ISSN 1008-5548

CN 37-1316/TU

2024年30卷  第3期
<返回第3期

微胶囊相变材料改良粉砂土的导热系数及预测模型

Thermal conductivity and predictive modeling of microencapsulated phase change materials for improved silt sands


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


参考文献(References)

[1]孙斌祥,陈加集,潘建光. 掺微胶囊相变材料粗粒土的冻胀试验研究[J]. 冰川冻土,2023,45(1):178-185.

SUN B X,CHEN J J,PAN J G. Experimental study on frost heaving of coarse grained soil mixed with microencapsulated phase change materials[J]. Journal of Glaciology and Geocryology,2023,45(1):178-185.

[2]LIU D H,WANG Y L,LIANG J Y. Potential applications of phase change materials to extend the winter construction time of earth-rock dam in cold regions[J]. Journal of Materials in Civil Engineering,2021,33(8):04021194.

[3]AFSHIN M,MONCEF L N. Integrating phase change materials in construction materials:critical review[J]. Construction and Building Materials,2019,217:36-49.

[4]唐少容,杜鹏,李昊天,等. 由石蜡基相变材料和煤渣改良的粉砂土的冻融性能[J]. 中国粉体技术,2024,30(1) 123-131.

TANG S R,DU P,LI H T,et al. Freeze-thaw properties of silty sand modified by paraffin-based phase change materials and cinde[r J]. China Powder Science and Technology,2024,30(1):123-131.

[5]KRAVCHENKO E,LIU J K,CHANG D,et al. Study of the thermal field of a mixture of soil and PCM materials with simulation of the warming effect during a phase change[J]. Construction and Building Materials,2020,262:120818. 

[6]徐云山,肖子龙,孙德安,等. 土体导热系数温度效应及其预测模型[J]. 岩土工程学报,2023,45(6):1180-1189.

XU Y S,XIAO Z L,SUN D A,et al. Temperature effects and prediction model of thermal conductivity of soil[J]. Chinese Journal of Geotechnical Engineering,2023,45(6):1180-1189.

[7]徐洁,胡海涛,郑植. 压实度和含水率对非饱和土导热系数的影响[J]. 岩土工程学报,2020,42(S1):244-248.

XU J,HU H T,ZHENG Z. Effects of compaction and water content on thermal conductivity of unsaturated soils[J]. ChineseJournal of Geotechnical Engineering,2020,42(S1):244-248.

[8]靳贻杰,陶勇,张婷,等. 含盐冻土冻结温度及导热系数试验研究[J]. 郑州大学学报(工学版),2023,44(4):120-126.

JIN Y J,TAO Y,ZHANG T,et al. Experimental study on freezing temperature and thermal conductivity of salty frozen soil [J]. Journal of Zhengzhou University(Engineering Science),2023,44(4):120-126.

[9]QUOC HUNG VU,JEAN-MICHEL PEREIRA,et al. Effect of clay content on the thermal conductivity of unfrozen and frozen sandy soils[J]. International Journal of Heat and Mass Transfer,2023,206:123923.

[10]BARRY-MACAULAY D,BOUAZZA A,SINGH R M,et al. Thermal conductivity of soils and rocks from the Melbourne (Australia)region[J]. Engineering Geology,2013,164:131-138.

[11]谭贤君,褚以惇,陈卫忠,等. 考虑冻融影响的岩土类材料导热系数计算新方法[J]. 岩土力学,2010,31(S2):70-74. 

TAN X J,CHU Y D,CHEN W Z,et al. A new method to study thermal conductivity of geomaterials considering phase change[J]. Rock and Soil Mechanics,2010,31(S2):70-74.

[12]张涛,刘松玉,张楠,等. 土体热传导性能及其热导率模型研究[J]. 建筑材料学报,2019,22(1):72-80.

ZHANG T,LIU S Y,ZHANG N,et al. Research of soil thermal conduction properties and its thermal conductivity model[J]. Journal of Building Materials,2019,22(1):72-80.

[13]王理想,宋新江,黄铭,等. 分散性黏土导热系数试验与预测方法研究[J]. 水利学报,2023,54(3):311-322.

WANG L X,SONG X J,HUANG M,et al. Influence factors and prediction method of thermal conductivity for dispersiveclay[J]. Journal of Hydraulic Engineering,2023,54(3):311-322.

[14]TONG F,JING L,ZIMMERMAN R W. An effective thermal conductivity model of geological porous media for coupledthermo-hydro-mechanical systems with multiphase flow[J]. International Journal of Rock Mechanics and Mining Sciences,2009(8):46.

[15]张涛, 杨玉玲,张家铭,等 . 基于相似性原则的橡胶颗粒-砂混合物热导率理论模型[J]. 岩土工程学报,2024,46(2):436-444.

ZHANG T,YANG Y L,ZHANG J M,et al. Theoretical model for thermal conductivity of rubber-sand mixtures based on similarity heat conduction principle[J]. Chinese Journal of Geotechnical Engineering,2024,46(2):436-444.

[16]刘志云,张伟,王伟,等 . 昆仑山地区冻融土导热系数试验测试与预测模型研究[J]. 水文地质工程地质,2021, 48(1):105-113.

LIU Z Y,ZHANG W,WANG W,et al. Research on experimental tests and prediction models of thermal conductivity offreezing-thawing soil in the kunlun mountains[J]. Hydrogeology & Engineering Geology,2021,48(1):105-113. 

[17]关鹏,焦玉勇,段新胜. 基于RBF神经网络的土体导热系数非线性预测[J]. 太阳能学报,2021,42(3):171-178.

GUAN P,JIAO Y Y,DUAN X S. Non-Liner prediction of soil thermal conductivity based on RBF neural network[J]. ActaEnergiae Solaris Sinica,2021,42(3):171-178.

[18]周殷康,阎长虹,谢胜华,等. 基于细观模拟的软土导热系数数值预测模型[J]. 工程地质学报,2019,27(5):1070-1077.

ZHOU Y K,YAN C H,XIE S H,et al. A numerical model for thermal conductivity of soft soils based on mesoscopic simu- lation[J]. Journal of Engineering Geology,2019,27(5):1070-1077.

[19]周殷康,阎长虹,郑军,等. 双孔隙压实膨润土的细观导热模型[J]. 岩土工程学报,2021,43(7):1352-1359. 

ZHOU Y K,YAN C H,ZHENG J,et al. Mesoscale model for thermal conductivity of compacted dual-porosity bentonite[J]. Chinese Journal of Geotechnical Engineering,2021,43(7):1352-1359.

[20]褚召祥,周国庆,饶中浩,等 . 颗粒岩土介质热导率预测关联式及其演化机制[J]. 岩石力学与工程学报,2020,39(2):384-397.

CHU Z X,ZHOU G Q,RAO Z H,et al. Predicting correlation and evolution mechanisms of the effective thermal conducti- vity of granular geomaterials[J]. Chinese Journal of Rock Mechanics and Engineering,2020,39(2):384-397.

[21]TIAN Z,LU Y,HORTON R,et al. A simplified de Vries-based model to estimate thermal conductivity of unfrozen and frozen soil,European Journal of Soil Science,2016,67(5):564-572.

[22]HE H,FLERCHINGER G N,KOJIMA Y,et al. A review and evaluation of 39 thermal conductivity models for frozen soils,Geoderma,2021,382:114694.

[23]王红旗,李栋伟,钟石明,等. 石灰改良红黏土导热系数影响因素及模型预测[J]. 科学技术与工程,2023,23(5): 2084-2092.

WANG H Q,LI D W,ZHONG S M,et al. Influence factors and model prediction of thermal conductivity of lime-modifiedred clay[J]. Science Technology and Engineering,2023,23(5):2084-2092. 

[24]中华人民共和国住房和城乡建设部,国家市场监督管理局. 土工试验方法标准:GB/T50123—2019[S]. 北京:中国计划出版社,2019.

Ministry of Housing and Urban-Rural Development of the People's Republic of China,State Administration for Market Reg- ulation. Standard for geotechnical testing method:GB/T 50123—2019[S]. Beijing:China Planning Press,2019.

[25]唐盼盼,徐洁,卢永洪 . 含水率及温度影响非饱和土导热系数的试验研究[J]. 防灾减灾工程学报,2019,39(4): 678-683.

TANG P P,XU J,LU Y H. Experimental study on effects of water content and temperature on thermal conductivity of unsaturated soils[J]. Journal of Disaster Prevention and Mitigation Engineering,2019,39(4):678-683. 

[26]李昊天. 石蜡基相变材料改良渠基粉砂土冻融特性研究[D]. 宁夏:宁夏大学,2023.

LI H T. The freeze-thaw characteristics of paraffin based phase changematerials for improving channel foundation sand soil Research[D]. Ning Xia,Ning Xia:University,2023.

[27]王才进,蔡国军,武猛,等. 基于人工智能算法预测土体导热系数[J]. 岩土工程学报,2022,44(10):1899-1907.

WANG C J,CAI G J,WU M,et al. Prediction of thermal conductivity of soils based on artificial intelligence algorithm[J]. Chinese Journal of Geotechnical Engineering,2022,44(10):1899-1907.