Parameterized Diesel Engine Combustion Modeling for Torque Based Combustion Property Estimation
Bibliographic record
Abstract
Combustion model structures based on Vibe functions are outlined and investigated in this work. The focus of the study is the use of such model structures for estimation of diesel combustion properties by reconstructing in-cylinder pressure from measurements of crankshaft torque. Investigated combustion properties include the start and phasing of the combustion as well as maximum values of the in-cylinder pressure and its derivative. The accuracy associated with the proposed estimation method is evaluated using ideal torque data, i.e. torque calculated from in-cylinder pressure, that is generated using both simulations and experiments. The results indicate that the uncertainty associated with the estimation of a selected combustion property tends to increase if that property is located close to TDC, where the signal-to-noise ratio is low for a torque signal. The results also show that the ideal torque based estimation of combustion phasing and maximum in-cylinder pressure generally is very accurate while the estimation of start of combustion and the maximum in-cylinder pressure derivative suffers more from disturbances and model imperfections.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".