Optimization and Accuracy Analysis of a Soil–Planter Model during the Sowing Period of Wheat after a Rice Stubble Based Discrete Element Method

Author:

Luo Weiwen1,Chen Xulei1,Guo Kai1ORCID,Qin Mingyang1,Wu Feng1,Gu Fengwei2,Hu Zhichao1

Affiliation:

1. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China

2. Graduate School of Chinese Academy of Agricultural Sciences, Beijing 100083, China

Abstract

The soil during the sowing period of wheat after rice stubble cannot be accurately described by existing models and parameters with DEM because of its high moisture content and strong viscosity. The purpose of this study is to conduct an overall simulation of high-viscosity paddy soil and to analyze the accuracy of the model. Based on the results of an unconfined compression test and shear test, the range of bond parameters is preliminarily determined by a simulation test. Through the P-BD test and RSM test, the parameters with significant influence are determined to be normal stiffness per unit area (SN), shear stiffness per unit area (SS), and critical shear stress (CS), and an optimized combination of these parameters is obtained. Based on the optimized model, the error range and error generation mechanism of the model are analyzed under different operating parameters. The results show that the optimal parameter combination is SN of 1.07 × 107 N/m3, SS of 0.70 × 107 N/m3, and CS of 0.35 × 105 Pa, corresponding to a compression force of 120.1 N and a shear force of 7.70 N. With an increase in forward speed or seeding quantity or a decrease in rotary plowing speed, the model accuracy tends to increase, and the range of relative errors was found to be from 8.8% to 28.4%. The results can provide a research basis for the study of the motion state of seeds under soil. It can also further enrich parameter data of soil discrete element simulation models and provide a reference for related research studies.

Funder

the Natural Science Foundation of Jiangsu Province

Publisher

MDPI AG

Subject

Plant Science,Agronomy and Crop Science,Food Science

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3