Bibliographic record
Abstract
A Computational Fluid Dynamics (CFD) model was built that simulates the transient, compressible, reacting, multi-phase environment that exists within a reciprocating engine's combustion chamber(s). ANSYS Fluent v13.0 was used with the Euler-Lagrangian Discrete Phase Model (DPM), the Shell autoignition model, and the Large Eddy Simulation (LES) method of turbulence modeling. Validation of the spray dynamics was performed by comparing simulation results with experiments of liquid and vapour penetration length of an n-Heptane spray experiment done by Sandia National Laboratories. It was found that LES produced more accurate results than several Reynolds Averaged Navier-Stokes (RANS) models. The Shell autoignition model was coded to function with C10.17H19.91 and compared with experimental ignition results in a Rapid Compression Machine (RCM) environment. All of the above models were then combined to simulate a directly-fueled lean-burn combustion prechamber configuration wherein the effects of spray angle, timing, and duration were studied.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".