Development and Validation of an N-Dodecane Skeletal Mechanism Using a Hybrid Reduction Method in a Jet Stirred Reactor
Bibliographic record
Abstract
Diesel and Kerosene fuels are widely used in transportation, including aviation.However, their complex chemistry and high carbon numbers provide many challenges in simulating real-world conditions.Therefore, researchers are considering surrogates that can help to understand the combustion behaviour of different hydrocarbon fuels.n-dodecane is one of the important surrogates for kerosene and diesel due to its same physical properties.The fact that the chemical kinetic in flames is not well noticed.This study proposes a skeletal mechanism for n-dodecane, further used to investigate the ten species (O2, CO, CO2, H2, H2O, CH4, C2H2, C2H4, C6H6, n-C12H26) in the jet-stirred reactor.For the first time, the n-dodecane mechanism is reduced using the hybrid reduction method (path flux analysis + artificial neural network).The detailed mechanism [1] of n-dodecane is reduced to 94 species, and 516 reactions from 255 species and 1521 reactions [1] by using hybrid reduction method.The newly reduced mechanism maintained the accuracy of the detailed mechanism in the different reactors (ignition delay time, flame speed, and jet stirred reactor).In the future, the reduced mechanism will incorporate into a 2-D co-flow reactor to analyze the insight information of PAH and soot formation in ndodecane flames.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".