物理
对流
传热
对流换热
热的
机械
航空航天工程
气象学
工程类
作者
Yu Zhang,Yue Yang,Geng Chen,Qi Jiang,Bo Hao
出处
期刊:Physics of Fluids
[American Institute of Physics]
日期:2025-01-01
卷期号:37 (1)
被引量:1
摘要
The triply periodic minimal surface (TPMS) is considered an ideal choice for constructing surface structure of high-speed aircraft due to its excellent convective heat transfer. In recent years, multi-morphology TPMS structures have attracted increasing attention in various fields, as they offer superior and more desirable properties compared to traditional TPMS structures with uniform units. However, the relationship between different morphologies of TPMS and their thermodynamic performance has not been extensively studied. This paper proposes a method to quantitatively analyze the heat dissipation performance of different lattice structures. We compared the heat transfer performance parameters of six minimal surface lattice structure models through experiments and simulations, finding a strong correlation between experimental and simulation results. The results indicate that under flow rate conditions of 2.08–4.58 m/s, the Gyroid-Sheet model exhibits the highest comprehensive heat transfer coefficient. Compared to the Gyroid-Solid, Primitive-Solid, Primitive-Sheet, IWP-Solid, and IWP-Sheet models, the comprehensive heat transfer coefficient increased by 15.2–20.1%, 212.6–277.9%, 110.2–137.6%, 12.5–25.7%, and 31.3–54.6%, respectively. Additionally, under the same experimental conditions, we compared the comprehensive heat transfer coefficients of the multi-morphology Gyroid-Primitive model and the Gyroid-Sheet model. The results show that the combined Gyroid-Primitive model has a comprehensive heat transfer coefficient that is 10.5–16.1% higher than that of the Gyroid-Sheet model alone. This study lays the groundwork for the application of lattice structures in surface structure of high-speed aircraft and provides a basis for meeting the design and manufacturing requirements for future lightweight structures with high heat dissipation capabilities.
科研通智能强力驱动
Strongly Powered by AbleSci AI