十八烷酸热传导机制的尺度效应研究

十八烷酸的热导率具有显著的尺寸依赖性,然而只有宏观热输运性质得到充分研究,限制了微尺度特征结构的复合材料热性能的进一步提高。因此,为满足新型复合相变材料的研究需求,十八烷酸的纳米尺度热输运特性亟待解决。基于分子动力学模拟,系统性地研究了块体、纳米线和纳米链三种形态的十八烷酸热导率的演变规律,通过平衡法模拟的热导率分别为0.4546、0.2213和0.0085W·m^-1·K^-1;结合声子态密度和重叠能分析,发现在较低频率声子振动的衰减导致纳米线热导率低于块体,显著降低的声子重叠能严重阻碍纳米链的声子输运导致极低热导率。

封装铜纳米线的碳米纳管热输运温度影响机制探究

碳纳米管的独特中空结构使其能够通过内部封装材料形成核壳结构,制备各种可调控功能性纳米器件。受限于纳米特征尺寸,有限的研究报道导致微观热输运机制尚不明晰,导致碳纳米管核壳结构的材料设计缺乏明确的理论支持。其中,管内封装金属纳米线的制备、调控和应用已经得到初步探索,作为一种典型的碳管核壳结构,其热输运特性及机制具有较强的研究价值。因此,本工作以铜金属为典型,探究碳管内封装铜纳米线结构在不同的加热温度下复合结构的热导率变化,并通过管内纳米线轨迹、温度分布、声子态密度和重叠能等进行热输运机制分析。

A Neural Regression Model for Predicting Thermal Conductivity of CNT Nanofluids with Multiple Base Fluids

High thermal conductivity of carbon nanotube nanofluids(k_(nf))has received great attention.However,the current researches are limited by experimental conditions and lack a comprehensive understanding of k_(nf) variation law.In view of proposition of data-driven methods in recent years,using experimental data to drive prediction is an effective way to obtain k_(nf),which could clarify variation law of k_(nf) and thus greatly save experimental and time costs.This work proposed a neural regression model for predicting k_(nf).It took into account four influencing factors,including carbon nanotube diameter,volume fraction,temperature and base fluid thermal conductivity(k_(f)).Where,four conventional fluids with k_(f),including R113,water,ethylene glycol and ethylene glycol-water mixed liquid were considered as base fluid considers.By training this model,it can predict k_(nf) with different factors.Also,change law of four influencing factors considered on the k_(nf) enhancement has discussed and the correlation between different influencing factors and k_(nf) enhancement is presented.Finally,compared with nine common machine learning methods,the proposed neural regression model shown the highest accuracy among these.