The First and Second Law Analysis of Spark Ignition Engine Fuelled with Compressed Natural Gas
Bibliographic record
Abstract
This paper presents a fundamental thermodynamic modeling approach to study internal combustion engines. The computations of the thermodynamic functions, especially availability, have been developed to seek better energy utilization, analyze engine performance and optimize design of spark ignition (SI) engines fueled with compressed natural gas (CNG), by using both the first and the second law analyses. A single-zone heat release model with constant thermodynamic properties is built into the air cycle simulation, while a more comprehensive two-zone combustion model with burning rate as a sinusoidal function of crank angle is built into the fuel/air thermodynamic engine cycle simulation. The computations mainly include pressure, unburned and burned zone temperature, indicated work, heat loss, mass blowby, availability destruction due to combustion, fuel chemical availability, availability transfer with heat, availability transfer with work and availability exhaust to the environment. The validation of the simulation results with the experimental data is performed for a DaimlerChrysler 4.7 Liter CNG fueled V8 engine at wide open throttle (WOT), and 4000 rpm.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".