can extend beyond conductive heat sinks to encompass conjugate heat transfer and multi-physics systems by adjusting design objectives or finite element formulations. This work offers a blueprint for generative thermofluids design in applications such as phase-change materials, heat pipes, and cold plate systems.
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| AbstractList | To enhance performance and energy efficiency in compact electronic systems, effective thermal management through miniaturized heat sinks is essential. This study introduces a generative design framework for optimizing bi-material heat sink architectures suitable for space-constrained electronics. Conventional thermal optimization methods often rely on gradient-based techniques or parametric geometric shapes, which limit adaptability and design resolution. In contrast, our approach integrates topology optimization, constructal theory, and bio-inspired principles via a generative model that couples a variational autoencoder with evolutionary algorithms. Trained on a diverse dataset of microleaf structures, constructal patterns, and synthetic conductive networks, the model addresses the area-to-point conduction challenge, a common scenario where heat must be efficiently transferred from a broad source area to a narrow sink. Compared to standard methods, the proposed design pipeline reduces average device temperatures by 10% and peak hotspot temperatures by up to 1%. Additionally, it achieves a 13% increase in Pareto front hypervolume, indicating superior trade-offs among conflicting thermal objectives. By enabling smooth feature transitions and adaptive geometries through latent space interpolation, the method supports modular and scalable design of unit cells and full assemblies. A demonstrative case assembles these cells into a manufacturable three-dimensional heat sink tailored for spatially varying thermal loads. The methodology can extend beyond conductive heat sinks to encompass conjugate heat transfer and multi-physics systems by adjusting design objectives or finite element formulations. This work offers a blueprint for generative thermofluids design in applications such as phase-change materials, heat pipes, and cold plate systems.
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| ArticleNumber | 109098 |
| Author | Stärk, Hannes Ignuta-Ciuncanu, Matei C. Martinez-Botas, Ricardo F. |
| Author_xml | – sequence: 1 givenname: Matei C. orcidid: 0009-0003-3591-9033 surname: Ignuta-Ciuncanu fullname: Ignuta-Ciuncanu, Matei C. organization: Sustainable Energy Technology and Turbomachinery Lab, Imperial College London, London, SW7 2AZ, UK – sequence: 2 givenname: Hannes orcidid: 0000-0002-4463-326X surname: Stärk fullname: Stärk, Hannes organization: MIT CSAIL, Cambridge, MA, USA – sequence: 3 givenname: Ricardo F. orcidid: 0000-0002-3263-8151 surname: Martinez-Botas fullname: Martinez-Botas, Ricardo F. email: r.botas@ic.ac.uk organization: Sustainable Energy Technology and Turbomachinery Lab, Imperial College London, London, SW7 2AZ, UK |
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| Keywords | Bio-inspiration Generative design Thermal design Evolutionary optimization |
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