Zhu Chuan-Yong, He Zhi-Yang, Du Mu, Gong Liang, Wang Xinyu
College of New Energy, China University of Petroleum (East China), Qingdao, Shandong, People's Republic of China.
Institute for Advanced Technology, Shandong University, Jinan 250061, People's Republic of China.
Nanotechnology. 2021 Nov 19;33(6). doi: 10.1088/1361-6528/ac3688.
The effective thermal conductivity of soils is a crucial parameter for many applications such as geothermal engineering, environmental science, and agriculture and engineering. However, it is pretty challenging to accurately determine it due to soils' complex structure and components. In the present study, the influences of different parameters, including silt content (), sand content (), clay content (), quartz content (), porosity, and water content on the effective thermal conductivity of soils, were firstly analyzed by the Pearson correlation coefficient. Then different artificial neural network (ANN) models were developed based on the 465 groups of thermal conductivity of unfrozen soils collected from the literature to predict the effective thermal conductivity of soils. Results reveal that the parameters of,,, andhave a relatively slight influence on the effective thermal conductivity of soils compared to the water content and porosity. Although the ANN model with six parameters has the highest accuracy, the ANN model with two input parameters (porosity and water content) could predict the effective thermal conductivity well with acceptable accuracy and= 0.940. Finally, a correlation of the effective thermal conductivity for different soils was proposed based on the large number of results predicted by the two input parameters ANN-based model. This correlation has proved to have a higher accuracy without assumptions and uncertain parameters when compared to several commonly used existing models.
土壤的有效热导率是地热工程、环境科学、农业和工程等许多应用中的一个关键参数。然而，由于土壤结构和成分复杂，准确测定它颇具挑战性。在本研究中，首先通过皮尔逊相关系数分析了不同参数，包括粉砂含量、砂含量、黏土含量、石英含量、孔隙率和含水量对土壤有效热导率的影响。然后基于从文献中收集的465组未冻土热导率数据，建立了不同的人工神经网络（ANN）模型来预测土壤的有效热导率。结果表明，与含水量和孔隙率相比，、、和参数对土壤有效热导率的影响相对较小。虽然具有六个参数的ANN模型精度最高，但具有两个输入参数（孔隙率和含水量）的ANN模型能够以可接受的精度（= 0.940）很好地预测有效热导率。最后，基于由两个输入参数的ANN模型预测的大量结果，提出了不同土壤有效热导率的相关性。与几个常用的现有模型相比，这种相关性在没有假设和不确定参数的情况下具有更高的精度。
