Influence of Tyre Inflation Pressure and Wheel Load on the Traction Performance of a 65 kW MFWD Tractor on a Cohesive Soil
Bibliographic record
Abstract
The choice of tractor configuration is of primary importance in tillage operations for the optimisation of traction performance, i.e. for limiting slip which involves energy loss. To a great extent, this aspect affects the fuel consumption and the time required for soil tillage. Tyre inflation pressure and wheel load are both easily managed parameters which play a significant role in controlling the traction performance of a tractor. The present study aimed to investigate the influence of tyre inflation pressure and wheel load on the traction performance of a mechanical front wheel drive MFWD tractor (65 kW engine power) on an agricultural clay (C) Vertic Cambisol on the basis of results of traction tests and simulations with a semi-empirical soil-tyre interaction model adapted for MFWD vehicles. The traction tests were carried out using four tractor configurations with two tractor weights (40.8 kN and 50.2 kN) and two tyre inflation pressures (60 kPa and 160 kPa). Traction performance was considered in terms of drawbar pull, traction coefficient, tractive efficiency, power delivery efficiency and specific fuel consumption in relation to wheel slip. A decrease in tyre pressure and an increase in wheel load resulted in higher drawbar pull however, only the former produced improvements in terms of coefficient of traction, tractive efficiency, power delivery efficiency and specific fuel consumption, while the only significant benefit resulting from the latter was a reduction in specific fuel consumption at a tyre pressure of 160 kPa and a slip of under 15%.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".