A generative design framework for passive thermal control with macroscopic metamaterials

Efficient thermal management holds pivotal significance in the rapidly evolving realm of energy-dense applications, notably in high-performance computing, where the push toward miniaturization encounters limitations in controlling heat flux within confined spaces. The advent of metamaterials present...

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Bibliographic Details
Published in:Thermal science and engineering progress Vol. 51; p. 102637
Main Authors: Ignuta-Ciuncanu, Matei C., Tabor, Philip, Martinez-Botas, Ricardo F.
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.06.2024
Subjects:
Genetic algorithm
Heat conduction
Multi-objective optimization
Passive thermal management
Thermal cloaking
Variational autoencoder
Heat conduction
Variational autoencoder
Passive thermal management
Multi-objective optimization
Genetic algorithm
Thermal cloaking
ISSN:2451-9049, 2451-9049
Online Access:Get full text
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Summary:Efficient thermal management holds pivotal significance in the rapidly evolving realm of energy-dense applications, notably in high-performance computing, where the push toward miniaturization encounters limitations in controlling heat flux within confined spaces. The advent of metamaterials presents an energy-free opportunity to transform thermal control, essential to boosting system efficiency. This work marks a step toward integrating AI in the design and optimization of compact systems, such as printed circuit boards, solar collectors, and power electronics. We present an automated generative design exploration framework for thermal metamaterials capable of effectively controlling heat transfer to achieve multiple objectives: unperturbed temperature distribution (cloaking) and targeted heat flux insulation and concentration. The architected structures developed leverage the optimization of unit cell arrangements with contrasting thermal properties to exhibit characteristics not commonly occurring in nature. This framework employs a variational autoencoder (VAE), trained on bio-inspired leaf and mushroom microstructures, to assemble bi-material unit cells on a flexible lattice for arbitrary domains. By operating a genetic optimizer within the reduced design space of the VAE, we develop these assemblies to address adversarial temperature and heat flux objectives using a finite element solver. We demonstrate the modularity and scalability of the framework, outputting multi-objective metamaterial solutions for user-specified thermal requirements within a total run-time of 5 min only. •A generative design framework was developed for macroscopic thermal metamaterials.•Biomimetic thermal cells, inspired by leaves and gills, were sampled using a VAE.•A genetic optimizer explored the latent design space for temperature and heat flux goals.•The framework was used on a thermal management problem, tackling heat flux concentration and cloaking.•The framework outputs multi-objective metamaterial solutions in 5 minutes on a standard computer.
ISSN:2451-9049
2451-9049
DOI:10.1016/j.tsep.2024.102637