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基于解析方法、离散元模拟及土槽试验数据的精确深松预测

Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin.

作者信息

Makange Nelson Richard, Ji Changying, Nyalala Innocent, Sunusi Idris Idris, Opiyo Samwel

机构信息

College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China.

Department of Engineering Sciences and Technology, Sokoine University of Agriculture, P.O.BOX 3003, Morogoro, Tanzania.

出版信息

Sci Rep. 2021 May 26;11(1):11082. doi: 10.1038/s41598-021-90682-w.

DOI:10.1038/s41598-021-90682-w
PMID:34040130
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8155074/
Abstract

Prediction of a precise subsoiling using an analytical model (AM) and Discrete Element Method (DEM) was conducted to explain cutting forces and the soil profile induced changes by a subsoiler. Although sensors, AMs and DEM exist, there are still cases of soil structure deformation during deep tillage. Therefore, this study aimed to provide a clear understanding of the deep tillage using prediction models. Experimental data obtained in the soil bin trolley with force sensors were used for verification of the models. Experiments were designed using Taguchi method. In the AM, the modified-McKyes and Willat and Willis equations were used to determine cutting forces and soil furrow profile respectively. Calculations were done using MATLAB software. The elastoplastic behavior of soil was incorporated into the DEM. The DEM predicted results with the best regression of 0.984 [Formula: see text] at a [Formula: see text] of 1.936 while the AM had the lowest [Formula: see text] of 0.957, at a [Formula: see text] of 6.008. All regression results were obtained at p < 0.05. The ANOVA test showed that the p-values for the horizontal and vertical forces were 0.9396 and 0.9696, respectively. The DEM predicted better than the AM. DEM is easy to use and is effective in developing models for precision subsoiling.

摘要

利用解析模型（AM）和离散元法（DEM）对精确深松进行预测，以解释深松机产生的切削力和土壤剖面变化。尽管存在传感器、解析模型和离散元法，但深耕过程中仍存在土壤结构变形的情况。因此，本研究旨在通过预测模型来清晰地理解深耕过程。在装有力传感器的土槽小车上获得的实验数据用于模型验证。实验采用田口方法设计。在解析模型中，分别使用修正的麦凯斯方程以及威拉特和威利斯方程来确定切削力和土壤犁沟剖面。计算使用MATLAB软件完成。将土壤的弹塑性行为纳入离散元法。离散元法预测结果的最佳回归系数在1.936时为0.984 [公式：见原文]，而解析模型在6.008时的最低回归系数为0.957。所有回归结果均在p < 0.05时获得。方差分析表明，水平力和垂直力的p值分别为0.9396和0.9696。离散元法的预测效果优于解析模型。离散元法易于使用，在开发精确深松模型方面很有效。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/16f1de081bd3/41598_2021_90682_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/4a723fcfedf3/41598_2021_90682_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/256a8f720da8/41598_2021_90682_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/35d55d1b44b9/41598_2021_90682_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/2a34b5716e18/41598_2021_90682_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/c562b8f17440/41598_2021_90682_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/a4652e246386/41598_2021_90682_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/0bf049c2589c/41598_2021_90682_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/761b41ee5ecf/41598_2021_90682_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/23c6e287e28e/41598_2021_90682_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/daea1d12cde7/41598_2021_90682_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d5f1/8155074/16f1de081bd3/41598_2021_90682_Fig11_HTML.jpg

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