Calibration strategy of diesel-fuel spray atomization models using a design of experiment method
Bibliographic record
Abstract
The Reitz and Diwakar and the KHRT atomization models are widely used for high-pressure diesel-fuel spray. The constants in both models must be calibrated to correctly predict the injection process based on the nozzle geometry, injection conditions, and fuel. Calibration can be significantly time-consuming given the four constants in both models. This paper suggests a strategy to assess the impact of models’ constants on spray tip penetration and mean droplet-diameter predictions on a reference case, with an injection pressure of 700 bar, to characterize the influence of the atomization model’s calibration. The assessment used a design of experiment method (DOE), which demonstrated the important interaction between constants on the results. Obtained calibrations were used for comparing the models’ performances qualitatively and quantitatively by accounting for spray and air-entrainment characteristics. Both models gave similar results, but the KHRT model yielded a better spray shape. Finally, based on DOE results, a method is proposed to modify the model’s constants for higher pressures (900 and 1300 bar).
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".