MODELLING OF ENERGY REQUIREMENTS BY A NARROW TILLAGE TOOL
Notice bibliographique
Résumé
The amount of energy consumed during a tillage operation depends on three categories of parameters: (1) soil parameters (2) tool parameters and (3) operating parameters. Although many research works have been reported on the effects of those parameters on tillage energy, the exact number of affecting parameters and the contribution of each parameter in total energy requirement have not been specified. A study with the objectives of specifying energy consuming components and determining the amount of each component for a vertical narrow tool, particularly at high speeds of operation, was conducted in the soil bin facilities of the Department of Agricultural and Bioresource Engineering, University of Saskatchewan. Based on studies by Blumel (1986) and Kushwaha and Linke (1996), four main energy consuming components were assumed: (1) energy requirements associated with soil-tool interactions;(2) energy requirements associated with interactions between tilled and fixed soil masses;(3) energy requirements associated with soil deformation; and (4) energy requirements associated with the acceleration of the tilled soil. Energy requirement of a vertical narrow tool was calculated based on the draft requirement of the tool measured in the soil bin. The effects of three variables, moisture content, operating depth and forward speed, were studied at different levels: (1) moisture content at 14% and 20%; (2) depth at 40, 80, 120 and 160 mm; and (3) speed at 1, 8, 16 and 24 km h-1. Total energy requirement was divided into these four components based upon the procedure developed in the research. Regression equations for different energy components were developed based on experimental data of two replicates and then validated by extra soil bin experiments conducted at same soil and tool but different operational conditions. The set up of energy components data in the model development showed good correlation with the available experimental data for all four components. Coefficients of all regression equations showed a first order energy-moisture content relationship best applicable to those equations of energy components. For the acceleration component, energy-depth relationship at all speed levels resulted in an equation which included first and second orders of depth. In contrast, if only two higher levels of speed were used in the regression model, the relationship between acceleration energy and depth resulted in the second order of depth. When experimental data of acceleration energy at 8, 16, and 24 km h-1 speeds were used in the regression equation, the acceleration energy-speed relationship resulted in both linear and quadratic relationships. It was concluded that for the tool and soil conditions used in the experiments, 8 km h-1 speed resulted in only linear relationship. On the other hand, 16 and 24 km h-1 speeds resulted in a quadratic relationship. Therefore, for all 3 speeds used in experiments, both linear and quadratic relationships were obtained. Considering that the tool was operating at high speeds, this…
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».