An optimal gear-shifting strategy for heavy trucks with trade-off study between trip time and fuel economy
Why this work is in the frame
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Bibliographic record
Abstract
We show how the fuel efficiency of heavy mining trucks can be improved by optimising the gear-shifting strategy. Using characteristic tests of the diesel engine, a high-fidelity model of a mining truck was built in MapleSim and a consistent low-order model was developed in Matlab. Dynamic programming was used to optimise the low-order model of the specialised off-road 30-tonne truck over a fixed route in a mining area. There were two competing objectives: fuel use and trip time, which were combined in a single objective function using weighting coefficients. A Pareto curve was created to analyse the effect of the weights on the fuel use and trip time. Applying the control strategy obtained from dynamic programming to the high-fidelity model, it is estimated that 40,000 L of fuel can be saved annually for a mine that produces 110 kilotonnes of coal per day.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it