唐少容，殷 磊 ^a，杨 强 ^a，柯德秀 ^a

(宁夏大学 a. 土木与水利工程学院，b. 宁夏节水灌溉与水资源调控工程技术研究中心，c. 旱区现代农业水资源高效利用教育部工程研究中心，宁夏 银川750021)

引用格式:

唐 少 容 ，殷 磊 ，杨 强 ，等 . 微 胶 囊 相 变 材 料 改 良 粉 砂 土 的 导 热 系 数 及 预 测 模 型[J]. 中 国 粉 体 技 术 ，2 0 2 4 ，3 0(3): 112-123.

TANG S R，YIN L，YANG Q，et al. Thermal conductivity and predictive modeling of microencapsulated phase change materials for improved silt sands[J]. China Powder Science and Technology，2024，30(3):112−123.

DOI：10.13732/j.issn.1008-5548.2024.03.010

收稿日期:2023-11-09，修回日期:2024-04-15，上线日期:2024-04-25。

基金项目:国家自然科学基金项目，编号:52368050;宁夏回族自治区重点研发计划项目，编号:2021BEG03023;宁夏高等学校一流学科

建设项目，编号:NXYLXK2021A03;宁夏大学学生创新创业训练项目，编号:202310749586。 

第一作者简介:唐少容(1982—)，女，副教授，博士，硕士生导师，研究方向为岩土工程。E-mail:tangsrong@126. com。

摘要:【目的】 针对季节冻土地区渠道冻融破坏，分析微胶囊相变材料(microencapsulated phase change materials，mPCM) 改良粉砂土层渠基的温度场，对改良粉砂土的导热系数进行研究。【方法】 以mPCM为改良剂，掺入渠基粉砂土形成 mPCM 改良粉砂土;对 mPCM 改良粉砂土进行导热系数实验和内部结构表征;采用多元线性回归和支持向量机(support vector machine，SVM)方法分别建立 mPCM 改良粉砂土的导热系数预测模型。【结果】 mPCM 改良粉砂土导热系数与含水 率、干密度、mPCM 掺量有关，且受冰水相对含量、冰水相变潜热、mPCM 相变潜热和 mPCM 填充密实作用的影响，具有 明 显 的 温 度 效 应 ; m P C M 改 良 粉 砂 土 导 热 系 数 的 变 化 与 实 验 温 度 和 m P C M 相 变 温 度 有 关 ，可 分 为 快 速 降 低 ( - 1 0 ~ 0 °C ) 、 缓慢降低(0~5 °C)和逐步上升(5~10 °C)3 个阶段;多元线性回归和 SVM 模型均能较好地拟合预测 mPCM 改良粉砂土的导 热系数，但 SVM 模型更适用于表征 mPCM 改良粉砂土导热系数各影响因素间的非线性关系。【结论】 mPCM 改良粉砂土 的导热系数提高能够有效调控渠基土温度场，减轻渠道冻害，且 SVM 模型能更加准确地进行导热系数预测。

关键词:微胶囊相变材料;粉砂土;导热系数;预测模型;多元线性回归;支持向量机

Abstract

Objective

　In regions with seasonal frozen ground, frost damage in channels is a common issue due to significant temperature fluctuations affecting the foundation soil. To address this challenge, phase change materials (PCMs) are being integrated into foundation soil to regulate soil temperature dynamics and mitigate frost damage. Understanding the thermal conductivity of PCMmodified soil is crucial for accurately analyzing temperature distribution. Experimental studies have highlighted several factors influencing soil thermal conductivity, including temperature, moisture content, dry density, salt content, mineral composition, and fine particle content. The sensitivity of these factors varies depending on whether the soil is frozen or thawed. Due to the complex interplay of these factors, developing accurate predictive models is essential to assess their impact on thermal conductivity. Empirical models employing artificial intelligence algorithms are gaining traction due to their high accuracy and adaptability, particularly in thermal conductivity prediction. However, significant progress has been made in analyzing and predicting thermal conductivity in typical geological materials. Despite advances in thermal conductivity analysis for standard soils, research on atypical soils like PCM-modified soils remains relatively limited. To bridge this gap, studies are investigating microencapsulated phase change materials (mPCM) as amendments for sandy soil foundations in Ningxia. They are examining how factors such as mPCM content, moisture, temperature, and dry density affect thermal conductivity, supplemented by scanning electron microscopy (SEM) to analyze internal pore structures. To accurately assess the temperature distribution in the drainage base of pulverized sandy soil improved by microencapsulated phase change materials (mPCM), it is essential to study the thermal conductivity of this modified soil and establish a reliable prediction model. This research will provide crucial references for the application of PCMmodified soils in engineering projects within regions with seasonal frozen ground.

Results and Discussion The thermal conductivity of silt sand amended with mPCM was observed to be influenced by the test temperature and the phase transition temperature of the mPCM. Notably，the thermal conductivity demonstrated a pronounced temperature dependency，characterized by three distinct phases:a rapid decrease(-10 to 0 °C)，a slow decrease(0 to 5 °C)， and a gradual increase(5 to 10 °C). Furthermore，the coefficient of thermal conductivity of mPCM-amended silt loam exceeded that of unamended silt loam and exhibited augmentation with increasing water content，dry density，and mPCM mass fraction. Both multiple linear regression and Support Vector Machine(SVM)models effectively predicted the thermal conductivity of mPCM-amended silt loam. Nevertheless，the SVM model proved to be more adept at capturing the nonlinear relationship among the influencing factors of thermal conductivity in mPCM-amended silt loam.

Conclusion The thermal conductivity of mPCM-amended silty sand soil is influenced by several key factors，including water content，dry density，and mPCM content. Furthermore，it is significantly impacted by the relative proportions of ice and water， the latent heat of phase change for ice and water，the latent heat of phase change of mPCM，as well as the role of mPCM filling and densification. These factors exhibit notable temperature-dependent effects. For accurate prediction of thermal conductivity， the SVM model proves to be effective. The findings of this study can provide valuable insights for the application and exploration of phase change materials in regions characterized by seasonal permafrost.

Keywords:microencapsulated phase change material;silty sand;thermal conductivity;predictive modeling;multiple linear regression;support vector machine

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