Advances in Turbocharging Technology and its Impact on Meeting Proposed California GHG Emission Regulations
Bibliographic record
Abstract
The State of California considers greenhouse gases (GHGs) to be air pollutants and has directed the Air Resources Board to adopt cost effective regulations for GHG emissions from motor vehicles. The northeastern states and Canada through NESCCAF have worked closely with CARB and CO2 equivalent emission regulations have been proposed. The eventual status of these regulations may not be clear, but what is clear is that there is a need to develop cost effective technology to reduce GHG emissions. This paper presents such technology. Advances in turbocharging technology relevant to both gasoline and diesel engines are described. Turbocharging, as a technology has been around for 70 years, but just like the internal combustion engine itself, it is far from being mature. Conventional evolutionary development of turbocharging such as inertia reduction, aerodynamics and bearing improvements have been ongoing. More recent step improvements due to variable geometry technology both on the turbine and compressor side are now available. Work is under way on electrically assisted turbocharging as well as new concepts to meet the demands of exhaust gas recirculation. It is shown that turbocharging, applied to different classes of vehicles with appropriate choices of gasoline and diesel engines, provides a cost effective way of reducing GHG emissions well within the goals being considered by the California Air Resources Board.
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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 | 0.002 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".