Numerical-Experimental Comparison of the Performance of a Partially Stratified Charge Natural Gas Fuelled Engine
Bibliographic record
Abstract
Compressed natural gas (CNG) has great potential as an alternative fuel for vehicle engines, and can reduce emissions and improve fuel economy. A single cylinder research engine has been modified to enable direct injection of a small quantity of fuel near the spark plug, independently of an overall lean homogeneous charge. Thus a partially stratified charge is formed within the chamber, which allows significant extension of the lean limit of combustion. This results in an improvement in specific fuel consumption. Numerical simulation also plays an important role in the development of such technological solutions. 3D simulations, in particular, are desirable to provide complete information about thermal and fluid dynamical fields within the chamber. In particular, among the developed numerical tools linked to the KIVA-3V code, special attention was dedicated to the formulation of the combustion model (CFM) turbulent combustion model based on the flamelet hypothesis), to adequately model non-homogeneities and lean mixture compositions. In this paper an optimization procedure is assessed, with the ultimate goal of designing combustion chambers properly devoted to be operated under lean (homogeneous and PSC) mixture conditions. The results related to the procedure definition and to its experimental validation are presented. Experimental and numerical data have been compared in terms of pressure cycles and heat release rate profiles. The overall results are encouraging, taking into special account the difficulty to reliably predict the key performance parameters without any “tuning interventions”, even when mixture richness and homogeneity were varied.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".