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

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 de...

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Autores principales:	Nelson Richard Makange, Changying Ji, Innocent Nyalala, Idris Idris Sunusi, Samwel Opiyo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c813b767df574a4393d4a93e813e68be
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spelling	oai:doaj.org-article:c813b767df574a4393d4a93e813e68be2021-12-02T14:49:12ZPrediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin10.1038/s41598-021-90682-w2045-2322https://doaj.org/article/c813b767df574a4393d4a93e813e68be2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90682-whttps://doaj.org/toc/2045-2322Abstract 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 $$R^{2}$$ R 2 at a $$NRMSE$$ NRMSE of 1.936 while the AM had the lowest $$R^{2}$$ R 2 of 0.957, at a $$NRMSE$$ NRMSE 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.Nelson Richard MakangeChangying JiInnocent NyalalaIdris Idris SunusiSamwel OpiyoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Nelson Richard Makange Changying Ji Innocent Nyalala Idris Idris Sunusi Samwel Opiyo Prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin
description	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 $$R^{2}$$ R 2 at a $$NRMSE$$ NRMSE of 1.936 while the AM had the lowest $$R^{2}$$ R 2 of 0.957, at a $$NRMSE$$ NRMSE 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.
format	article
author	Nelson Richard Makange Changying Ji Innocent Nyalala Idris Idris Sunusi Samwel Opiyo
author_facet	Nelson Richard Makange Changying Ji Innocent Nyalala Idris Idris Sunusi Samwel Opiyo
author_sort	Nelson Richard Makange
title	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_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_sort	prediction of precise subsoiling based on analytical method, discrete element simulation and experimental data from soil bin
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c813b767df574a4393d4a93e813e68be
work_keys_str_mv	AT nelsonrichardmakange predictionofprecisesubsoilingbasedonanalyticalmethoddiscreteelementsimulationandexperimentaldatafromsoilbin AT changyingji predictionofprecisesubsoilingbasedonanalyticalmethoddiscreteelementsimulationandexperimentaldatafromsoilbin AT innocentnyalala predictionofprecisesubsoilingbasedonanalyticalmethoddiscreteelementsimulationandexperimentaldatafromsoilbin AT idrisidrissunusi predictionofprecisesubsoilingbasedonanalyticalmethoddiscreteelementsimulationandexperimentaldatafromsoilbin AT samwelopiyo predictionofprecisesubsoilingbasedonanalyticalmethoddiscreteelementsimulationandexperimentaldatafromsoilbin
_version_	1718389534421417984

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

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