Evaluation of Tailpipe Emissions and Cold Start Performance of E85 Vehicles from the 2000 Ethanol Challenge
Bibliographic record
Abstract
The transportation industry has been investigating ethanol as an alternative fuel for many years. Ethanol provides a clean alternative to gasoline, though widespread use of this fuel has been limited due to a number of technical challenges, such as poor cold-start performance and reduced vehicle range, and economic competitiveness with gasoline. To address the technical issues surrounding the use of ethanol, a group of university engineering teams were selected to participate in the Ethanol Vehicle Challenge by converting 1999 Chevrolet Silverado trucks to dedicated E85 operation (85% ethanol and 15% gasoline). The goals of the Ethanol Vehicle Challenge concentrated on significantly lowering emissions and improving the cold-start performance, fuel efficiency and overall vehicle performance of the trucks. This report examines the emissions and cold-start performance results from the competition and the approaches taken by the teams to address these issues. In May 2000, the Challenge vehicles underwent emissions testing at Environment Canada's Environmental Technology Centre in Ottawa, Ontario, Canada. The vehicles were placed on chassis dynamometers and tested according to the Federal Test Procedure 75. Exhaust emissions were collected and analyzed for nitrogen oxides, carbon monoxide, carbon dioxide, ethanol, aldehydes, and non-methane hydrocarbons. Teams incorporated novel approaches to changing the fuel delivery system to improve the vehicle's power, fuel economy, and emissions. The technologies that demonstrated an impact on emission reductions were secondary air injection, phase change material insulated catalytic converters, PCM adjustments, and EGR. These developments led one team to achieve the California Air Resources Board Ultra Low Vehicle Emissions level for light-duty vehicles and another team to demonstrate improved cold starting and better driveability compared to the stock vehicle operating on gasoline.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".