AI 中文总结
针对生成式AI发展带来的散热需求，该研究推广有效介质近似，引入含菲涅耳反射系数形式的校正系数，将热流的类反射行为纳入几何光学框架，拓展了高对比度热超材料的热输运预测能力。
AI 中文摘要
生成式AI的快速发展加剧了大型数据中心对高效散热的需求。为控制热流，具有层状结构的热超材料被广泛应用，这类材料赋予热导率各向异性特性。然而，传统有效介质近似（EMA）在相邻层与背景介质间存在高热导率对比度的系统中，往往无法提供准确预测。本研究通过引入两个校正系数对EMA进行推广，将其适用范围扩展至传统EMA此前不适用的领域，即含背景介质的高对比度热超材料。值得注意的是，所提出的其中一个系数具有与光学中菲涅耳反射系数相同的数学形式，这使我们能够将热流穿透高热对比度相邻层时的“类反射”行为直观地理解为，热扩散这一传统上被视为纯耗散过程的现象，可通过几何光学框架来阐释。
英文摘要
The rapid growth of generative AI has intensified the need for efficient heat dissipation in large-scale data centers. To control heat flow, thermal metamaterials with layered structures have been widely used, which impart the anisotropic properties of thermal conductivities. However, the conventional effective medium approximation (EMA) often fails to provide accurate predictions in systems with a high thermal conductivity contrast between adjacent layers embedded in a background medium. Here, we generalize the EMA by introducing two corrective coefficients that extend its validity to regimes where the conventional EMA was previously inapplicable, i.e., high-contrast thermal metamaterials with the background medium. Notably, one of these coefficients that we proposed has the same mathematical form as the Fresnel reflection coefficient in optics. This allows us to interpret the "reflection-like" behavior of heat flow as it penetrates adjacent layers with high thermal contrast. Our findings suggest that heat diffusion, traditionally viewed as a purely dissipative process, can be understood intuitively through the framework of ray optics.
Comments10 pages, 4 figures
