校准
土壤水分
耕作
渗透(HVAC)
环境科学
离散元法
土壤科学
修正案
混合(物理)
土工试验
岩土工程
材料科学
工程类
机械
数学
复合材料
物理
统计
生物
生态学
量子力学
政治学
法学
作者
Zhengyang Wu,Hongwen Li,Jin He,Xu Zhang,Caiyun Lu,Chao Wang,Hongdao Shan,Rongrong Li,Zongfu Yang
标识
DOI:10.1016/j.compag.2024.109079
摘要
Liquid soil amendments could improve the characteristics of soils and positively affect crop yields by mixing them with soils evenly. However, the lack of a scientific approach to model the mix by the discrete element method (DEM) limited the development of the study of the mixing mechanism, where a reliable method for calibrating the DEM parameters was critical. To solve this, this study developed an amendment infiltration test (AIT) to calibrate DEM parameters by using the Kneading contact model. The relationships between the DEM parameters and the AIT results were evaluated, and the DEM parameters were calibrated. Then a rotary tillage with mixing liquid amendment test (RTMLAT) was used to verify the reliability of the calibrated parameters, where the liquid contents at 50 mm and 100 mm depths were used to evaluate differences between experimental RTMLAT and simulated RTMLAT. It was shown that the maximum liquid content of the measured layer in the AIT was linearly correlated with the parameter of the minimum liquid content gradient (R2 = 0.9987), and the maximum slope for 10 consecutive seconds was also linearly correlated with the parameter of the liquid conductivity (R2 = 0.9989). DEM parameters of the minimum liquid content gradient and the liquid conductivity were calibrated to 0.678 % and 30.33 s, respectively. The verification results showed that the trend of the liquid content distributions simulated by RTMLAT was the same as in the experiment. The differences between simulated and measured average liquid content in the 50 mm and 100 mm depths were 1.08 % and 0.54 %, respectively. These were shown that the Kneading model parameters calibrated by the AIT could give reliable results in RTMLAT. A DEM calibration approach for modeling the mixes of liquid amendments and soils was developed. It would provide a scientific approach to model the DEM solid–liquid mixing.
科研通智能强力驱动
Strongly Powered by AbleSci AI