Thermal camouflage device with efficient thermal management

Zeng, Xu; Wang, Wei; Sun, Chen; Tian, Xi-Wei; Wang, Xiaohui; Cai, Yanzhao; Chen, Chunyan; Xue, Yongbiao

doi:10.1016/j.ijheatmasstransfer.2022.123402

Cited by 5 publications

(2 citation statements)

References 51 publications

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“…427−430 Besides, thermal camouflage has been optimized to conceal the thermal signature of the targeted electronic equipment serving as a heat source by dissipating heat from the target to reduce its temperature. 431 Shen et al 432 introduced a thermal converger that enables the design of a thermal grating, effectively concentrating and focusing heat with high efficiency for subsequent energy conversion.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

“…Additionally, the thermal rotator can distort and guide the heat flow toward a heat sink, enhancing heat dissipation efficiency. Interested readers can refer to highlighted references for further exploration. − Besides, thermal camouflage has been optimized to conceal the thermal signature of the targeted electronic equipment serving as a heat source by dissipating heat from the target to reduce its temperature . Shen et al introduced a thermal converger that enables the design of a thermal grating, effectively concentrating and focusing heat with high efficiency for subsequent energy conversion.…”

Section: Challenges and Perspectivesmentioning

confidence: 99%

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Machine Learning Aided Design and Optimization of Thermal Metamaterials

Zhu,

Bamidele,

Shen

et al. 2024

Chem. Rev.

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Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target properties. (2) Integration of discriminative models with optimization algorithms to enhance computational efficiency. (3) Generative models for the structural design and optimization of thermal metamaterials.

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Section: Challenges and Perspectivesmentioning

confidence: 99%