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

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 tillag...

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Autores principales: Makange, Nelson Richard, Ji, Changying, Nyalala, Innocent, Sunusi, Idris Idris, Opiyo, Samwel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155074/
https://www.ncbi.nlm.nih.gov/pubmed/34040130
http://dx.doi.org/10.1038/s41598-021-90682-w
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author Makange, Nelson Richard
Ji, Changying
Nyalala, Innocent
Sunusi, Idris Idris
Opiyo, Samwel
author_facet Makange, Nelson Richard
Ji, Changying
Nyalala, Innocent
Sunusi, Idris Idris
Opiyo, Samwel
author_sort Makange, Nelson Richard
collection PubMed
description 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.
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spelling pubmed-81550742021-05-27 Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin Makange, Nelson Richard Ji, Changying Nyalala, Innocent Sunusi, Idris Idris Opiyo, Samwel Sci Rep Article 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. Nature Publishing Group UK 2021-05-26 /pmc/articles/PMC8155074/ /pubmed/34040130 http://dx.doi.org/10.1038/s41598-021-90682-w Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Makange, Nelson Richard
Ji, Changying
Nyalala, Innocent
Sunusi, Idris Idris
Opiyo, Samwel
Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin
title Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin
title_full Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin
title_fullStr Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin
title_full_unstemmed Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin
title_short Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin
title_sort prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8155074/
https://www.ncbi.nlm.nih.gov/pubmed/34040130
http://dx.doi.org/10.1038/s41598-021-90682-w
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