<scp>ReaxFF MD</scp> investigation of different <scp>JP8</scp> surrogates on combustion mechanism and reaction kinetics
Bibliographic record
Abstract
Abstract This work aims to investigate the combustion characteristics difference between JP8‐1 and JP8‐2, and to reveal the combustion mechanism of JP8 surrogate fuels by reactive molecular dynamic simulations (ReaxFF). The JP8‐1 fuel shows higher reaction activity, and the JP8‐2 surrogate conversion is faster than that of the JP8‐1 model. The maximal mass fraction difference for two surrogate fuels consumption can be up to 12% in the pyrolysis process. The differences of reaction pathways, oxygen‐containing intermediates, and key products between two surrogate fuels have been analyzed using the VARxMD software. The evolution trends of the vital products and key intermediates of the two fuels are similar owing to the similar fuel structures of linear paraffin components. The branched component isooctane in JP8‐1 model can enhance the formation of cyclic chain hydrocarbons products, while branched paraffin component decalin in JP8‐2 model promote the formation of structurally stable cycloalkanes. Compared to JP8‐2 fuel, the formation of CH 2 O and coke precursors, the JP8‐1 fuel generate more harmful substances. This work can provide an important theoretical basis for the rational design and screening of high‐performance surrogate components and additives for complex multi‐component jet fuels. And also suggests that the ReaxFF simulation method have the potential as an effective approach for evaluating combustion behaviour in fuel combustion.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".