Investigation of completeness of combustion in CNG fueled spark ignition engines.
Bibliographic record
Abstract
The focus of this study was to apply and evaluate two separate techniques for estimating of the completeness of combustion in a multi-cylinder engine fueled with compressed natural gas. The degree of correlation between such two independent estimates of the combustion completeness was sought. One technique, with better time resolution and viewed to be more accurate, calculates the completeness of combustion on a cycle-by-cycle basis using in-cylinder pressure measurements. The technique utilizes the normalized pressure rise parameter due to combustion to describe the completeness of combustion. The second technique evaluates the completeness of combustion based on time-averaged measurements of unburned hydrocarbons in engine exhaust gases. Both the in-cylinder pressure and exhaust gas composition data were obtained from a test multi-cylinder engine fuelled with compressed natural gas (CNG). The crank angle resolved pressure data were analyzed with combustion analysis software. The establishment of the correlation between the two estimates of the completeness of combustion could allow dispensing with elaborate in-cylinder pressure measurements. The successful aspect of this investigation was to propose and test a new parameter that is strongly related to completeness of combustion. The advantage of the new parameter is that its calculation does not require computations of the pressure rise due to combustion and avoids difficulties associated with that procedure in finding such parameters as the polytropic exponents, the start and end of combustion, etc.Dept. of Mechanical, Automotive, and Materials Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2002 .A94. Source: Masters Abstracts International, Volume: 44-01, page: 0426. Thesis (M.A.Sc.)--University of Windsor (Canada), 2003.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 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".