The Impact of Lift Axles on Fuel Economy and GHG Emissions Reduction
Bibliographic record
Abstract
Using lift axles enables fleet to increase the load capacity of a vehicle, eliminating the need for multiple trips, thus reducing operational costs. In a project to assess the potential of reducing fuel consumption and greenhouse gas (GHG) emissions by lifting axles on unloaded semi-trailers, lift axle regulations in various jurisdictions and the studies that led to these regulations were analyzed. The SAE Fuel Consumption Test Procedures Type II (J1321) was used for fuel consumption track test evaluations. The tests were conducted on unloaded two-axle van semi-trailers, four-axle van semitrailers, and B-trains, and resulted in fuel savings of 1.3% to 4.8%, depending on vehicle configuration and the number of axles lifted during the test. Temperature measurements and thermal images taken during the tests show that the difference in temperature of rolling tires of tandem axles with one axle lifted, compared to the temperature of rolling tires of tandem axles with both axles on ground was minimal. As such, lifting the axles of unloaded van semi-trailers shows an interesting potential in fuel savings and GHG emissions reduction because in many applications, semi-trailers travel unloaded 50% of the time. Special equipment is however necessary to lift axles and there are regulatory constraints regarding these devices. Several Canadian provinces allow the lifting of axles when a semi-trailer is empty, however, additional studies are necessary to evaluate the impact of lift axles on maintenance (tire wear) and safety (braking performance).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".