Improved aerodynamic and road-load measurements using the coast-down method year 1: literature review and roadmapping
Bibliographic record
Abstract
In 2016, the transportation sector was the second largest source of greenhouse gas (GHG) emissions in Canada, accounting for 25% of total national emissions. Road transportation, which includes personal transportation (light-duty vehicles and trucks) and heavy-duty vehicles, accounted for over 70% of transport emissions in Canada. In an effort to reduce GHG emissions, manufacturers are addressing the need to make vehicles more fuel-efficient, and regulators are required to verify claims of increased fuel-efficiency and reduced emissions. Fuel-efficiency and emissions testing is generally carried out on a chassis dynamometer and requires input in the form of road load coefficients. Coast-down testing is a convenient and effective method to obtain road load coefficients for fuel-efficiency and emissions testing. However, there is considerable variability in the results obtained from coast-down tests, indicating that there is room for improving the methodology. As such, Transport Canada, through its ecoTECHNOLOGY for Vehicles program, have commissioned a multi-phase project to investigate possible improvements in aerodynamic and road-load measurements using the coast-down method. The main objective of this first year of the project was to lay out a detailed roadmap of the steps required to improve the reliability and accuracy of road-load measurements using coast-down testing, so as to begin developing improved measurement and analysis techniques that will be evaluated in subsequent phases of the project. The road-mapping exercise was guided by an extensive review of the literature on coast-down testing and methodologies, the summary and main findings of which are covered in this report. It is recommended that the focus of the next year of this study be to improve coast-down data analysis methods to address the knowledge gaps identified in this study. It is suggested to revisit coast-down data previously measured by NRC as well as data obtained from other sources to develop and evaluate new analytical models, test and data analysis procedures. It is also recommended to research and investigate the effects of road surface roughness and irregularities, track geography and ambient wind variations and unsteadiness on coast-down test results. Coast-down tests that implement the improvements brought about by these recommended tasks are planned for follow-on phases of this study.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".