Effects of Fuel Composition Variations (H<sub>2</sub>:CO) for Biomass Gas HCCI Combustion
Bibliographic record
Abstract
Research regarding higher efficiency engines and renewable energy has lead to HCCI engine technology as a viable option with the ability to utilize a variety of fuels. With a larger focus on environmental effects the ability of HCCI engines to produce low levels of NOx and potentially other combustion products is another attractive feature of the technology. Biomass gas as a renewable primary fuel is becoming more predominant regarding internal combustion engine research. The simulated fuel in this study replicates compositions derived from real-world gasification processes; the focus in this work corresponds to fuel composition variations and their effects regarding combustion phasing and performance. There are three biomass gas fuel compositions investigated in this study. All compositions consisted of combustibles of CH₄, CO, and H₂ accompanied by CO₂ then balanced with N₂. The CH₄ and CO₂ constituents of each fuel mixture are held constant at 2% and 5% respectively. The H₂ content varies from 10% to 20% and the CO ranges from 15% to 25%; the H₂:CO ratio varies between 0.40 and 1.33. All compositions are balanced with N₂. The engine speed was held constant at 1500 rpm with variations in both equivalence ratio and intake temperature. All results are derived from in-cylinder cycle-resolved pressure measurements. Combustion phasing was quantified through CA10 or SOC and CA10-90 (crank angle corresponding to 10% of the heat release and 10-90% of heat release). Analysis of IMEP and indicated efficiency will be reported on to quantify the useful work produced.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".